In vitro efficacy of tavaborole topical solution, 5% after penetration through nail polish on ex vivo human fingernails

This document is the Accepted Manuscript of the following article: Aditya K. Gupta, et al, 'In vitro efficacy of tavaborole topical solution, 5% after penetration through nail polish on ex vivo human fingernails', Journal of Dermatological Treatment, Jan 2018. Under embargo until 10 January 2019. The final, published version is available online at doi: https://doi.org/10.1080/09546634.2017.1422078.Background: Topical antifungal treatments for onychomycosis are applied to clean, unpolished nails for 48 weeks or longer. Patients often wish to mask their infection with nail polish yet there is no evidence to suggest antifungal efficacy in the presence of nail polish. Objective: To determine if tavaborole retains the ability to penetrate the nail plate and inhibit fungal growth in the presence of nail polish. Method: Tavaborole was applied to human fingernails painted with 2 or 4 coats of nail polish, and unpainted nails in an ex vivo model. Nails were mounted on TurChub ® chambers seeded with Trichophyton rubrum and allowed to incubate for 7 days. Antifungal activity was assessed by measuring zones of inhibition. Results and conclusion: Tavaborole exhibited antifungal activity in all experimental groups. The zones of inhibition of T. rubrum for all experimental groups (2 or 4 coats of polish, unpolished) were greater than infected controls (polished and unpolished), p s <.001. Tavaborole penetrates polished nails and kills T. rubrum in this ex vivo model.Peer reviewe

Atomic States Entanglement in Carbon Nanotubes

The entanglement of two atoms (ions) doped into a carbon nanotube has been investigated theoretically. Based on the photon Green function formalism for quantizing electromagnetic field in the presence of carbon nanotubes, small-diameter metallic nanotubes are shown to result in a high degree of the two-qubit atomic entanglement for long times due to the strong atom-field coupling.Comment: 4 pages, 2 figure

Supervised learning applied to high-dimensional millimeter wave transient absorption data for age prediction of perovskite thin-film

We have analyzed a limited sample set of 120 GHz, and 150 GHz time-resolved millimeter wave (mmW) photoconductive decay (mmPCD) signals of 300 nm thick air-stable encapsulated perovskite film (methyl-ammonium lead halide) excited using a pulsed 532-nm laser with fluence 10.6 micro-Joules per cm-2. We correlated 12 parameters derived directly from acquired mmPCD kinetic-trace data and its step-response, each with the sample-age based on the date of the experiment. Five parameters with a high negative correlation with sample age were finally selected as predictors in the Gaussian Process Regression (GPR) machine learning model for prediction of the age of the sample. The effects of aging (between 0 and 40,000 hours after film production) are quantified mainly in terms of a shift in peak voltage, the response ratio (conductance parameter), loss-compensated transmission coefficient, and the radiofrequency (RF) area of the transient itself (flux). Changes in the other step-response parameters and the decay length of the aging transients are also shown. The GPR model is found to work well for a forward prediction of the age of the sample using this method. It is noted that the Matern-5 over 2 GPR kernel for supervised learning provides the best realistic solution for age prediction with R squared around 0.97

Cumulative structure function in terms of nucleonic wave function of the nucleus

The structure function of the nucleus in the cumulative region $x>1$ is studied in terms of nucleon degrees of freedom. At high $Q^2$ the resulting expressions are presented as a sum of contributions from few-nucleon correlations. Two-nucleon correlations are studied in some detail. Spin variables are averaged out. In the region $1<x<2$ the structure functions are calculated for the relativistic interaction proposed by F.Gross {\it et al}. They are found to fall with $x$ faster than the exponential. For Carbon at $x=1.05$, where the method is not rigorously applicable, they turn out to be rougly twice larger than the experimental data.Comment: text and 2 figures in LaTex, 7 figures in P