Fluorescent polyacrylate derivate from 4-biphenylmethanol with UV-green emission

In this paper we report the synthesis of a new polyacrylate named poly(1,1?-BP4MA) which is a derivate from 4-biphenylmethanol monomer. Poly(1,1?-BP4MA) was obtained by solution and bulk polymerization techniques to yield polymers with high molecular weight and high solubility. The study of the optical properties showed that poly(1,1?-BP4MA) is a fluorescent material with emission in the UV-green region and it has similar quantum yield to tryptophan.     

Topological Imaging Methods for the Iterative Detection of Multiple Impedance Obstacles

Journal of Mathematical Imaging and Vision - In this paper, we investigate shape inversion algorithms based on the computation of iterated topological derivatives for the detection of multiple...     

Combination of Reaction and Separation in Heterogeneous Catalytic Hydrogenation of Ethylformate

Continuous hydrogenation reaction of ethyl benzoylformate was studied over a (–)‐cinchonidine (CD)‐modified Pt/Al₂O₃ catalyst. The catalyst showed a good stability, and high enantioselectivity was achieved in the fixed‐bed reactor. Chromatographic separation of (R)‐ and (S)‐ethyl mandelate originating from a post‐continuous hydrogenation reaction of ethyl benzoylformate over the (–)‐CD‐modified Pt/Al₂O₃ catalyst was investigated in the same reaction mixture. A commercial column filled with a chiral selector resin was chosen as a perspective preparative‐scale adsorbent. Since adsorption equilibrium isotherms were linear within the entire investigated range of concentrations, they were determined by pulse experiments for the isomers present in a post‐reaction mixture. Breakthrough curves were measured and described successfully by the dispersive plug‐flow model with linear driving force approximation.     

Invited review: Potential antiobesity effect of fermented dairy products

The growing prevalence of obesity affects millions of people around the world and has gained increased attention over the years because it is associated with the development of other chronic degenerative diseases. Different organizations recommend lifestyle changes to treat obesity; nevertheless, other strategies in addition to lifestyle changes have recently been suggested. One of these strategies is the use of probiotics in fermented dairy products; however, a need exists to review the different studies available related to the potential antiobesity effect of these products. Because probiotic fermented dairy products that support weight management are not available in the market, there is a great opportunity for the development of functional dairy products with new lactic acid bacteria that may present this added health benefit. Thus, the purpose of this overview is to highlight the importance of probiotic fermented dairy products as potential antiobesogenic functional foods and present in vitro and in vivo studies required before this kind of product may be introduced to the market. Overall, most studies attributed the antiobesity effect of fermented dairy foods to the probiotic strains present; however, bioactive peptides released during milk fermentation may also be responsible for this effect.     

Increases in nitrogen use efficiency decrease nitrous oxide emissions but can penalize yield in sugarcane

Nutrient Cycling in Agroecosystems - Nitrogen (N) fertilization strategies focused on increasing nitrogen use efficiency (NUE) and decreasing nitrous oxide (N2O) emissions are important for...     

Parastrephia quadrangularis: A Possible Alternative to Inhibit the Microbial Effect on the Generation of Acid Mine Drainage

Mine Water and the Environment - The role of iron- and sulfur- oxidizing microorganisms in the generation of acid mine drainage (AMD) from sulfide ores and tailings is widely recognized. The...     

R2D2: A scalable deep learning toolkit for medical imaging segmentation

Deep learning has gained a significant popularity in recent years thanks to its tremendous success across a wide range of relevant fields of applications, including medical image analysis domain in particular. Although convolutional neural networks (CNNs) based medical applications have been providing powerful solutions and revolutionizing medicine, efficiently training of CNNs models is a tedious and challenging task. It is a computationally intensive process taking long time and rare system resources, which represents a significant hindrance to scientific research progress. In order to address this challenge, we propose in this article, R2D2, a scalable intuitive deep learning toolkit for medical imaging semantic segmentation. To the best of our knowledge, the present work is the first that aims to tackle this issue by offering a novel distributed versions of two well-known and widely used CNN segmentation architectures [ie, fully convolutional network (FCN) and U-Net]. We introduce the design and the core building blocks of R2D2. We further present and analyze its experimental evaluation results on two different concrete medical imaging segmentation use cases. R2D2 achieves up to 17.5× and 10.4× speedup than single-node based training of U-Net and FCN, respectively, with a negligible, though still unexpected segmentation accuracy loss. R2D2 offers not only an empirical evidence and investigates in-depth the latest published works but also it facilitates and significantly reduces the effort required by researchers to quickly prototype and easily discover cutting-edge CNN configurations and architectures.