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The process of parameter estimation targeting a chosen set of observations is an essential aspect of numerical modeling. This process is usually named tuning in the climate modeling community. In climate models, the variety and complexity of physical processes involved, and their interplay through a wide range of spatial and temporal scales, must be summarized in a series of approximate submodels. Most submodels depend on uncertain parameters. Tuning consists of adjusting the values of these parameters to bring the solution as a whole into line with aspects of the observed climate. Tuning is an essential aspect of climate modeling with its own scientific issues, which is probably not advertised enough outside the community of model developers. Optimization of climate models raises important questions about whether tuning methods a priori constrain the model results in unintended ways that would affect our confidence in climate projections. Here, we present the definition and rationale behind model tuning, review specific methodological aspects, and survey the diversity of tuning approaches used in current climate models. We also discuss the challenges and opportunities in applying so-called objective methods in climate model tuning. We discuss how tuning methodologies may affect fundamental results of climate models, such as climate sensitivity. The article concludes with a series of recommendations to make the process of climate model tuning more transparent.

The phase diagram of high-pressure hydrogen is of great interest for fundamental research, planetary physics, and energy applications. A first-order phase transition in the fluid phase between a molecular insulating fluid and a monoatomic metallic fluid has been predicted. The existence and precise location of the transition line is relevant for planetary models. Recent experiments reported contrasting results about the location of the transition. Theoretical results based on density functional theory are also very scattered. We report highly accurate coupled electron–ion Monte Carlo calculations of this transition, finding results that lie between the two experimental predictions, close to that measured in diamond anvil cell experiments but at 25–30 GPa higher pressure. The transition along an isotherm is signaled by a discontinuity in the specific volume, a sudden dissociation of the molecules, a jump in electrical conductivity, and loss of electron localization.

Selective laser melting (SLM) is an additive manufacturing process in which multiple, successive layers of metal powders are heated via laser in order to build a part. Modeling of SLM requires consideration of both heat transfer and solid mechanics. The present work describes continuum modeling of SLM as envisioned for eventual support of part-scale modeling of this fabrication process to determine end-state information such as residual stresses and distortion. The determination of the evolving temperatures is dependent on the material, the state of the material (powder or solid), the specified heating, and the configuration. Similarly, the current configuration is dependent on the temperatures, the powder-solid state, and the constitutive models. A multi-physics numerical formulation is required to solve such problems. This article describes the problem formulation, numerical method, and constitutive parameters necessary to solve such a problem. Additionally, various verification and example problems are simulated in the parallel, multi-physics finite element code Diablo, and the results presented herein.

The numerical approximation of the Euler equations of gas dynamics in a movingLagrangian frame is at the heart of many multiphysics simulation algorithms. In this paper, we present a general framework for high-order Lagrangian discretization of these compressible shock hydrodynamics equations using curvilinear finite elements. This method is an extension of the approach outlined in [Dobrev et al., Internat. J. Numer. Methods Fluids, 65 (2010), pp. 1295--1310] and can be formulated for any finite dimensional approximation of the kinematic and thermodynamic fields, including generic finite elements on two- and three-dimensional meshes with triangular, quadrilateral, tetrahedral, or hexahedral zones. We discretize the kinematic variables of position and velocity using a continuous high-order basis function expansion of arbitrary polynomial degree which is obtained via a corresponding high-order parametric mapping from a standard reference element. This enables the use of curvilinear zone geometry, higher-order approximations for fields within a zone, and a pointwise definition of mass conservation which we refer to as strong mass conservation. We discretize the internal energy using a piecewise discontinuous high-order basis function expansion which is also of arbitrary polynomial degree. This facilitates multimaterial hydrodynamics by treating material properties, such as equations of state and constitutive models, as piecewise discontinuous functions which vary within a zone. To satisfy the Rankine--Hugoniot jump conditions at a shock boundary and generate the appropriate entropy, we introduce a general tensor artificial viscosity which takes advantage of the high-order kinematic and thermodynamic information available in each zone. Finally, we apply a generic high-order time discretization process to the semidiscrete equations to develop the fully discrete numerical algorithm. Our method can be viewed as the high-order generalization of the so-called staggered-grid hydrodynamics (SGH) approach and we show that under specific low-order assumptions, we exactly recover the classical SGH method. We present numerical results from an extensive series of verification tests that demonstrate several important practical advantages of using high-order finite elements in this context. [PUBLICATION ABSTRACT]

We propose data profiles as a tool for analyzing the performance of derivative-free optimization solvers when there are constraints on the computational budget. We use performance and data profiles, together with a convergence test that measures the decrease in function value, to analyze the performance of three solvers on sets of smooth, noisy, and piecewise-smooth problems. Our results provide estimates for the performance difference between these solvers, and show that on these problems, the model-based solver tested performs better than the two direct search solvers tested. [PUBLICATION ABSTRACT]

Mutations in the enzyme cytosolic isocitrate dehydrogenase 1 (IDH1) are a common feature of a major subset of primary human brain cancers. These mutations occur at a single amino acid residue of the IDH1 active site, resulting in loss of the enzyme’s ability to catalyse conversion of isocitrate to α-ketoglutarate. However, only a single copy of the gene is mutated in tumours, raising the possibility that the mutations do not result in a simple loss of function. Here we show that cancer-associated IDH1 mutations result in a new ability of the enzyme to catalyse the NADPH-dependent reduction of α-ketoglutarate to R (-)-2-hydroxyglutarate (2HG). Structural studies demonstrate that when arginine 132 is mutated to histidine, residues in the active site are shifted to produce structural changes consistent with reduced oxidative decarboxylation of isocitrate and acquisition of the ability to convert α-ketoglutarate to 2HG. Excess accumulation of 2HG has been shown to lead to an elevated risk of malignant brain tumours in patients with inborn errors of 2HG metabolism. Similarly, in human malignant gliomas harbouring IDH1 mutations, we find markedly elevated levels of 2HG. These data demonstrate that the IDH1 mutations result in production of the onco-metabolite 2HG, and indicate that the excess 2HG which accumulates in vivo contributes to the formation and malignant progression of gliomas. Role of 2-hydroxyglutarate in cancer A high percentage of human glioblastomas has been found to harbour mutations in the metabolic enzyme cytosolic isocitrate dehydrogenase 1 (IDH1). The predominant R132H mutation is now shown to act as a gain-of-function mutation, enabling IDH1 to convert α-ketoglutarate to 2-hydroxyglutarate (2-HG). Human glioblastoma samples with IDH1 mutations indeed contain elevated levels of 2-HG. Future work will be directed at understanding the mechanisms by which 2-HG can contribute to tumorigenesis. Mutations in the enzyme cytosolic isocitrate dehydrogenase 1 (IDH1) are commonly found in glioblastomas, a major subset of primary human brain cancers. However, only a single copy of the gene is mutated, suggesting that the mutation does not result in a simple loss of function. Here, IDH1 mutations are shown to act in a gain-of-function manner, resulting in a new ability of the enzyme to catalyse α-ketoglutarate to R (-)-2-hydroxyglutarate, an onco-metabolite.

One of the many definitions of \"cloud\" is that of an infrastructure-as-a-service (IaaS) system, in which IT infrastructure is deployed in a provider's data center as virtual machines. With IaaS clouds' growing popularity, tools and technologies are emerging that can transform an organization's existing infrastructure into a private or hybrid cloud. OpenNebula is an open source, virtual infrastructure manager that deploys virtualized services on both a local pool of resources and external IaaS clouds. Haizea, a resource lease manager, can act as a scheduling back end for OpenNebula, providing features not found in other cloud software or virtualization-based data center management software.

We present a numerical algorithm to implement entropy-based ($M_N$) moment models in the context of a simple, linear kinetic equation for particles moving through a material slab. The closure for these models---as is the case for all entropy-based models---is derived through the solution of a constrained, convex optimization problem. The algorithm has two components. The first component is a discretization of the moment equations which preserves the set of realizable moments, thereby ensuring that the optimization problem has a solution (in exact arithmetic). The discretization is a second-order kinetic scheme which uses MUSCL-type limiting in space and a strong-stability-preserving, Runge--Kutta time integrator. The second component of the algorithm is a Newton-based solver for the dual optimization problem, which uses an adaptive quadrature to evaluate integrals in the dual objective and its derivatives. The accuracy of the numerical solution to the dual problem plays a key role in the time step restriction for the kinetic scheme. We study in detail the difficulties in the dual problem that arise near the boundary of realizable moments, where quadrature formulas are less reliable and the Hessian of the dual objective function is highly ill-conditioned. Extensive numerical experiments are performed to illustrate these difficulties. In cases where the dual problem becomes \"too difficult\" to solve numerically, we propose a regularization technique to artificially move moments away from the realizable boundary in a way that still preserves local particle concentrations. We present results of numerical simulations for two challenging test problems in order to quantify the characteristics of the optimization solver and to investigate when and how frequently the regularization is needed. [PUBLICATION ABSTRACT]

Argonaute proteins form the functional core of the RNA-induced silencing complexes that mediate RNA silencing in eukaryotes. The 2.3 angstrom resolution crystal structure of human Argonaute2 (Ago2) reveals a bilobed molecule with a central cleft for binding guide and target RNAs. Nucleotides 2 to 6 of a heterogeneous mixture of guide RNAs are positioned in an A-form conformation for base pairing with target messenger RNAs. Between nucleotides 6 and 7, there is a kink that may function in microRNA target recognition or release of sliced RNA products. Tandem tryptophan-binding pockets in the PIWI domain define a likely interaction surface for recruitment of glycine-tryptophan-182 (GW182) or other tryptophan-rich cofactors. These results will enable structure-based approaches for harnessing the untapped therapeutic potential of RNA silencing in humans.

Reconstructing a metabolic model from the genome sequence of an organism is a useful but arduous approach for predicting phenotypes. Henry et al . describe a resource that automates most of this process and apply it to create >100 new metabolic models of microbes. Genome-scale metabolic models have proven to be valuable for predicting organism phenotypes from genotypes. Yet efforts to develop new models are failing to keep pace with genome sequencing. To address this problem, we introduce the Model SEED, a web-based resource for high-throughput generation, optimization and analysis of genome-scale metabolic models. The Model SEED integrates existing methods and introduces techniques to automate nearly every step of this process, taking ∼48 h to reconstruct a metabolic model from an assembled genome sequence. We apply this resource to generate 130 genome-scale metabolic models representing a taxonomically diverse set of bacteria. Twenty-two of the models were validated against available gene essentiality and Biolog data, with the average model accuracy determined to be 66% before optimization and 87% after optimization.