Single‐step synthesis of a radically polymerizable 1,1′‐bi‐2‐naphthol derivative for asymmetric catalysis

A new polymerizable 1,1′‐bi‐2‐naphthol derivative for polymer‐supported catalytic asymmetric synthesis is presented. The synthesis is conducted within a single reaction step, which is a major advantage over other approaches presented in the literature. The ligand‐bearing polymer is prepared through copolymerization with N‐isopropylacrylamide. Preliminary experiments on the utility in catalytic asymmetric alkylation reactions reveal the accessibility and activity of the polymer‐attached catalysts. The stereoselectivity of the reaction is found to be somewhat lower than for reactions performed in the presence of free 1,1′‐bi‐2‐naphthol, and thus requires further optimization. The enantiomeric excess of the reaction products was determined via ¹H NMR spectroscopy after chiral derivatization with (R)‐α‐methylbenzyl isocyanate. © 2015 Society of Chemical Industry     

CO2 laser irradiation of raw and bleached cotton fabrics,with focus on water and dye absorbency

In this research, the effect of carbon dioxide laser irradiation on various properties of raw and bleached cotton fabrics, including fabric weight, bending rigidity, wetting, and air permeability, as well as dyeing, was examined and compared. The experiments were carried out at three different laser powers ranging from 4.5 to 6 W to determine the effect of laser treatment on fabric properties. In particular, the influence of laser irradiation on the dyeing properties of treated fabrics with CI Reactive Blue 198 was studied. The colour change of laser‐treated fabrics was determined by calculation of the K/S values as a function of fabric reflectance. The morphological changes in laser‐treated fabrics were observed by scanning electron microscopy. The effects of laser treatment on the properties of raw and bleached cotton fabrics were varied. For instance, the wettability of raw cotton samples was reduced after laser irradiation, whereas the wettability of bleached cotton fabrics was greater. Possible reasons for the various dyeing behaviours observed with irradiation at different laser powers are discussed.     

Determination of rock type and moisture content in flysch using TLS intensity in the Elerji quarry (south-west Slovenia)

Bulletin of Engineering Geology and the Environment - Terrestrial laser scanning (TLS) was performed in tectonically undisturbed flysch in the Elerji quarry (south-west Slovenia), which represents...     

Torrefied biomass‐polypropylene composites

Torrefied almond shells and wood chips were incorporated into polypropylene as fillers to produce torrefied biomass‐polymer composites. The composites were prepared by extrusion and injection molding. Response surface methodology was used to examine the effects of filler concentration, filler size, and lignin factor (relative lignin to cellulose concentration) on the material properties of the composites. The heat distortion temperatures, thermal properties, and tensile properties of the composites were characterized by thermomechanical analysis, differential scanning calorimetry, and tensile tests, respectively. The torrefied biomass composites had heat distortion temperatures of 8–24°C higher than that of neat polypropylene. This was due to the torrefied biomass restricting mobility of polypropylene chains, leading to higher temperatures for deformation. The incorporation of torrefied biomass generally resulted in an increase in glass transition temperature, but did not affect melting temperature. Also, the composites had lower tensile strength and elongation at break values than those of neat polypropylene, indicating weak adhesion between torrefied biomass and polypropylene. However, scanning electron microscopy results did indicate some adhesion between torrefied biomass and polypropylene. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132, 41582.     

Reduction‐Sensitive Dextran Nanogels Aimed for Intracellular Delivery of Antigens

Targeting of antigens to dendritic cells (DCs) to induce strong cellular immune response can be established by loading in a nano‐sized carrier and keeping the antigen associated with the particles until they are internalized by DCs. In the present study, a model antigen (ovalbumin, OVA) is immobilized in cationic dextran nanogels via disulfide bonds. These bonds are stable in the extracellular environment but are reduced in the cytosol of DCs due to the presence of glutathione. Reversible immobilization of OVA in the nanogels is demonstrated by the fact that hardly any release of the protein occurred at pH 7 in the absence of glutathione, whereas rapid release of OVA occurs once the nanogels are incubated in buffer with glutathione. Furthermore, these OVA conjugated nanogels show intracellular release of the antigen in DCs and boost the MHC class I antigen presentation, demonstrating the feasibility of this concept for the aimed intracellular antigen delivery.     

Correcting evaluation bias of relational classifiers with network cross validation

Recently, a number of modeling techniques have been developed for data mining and machine learning in relational and network domains where the instances are not independent and identically distributed (i.i.d.). These methods specifically exploit the statistical dependencies among instances in order to improve classification accuracy. However, there has been little focus on how these same dependencies affect our ability to draw accurate conclusions about the performance of the models. More specifically, the complex link structure and attribute dependencies in relational data violate the assumptions of many conventional statistical tests and make it difficult to use these tests to assess the models in an unbiased manner. In this work, we examine the task of within-network classification and the question of whether two algorithms will learn models that will result in significantly different levels of performance. We show that the commonly used form of evaluation (paired t-test on overlapping network samples) can result in an unacceptable level of Type I error. Furthermore, we show that Type I error increases as (1) the correlation among instances increases and (2) the size of the evaluation set increases (i.e., the proportion of labeled nodes in the network decreases). We propose a method for network cross-validation that combined with paired t-tests produces more acceptable levels of Type I error while still providing reasonable levels of statistical power (i.e., 1−Type II error).