MEASURING THE BENEFITS OF AN IN-STORE CONSUMER INFORMATION PROGRAM

Consumer/Household Economics,

Oxidative Protection Using Pluronic Micelles

Methods for drug delivery are currently being researched in many different applications. One significant obstacle to drug delivery is a drug\u27s inherent hydrophobic character. Drugs that are hydrophobic are difficult to successfully administer since the body consists mostly of water. Therefore, a greater variety of drugs could potentially be used for the treatment of disease if they could successfully be administered. Recent research in oncology and virology has attempted to overcome the obstacles posed by hydrophobic drugs by encapsulating them in micelles. Micelles form spontaneously from compounds that contain both hydrophilic and hydrophobic regions and can be used to increase the solubility of hydrophobic drugs in water. Commonly used compounds that form micelles are Pluronic copolymers, which have a hydrophilic block that consists of polyethylene oxide (PEO) subunits and two hydrophobic blocks that consist of polypropylene oxide (PPO) subunits. Different types of Pluronic such as Pluronic F68 and F127 each have a triblock structure made of the same PEO and PPO subunits but differ in the number of these subunit repeats. This difference affects both size and hydrophilic lipophilic ratio, which is described as hydrophilic lipophilic balance (HLB). It was hypothesized that micelles offer encapsulated drugs protection from oxidation and that polymer properties such as HLB influence the degree of protection. Results from this experiment indicate that Pluronic can be used to form micelles that offer a degree of oxidative protection to encapsulated quercetin. Results also showed that the Pluronic polymers F68 and F127 may offer different levels of oxidative protection as well. The data suggests that Pluronic F127 may offer greater protection than Pluronic F68 which is thought to be due to differences in the inherent properties of each copolymer. Among these properties, HLB is thought to be a significant influencing factor

Passive scalar mixing downstream of a synthetic jet in crossflow

An experimental investigation on passive scalar mixing due to the interaction of a synthetic jet with a thermal boundary layer is presented. From velocity measurements, performed by particle image velocimetry, two jet behaviours were identified. For jet to crossflow velocity ratios less than 1.2, the velocity fluctuations due to the jet/crossflow interaction stayed close to the wall. At higher ratios, the fluctuations moved away from the wall. The thermal mixing was examined using laser induced fluorescence. During expulsion, the thickness of the downstream thermal boundary layer increased whilst the thermal boundary layer was annihilated immediately downstream of the jet during entrainment

Verifying the fully “Laplacianised” posterior Naïve Bayesian approach and more

Mussa and Glen would like to thank Unilever for financial support, whereas Mussa and Mitchell thank the BBSRC for funding this research through grant BB/I00596X/1. Mitchell thanks the Scottish Universities Life Sciences Alliance (SULSA) for financial support.Background In a recent paper, Mussa, Mitchell and Glen (MMG) have mathematically demonstrated that the “Laplacian Corrected Modified Naïve Bayes” (LCMNB) algorithm can be viewed as a variant of the so-called Standard Naïve Bayes (SNB) scheme, whereby the role played by absence of compound features in classifying/assigning the compound to its appropriate class is ignored. MMG have also proffered guidelines regarding the conditions under which this omission may hold. Utilising three data sets, the present paper examines the validity of these guidelines in practice. The paper also extends MMG’s work and introduces a new version of the SNB classifier: “Tapered Naïve Bayes” (TNB). TNB does not discard the role of absence of a feature out of hand, nor does it fully consider its role. Hence, TNB encapsulates both SNB and LCMNB. Results LCMNB, SNB and TNB performed differently on classifying 4,658, 5,031 and 1,149 ligands (all chosen from the ChEMBL Database) distributed over 31 enzymes, 23 membrane receptors, and one ion-channel, four transporters and one transcription factor as their target proteins. When the number of features utilised was equal to or smaller than the “optimal” number of features for a given data set, SNB classifiers systematically gave better classification results than those yielded by LCMNB classifiers. The opposite was true when the number of features employed was markedly larger than the “optimal” number of features for this data set. Nonetheless, these LCMNB performances were worse than the classification performance achieved by SNB when the “optimal” number of features for the data set was utilised. TNB classifiers systematically outperformed both SNB and LCMNB classifiers. Conclusions The classification results obtained in this study concur with the mathematical based guidelines given in MMG’s paper—that is, ignoring the role of absence of a feature out of hand does not necessarily improve classification performance of the SNB approach; if anything, it could make the performance of the SNB method worse. The results obtained also lend support to the rationale, on which the TNB algorithm rests: handled judiciously, taking into account absence of features can enhance (not impair) the discriminatory classification power of the SNB approach.Publisher PDFPeer reviewe

Christmas Tree Purchasing Habits of Greater Columbus, Ohio, Consumers -- 1956

Exact date of working paper unknown