Heavy-flavor observables at RHIC and LHC

We investigate the charm-quark propagation in the QGP media produced in ultrarelativistic heavy-ion collisions at RHIC and the LHC. Purely collisional and radiative processes lead to a significant suppression of final $D$-meson spectra at high transverse momentum and a finite flow of heavy quarks inside the fluid dynamical evolution of the light partons. The $D$-meson nuclear modification factor and the elliptic flow are studied at two collision energies. We further propose to measure the triangular flow of $D$ mesons, which we find to be nonzero in non-central collisions.Comment: Proceedings of the 24th Quark Matter conference, 19-24 May 2014, Darmstadt, Germany. 4 p

Physically consistent simulation of transport of inertial particles in porous media

A new numerical approach is presented for simulating the movement of test particles suspended in an incompressible fluid flowing through a porous matrix. This two-phase particle-laden flow is based on the Navier-Stokes equations for incompressible fluid flow and equations of motion for the individual particles in which Stokes drag is dominant. The Immersed Boundary method is applied to incorporate the geometric complexity of the porous medium. A symmetry-preserving finite volume discretization method in combination with a volume penalization method resolves the flow within the porous material. The new Lagrangian particle tracking is such that for mass-less test particles no (numerical) collision with the coarsely represented porous medium occurs at any spatial resolution

Efficient parametric analysis of the chemical master equation through model order reduction

Background: Stochastic biochemical reaction networks are commonly modelled by the chemical master equation, and can be simulated as first order linear differential equations through a finite state projection. Due to the very high state space dimension of these equations, numerical simulations are computationally expensive. This is a particular problem for analysis tasks requiring repeated simulations for different parameter values. Such tasks are computationally expensive to the point of infeasibility with the chemical master equation. Results: In this article, we apply parametric model order reduction techniques in order to construct accurate low-dimensional parametric models of the chemical master equation. These surrogate models can be used in various parametric analysis task such as identifiability analysis, parameter estimation, or sensitivity analysis. As biological examples, we consider two models for gene regulation networks, a bistable switch and a network displaying stochastic oscillations. Conclusions: The results show that the parametric model reduction yields efficient models of stochastic biochemical reaction networks, and that these models can be useful for systems biology applications involving parametric analysis problems such as parameter exploration, optimization, estimation or sensitivity analysis.Comment: 23 pages, 8 figures, 2 table

Structural Investigation of the Substrate Specificity of Offloading and Sugar Methylation in Macrolide Biosynthesis.

The increase in antibiotic resistance over the past century emphasizes the need for new antibiotic therapies. Macrolide antibiotics are commonly used to treat infections in humans and animals. Many macrolides are produced by type I polyketide synthases in actinobacteria. This thesis investigates the structural basis of substrate specificity of macrolactonization and sugar-O-methylation, two key steps in macrolide biosynthesis. These results are presented with the aim of supporting future efforts to design and produce new compounds through biocatalytic and bioengineering routes. Methylation of sugar hydroxyl groups is common in biosynthetic pathways for macrolides and other natural products. Two distinct methyltransferases act sequentially in the late stages of mycinamicin biosynthesis to methylate the 2’ and 3’ hydroxyls of a 6-deoxyallose sugar. Structural and biochemical investigation of the 3’-O-methyltransferase, MycF, from the mycinamicin biosynthetic pathway provided insight into the mechanism and substrate selectivity of this family of methyltransferases. The series of structures presented herein illuminate the mechanisms that underlie sequential methylation by two families of sugar-O-methyltransferases common in natural product biosynthetic pathways. This foundation led to successful enzyme engineering which circumvented the natural order of the pathway to produce a new macrolide. Macrolactonization is another common feature of natural product biosynthesis. The structure of the thioesterase (TE) domain from the tylosin biosynthetic pathway is the first structure of a 16-membered macrolactone forming TE. Similar to the pikromycin TE, the Tyl TE has a hydrophillic barrier between the active site and the exterior of the protein, which is proposed to aid in promoting cyclization. A model of the product complex provides insights into the substrate specificity of the Tyl TE. Biochemical experiments demonstrated the utility of fluorophosphonate affinity labels in future structural characterization of TE domains.PHDChemical BiologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/110364/1/steffenb_2.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/110364/2/steffenb_1.pd

Compatibility Complex for Black Hole Spacetimes

The set of local gauge invariant quantities for linearized gravity on the Kerr spacetime presented by two of the authors (Aksteiner and B\ue4ckdahl in Phys Rev Lett 121:051104, 2018) is shown to be complete. In particular, any gauge invariant quantity for linearized gravity on Kerr that is local and of finite order in derivatives can be expressed in terms of these gauge invariants and derivatives thereof. The proof is carried out by constructing a complete compatibility complex for the Killing operator, and demonstrating the equivalence of the gauge invariants from Aksteiner and B\ue4ckdahl (Phys Rev Lett 121:051104, 2018) with the first compatibility operator from that complex

Simulation of impaction filtration of aerosol droplets in porous media

We report on the development of a method to simulate from first principles the particle filtration efficiency of filters that are composed of structured porous media. We assume that the ratio of particle density to the fluid density is high. We concentrate on the motion of the particles in a laminar flow and quantify the role of inertial effects on the filtration of an ensemble of particles. We adopt the Euler-Lagrange approach, distinguishing a flow field in which the motion of a large number of discrete particles is simulated. We associate filtration with the deterministic collision of inertial particles with solid elements of the structured porous medium. To underpin the physical `consistency' of deterministic particle filtration, we investigate to what extent the particle tracking algorithm ensures that mass-less test-particles will not be captured by the structured porous filter at all. This element of the algorithm is essential in order to distinguish physical filtration by inertial effects from unwanted numerical filtration, due to the finite spatial resolution of the gas flow. We consider filtration of particles whose motion is governed by Stokes drag and determine the filtration efficiency in a range of Stokes relaxation times. An exponential decay of the number of particles with time is observed

Automata learning algorithms and processes for providing more complete systems requirements specification by scenario generation, CSP-based syntax-oriented model construction, and R2D2C system requirements transformation

Systems, methods and apparatus are provided through which in some embodiments, automata learning algorithms and techniques are implemented to generate a more complete set of scenarios for requirements based programming. More specifically, a CSP-based, syntax-oriented model construction, which requires the support of a theorem prover, is complemented by model extrapolation, via automata learning. This may support the systematic completion of the requirements, the nature of the requirement being partial, which provides focus on the most prominent scenarios. This may generalize requirement skeletons by extrapolation and may indicate by way of automatically generated traces where the requirement specification is too loose and additional information is required