Here, we report a one-pot preparation of a ruthenium-based reduced graphene oxide hybrid (Runano-based RGO) for supercapacitors applications.

The acid dissociation constants of some 2-mercaptobenzazoles in aqueous-organic solvent mixtures are determined potentiometrically at (25 ± 0.1) °C and an ionic strength of I = 0.02 mol dm-3 (KNO3). The organic solvents are methanol, ethanol, DMF, DMSO, and acetonitrile. The pK a values are discussed in terms of the proportion and nature of the organic cosolvent. It was concluded that hydrogen bonding interactions of the conjugate base with the solvent and the solvent basicity in addition to the electrostatic effect contribute the major effects in the ionization process. Moreover, thermodynamic functions (ΔH, ΔG, and ΔS) of the ionization process in an aqueous medium containing 0.1 mole fraction of ethanol are also determined and discussed.

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Despite the identification of the high incidence red cell antigen Era nearly 40 years ago, the molecular background of this antigen, together with the other two members of the Er blood group collection, has yet to be elucidated. Whole exome and Sanger sequencing of individuals with serologically defined Er alloantibodies identified several missense mutations within the PIEZO1 gene, encoding amino acid substitutions within the extracellular domain of the Piezo1 mechanosensor ion channel. Confirmation of Piezo1 as the carrier molecule for the Er blood group antigens was demonstrated using immunoprecipitation, CRISPR/Cas9-mediated gene knockout and expression studies in an erythroblast cell line. We report the molecular bases of five Er blood group antigens: the recognised Era, Erb and Er3 antigens; and two novel high incidence Er antigens, described here as Er4 and Er5, establishing a new blood group system. Anti-Er4 and anti-Er5 are implicated in severe hemolytic disease of the fetus and newborn (HDFN). Demonstration of Piezo1, present at just a few hundred copies on the surface of the red blood cell, as the site of a new blood group system highlights the potential antigenicity of even low abundance membrane proteins and contributes to our understanding of the in vivo characteristics of this important and widely studied protein in transfusion biology and beyond.

In real-world problems, input data may be pervaded with uncertainty. In this paper, we investigate the behavior of naive possibilistic classifiers, as a counterpart to naive Bayesian ones, for dealing with classification tasks in presence of uncertainty. For this purpose, we extend possibilistic classifiers, which have been recently adapted to numerical data, in order to cope with uncertainty in data representation. Here the possibility distributions that are used are supposed to encode the family of Gaussian probabilistic distributions that are compatible with the considered data set. We consider two types of uncertainty: i) the uncertainty associated with the class in the training set, which is modeled by a possibility distribution over class labels, and ii) the imprecision pervading attribute values in the testing set represented under the form of intervals for continuous data. Moreover, the approach takes into account the uncertainty about the estimation of the Gaussian distribution parameters due to the limited amount of data available. We first adapt the possibilistic classification model, previously proposed for the certain case, in order to accommodate the uncertainty about class labels. Then, we propose an algorithm based on the extension principle to deal with imprecise attribute values. The experiments reported show the interest of possibilistic classifiers for handling uncertainty in data. In particular, the probability-to-possibility transform-based classifier shows a robust behavior when dealing with imperfect data.