Kinetics and Products of the Acid-Catalyzed Ring-Opening of Atmospherically Relevant Butyl Epoxy Alcohols

Epoxydiols are produced in the gas phase from the photo-oxidation of isoprene in the absence of significant mixing ratios of nitrogen oxides (NO_x). The reactive uptake of these compounds onto acidic aerosols has been shown to produce secondary organic aerosol (SOA). To better characterize the fate of isoprene epoxydiols in the aerosol phase, the kinetics and products of the acid-catalyzed ring-opening reactions of four hydroxy-substituted epoxides were studied by nuclear magnetic resonance (NMR) techniques. Polyols and sulfate esters are observed from the ring-opening of the epoxides in solutions of H_2SO_4/Na_2SO_4. Likewise, polyols and nitrate esters are produced in solutions of HNO_3/NaNO_3. In sulfuric acid, the rate of acid-catalyzed ring-opening is dependent on hydronium ion activity, sulfate ion, and bisulfate. The rates are much slower than the nonhydroxylated equivalent epoxides; however, the hydroxyl groups make them much more water-soluble. A model was constructed with the major channels for epoxydiol loss (i.e., aerosol-phase ring-opening, gas-phase oxidation, and deposition). In the atmosphere, SOA formation from epoxydiols will depend on a number of variables (e.g., pH and aerosol water content) with the yield of ring-opening products varying from less than 1% to greater than 50%

Quantum Hamiltonian Learning Using Imperfect Quantum Resources

Identifying an accurate model for the dynamics of a quantum system is a vexing problem that underlies a range of problems in experimental physics and quantum information theory. Recently, a method called quantum Hamiltonian learning has been proposed by the present authors that uses quantum simulation as a resource for modeling an unknown quantum system. This approach can, under certain circumstances, allow such models to be efficiently identified. A major caveat of that work is the assumption of that all elements of the protocol are noise-free. Here, we show that quantum Hamiltonian learning can tolerate substantial amounts of depolarizing noise and show numerical evidence that it can tolerate noise drawn from other realistic models. We further provide evidence that the learning algorithm will find a model that is maximally close to the true model in cases where the hypothetical model lacks terms present in the true model. Finally, we also provide numerical evidence that the algorithm works for non-commuting models. This work illustrates that quantum Hamiltonian learning can be performed using realistic resources and suggests that even imperfect quantum resources may be valuable for characterizing quantum systems.Comment: 16 pages 11 Figure

Determinants of Mortality and Disease Progression of Kaposi Sarcoma in a Primary care ART Programme in Khayelitsha, South Africa

CROI 200

Atomic radius and charge parameter uncertainty in biomolecular solvation energy calculations

Atomic radii and charges are two major parameters used in implicit solvent electrostatics and energy calculations. The optimization problem for charges and radii is under-determined, leading to uncertainty in the values of these parameters and in the results of solvation energy calculations using these parameters. This paper presents a new method for quantifying this uncertainty in implicit solvation calculations of small molecules using surrogate models based on generalized polynomial chaos (gPC) expansions. There are relatively few atom types used to specify radii parameters in implicit solvation calculations; therefore, surrogate models for these low-dimensional spaces could be constructed using least-squares fitting. However, there are many more types of atomic charges; therefore, construction of surrogate models for the charge parameter space requires compressed sensing combined with an iterative rotation method to enhance problem sparsity. We demonstrate the application of the method by presenting results for the uncertainties in small molecule solvation energies based on these approaches. The method presented in this paper is a promising approach for efficiently quantifying uncertainty in a wide range of force field parameterization problems, including those beyond continuum solvation calculations.The intent of this study is to provide a way for developers of implicit solvent model parameter sets to understand the sensitivity of their target properties (solvation energy) on underlying choices for solute radius and charge parameters

Musings on genome medicine: cholesterol and coronary artery disease

Cholesterol levels and not inflammatory markers are the major variables that pose a risk of coronary artery disease. Diabetes greatly increases the risk at any cholesterol level. Coronary artery disease and cancer are linked by a common protein - an apoptotic protein that also functions as a regulator of insulin secretion

Musings on genome medicine: enzyme-replacement therapy of the lysosomal storage diseases

The lysosomal storage diseases, such as Gaucher's disease, mucopolysaccharidosis I, II and IV, Fabry's disease, and Pompe's disease, are rare inherited disorders whose symptoms result from enzyme deficiency causing lysosomal accumulation. Until effective gene-replacement therapy is developed, expensive, and at best incomplete, enzyme-replacement therapy is the only hope for sufferers of rare lysosomal storage diseases. Preventive strategies involving carrier detection should be a priority toward the successful management of these conditions