Enhancing Silymarin Fractionation via Molecular Modeling using the Conductor-like Screening Model for Real Solvents

The market for bio-based products from plant sources is on the rise. There is a global challenge to implement environmentally clean practices for the production of fuels and pharmaceuticals from sustainable resources. A significant hurdle for discovery of comparable plant-derived products is the extensive volume of trial-and-error experimentation required. To alleviate the experimental burden, a quantum mechanics based molecular modeling approach known as the COnductor-like Screening Model for Real Solvents (COSMO-RS) was used to predict the best biphasic solvent system to purify silymarins from an aqueous mixture. Silymarins are a class of flavonolignans present in milk thistle ( Silybum marianum L.), which has been used in traditional eastern medicine to treat liver disease. More recently, silymarins have been studied as a cancer treatment therapy due to their antioxidant properties, but effective large-scale separation methods need to be developed. Previous research has shown that these compounds can be fractionated using centrifugal partition chromatography (CPC), but not to an acceptable level of purity. Due to previous incomplete fractionation, the silymarins are ideal compounds to assess the use of a molecular modeling approach to predicting partitioning in a CPC separation. The COSMO-RS method was implemented using the software programs HyperChem, TmoleX, and COSMOthermX in order to calculate partition coefficients for the six silymarin compounds in various solvent systems. The partition coefficient for each silymarin in each solvent system was verified by experimentation using the shake flask method and compared to the results of the model

Egg on the Face, f in the Mouth, and the Overbite

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73161/1/aa.1986.88.3.02a00150.pd

FILM REVIEWS

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/71975/1/aa.1982.84.3.02a01050.pd

Assessing time knowledge in children aged 10 to 11 years

The acquisition of time knowledge involves learning how to read clocks, estimate time, read dates and learn about temporal sequences. Evidence suggests that many of these competencies are acquired by 10 years of age although not all children may follow this developmental path. The main purpose of this study was to collect normative data for a screening tool that assesses time knowledge. These data identify the prevalence and pattern of difficulties with time knowledge among a UK sample of Year 6 pupils (aged 10 to 11 years). The Time Screening Assessment tool (Doran, Dutt & Pembery, 2015), designed to assess time knowledge, was administered individually to a sample of 79 children. Findings revealed a median overall score of 32 out of a maximum score of 36. 25% of children performed at or close to ceiling, however seven children scored more than -1.5 standard deviations below the mean. The value of these findings to practitioners working with children in schools is discussed

Direct measurements of helium and hydrogen ion concentration and total ion density to an altitude of 940 kilometers

Measurement of ion concentration and total ion density in exosphere using mass spectrometer and electrostatic prob

A theoretical model of the ionosphere dynamics with interhemispheric coupling

Dynamic model for ionospheric plasma with interhemispheric couplin

Interest Rates and Information Geometry

The space of probability distributions on a given sample space possesses natural geometric properties. For example, in the case of a smooth parametric family of probability distributions on the real line, the parameter space has a Riemannian structure induced by the embedding of the family into the Hilbert space of square-integrable functions, and is characterised by the Fisher-Rao metric. In the nonparametric case the relevant geometry is determined by the spherical distance function of Bhattacharyya. In the context of term structure modelling, we show that minus the derivative of the discount function with respect to the maturity date gives rise to a probability density. This follows as a consequence of the positivity of interest rates. Therefore, by mapping the density functions associated with a given family of term structures to Hilbert space, the resulting metrical geometry can be used to analyse the relationship of yield curves to one another. We show that the general arbitrage-free yield curve dynamics can be represented as a process taking values in the convex space of smooth density functions on the positive real line. It follows that the theory of interest rate dynamics can be represented by a class of processes in Hilbert space. We also derive the dynamics for the central moments associated with the distribution determined by the yield curve.Comment: 20 pages, 3 figure