Anti-PTH monoclonal antibody, clone E2.2

The possibility to harness the intrinsic properties of bicontinuous microemulsions (BCME) has led to their application as nanocages in the synthesis of nanocoral-shaped Pt hierarchical superstructures (PtHSs). We employed two different platinum precursors (oil-soluble and water-soluble) and sodium borohydride (NaBH4) was used as reducing agent. PtHSs were observed to be pure and nanocrystalline, with small crystallite size (4.5-6.5 nm). XPS showed that the nanostructures are composed of Pt (0). The electrochemical surface areas obtained were 8.7 m(2)/g, 35.1 m(2)/g and 20.9 m(2)/g for PtH-O (synthesized using oil-soluble precursor), PtH-A2 and PtH-A4 (synthesized using 2 and 4% water-soluble Pt precursor), respectively. Linear sweep voltammetry (LSV), Tafel plots and electrochemical impedance spectroscopy (EIS) were used to study the hydrogen evolution reaction process. PtH-A2 showed an overpotential of 68 mV at 10 mA cm(-2), a Tafel slope of 30 mV dec(-1) and a low charge transfer resistance of 3.39 Omega. The stability of the PtHSs was tested before and after 1000 LSV cycles with minimal loss of current density. The improved electrochemical properties recorded, are due to the unique Pt hierarchical superstructure, which is key for the electrocatalytic performance. Through this research, the "template effect" of bicontinuous microemulsions towards the synthesis of metallic hierarchical superstructures is demonstrated for the first time. (C) 2020 Elsevier Ltd. All rights reserved.

Mean regression model could be inadequate if the probability distribution of the observed responses is not symmetric. Under such situation, the quantile regression turns to be a more robust alternative for accommodating outliers and misspecification of the error distribution, since it characterizes the entire conditional distribution of the outcome variable. This paper proposes a robust logistic quantile regression model by using a logit link function along the EM-based algorithm for maximum likelihood estimation of the pth quantile regression parameters in Galarza (Stat 6, 1, 2017). The aforementioned quantile regression (QR) model is built on a generalized class of skewed distributions which consists of skewed versions of normal, Student's t, Laplace, contaminated normal, slash, among other heavy-tailed distributions. We evaluate the performance of our proposal to accommodate bounded responses by investigating a synthetic dataset where we consider a full model including categorical and continuous covariates as well as several of its sub-models. For the full model, we compare our proposal with a non-parametric alternative from the so-called quantreg R package. The algorithm is implemented in the R package lqr, providing full estimation and inference for the parameters, automatic selection of best model, as well as simulation of envelope plots which are useful for assessing the goodness-of-fit.