Automated Protein Secondary Structure Assignment from Cα Positions Using Neural Networks.

The assignment of secondary structure elements in protein conformations is necessary to interpret a protein model that has been established by computational methods. The process essentially involves labeling the amino acid residues with H (Helix), E (Strand), or C (Coil, also known as Loop). When particular atoms are absent from an input protein structure, the procedure becomes more complicated, especially when only the alpha carbon locations are known. Various techniques have been tested and applied to this problem during the last forty years. The application of machine learning techniques is the most recent trend. This contribution presents the HECA classifier, which uses neural networks to assign protein secondary structure types. The technique exclusively employs Cα coordinates. The Keras (TensorFlow) library was used to implement and train the neural network model. The BioShell toolkit was used to calculate the neural network input features from raw coordinates. The study's findings show that neural network-based methods may be successfully used to take on structure assignment challenges when only Cα trace is available. Thanks to the careful selection of input features, our approach's accuracy (above 97%) exceeded that of the existing methods.

Improved mechanical strength, proton conductivity and power density in an 'all-protonic' ceramic fuel cell at intermediate temperature.

Protonic ceramic fuel cells (PCFCs) have become the most efficient, clean and cost-effective electrochemical energy conversion devices in recent years. While significant progress has been made in developing proton conducting electrolyte materials, mechanical strength and durability still need to be improved for efficient applications. We report that adding 5 mol% Zn to the Y-doped barium cerate-zirconate perovskite electrolyte material can significantly improve the sintering properties, mechanical strength, durability and performance. Using same proton conducting material in anodes, electrolytes and cathodes to make a strong structural backbone shows clear advantages in mechanical strength over other arrangements with different materials. Rietveld analysis of the X-ray and neutron diffraction data of BaCe0.7Zr0.1Y0.15Zn0.05O3-δ (BCZYZn05) revealed a pure orthorhombic structure belonging to the Pbnm space group. Structural and electrochemical analyses indicate highly dense and high proton conductivity at intermediate temperature (400-700 °C). The anode-supported single cell, NiO-BCZYZn05|BCZYZn05|BSCF-BCZYZn05, demonstrates a peak power density of 872 mW cm-2 at 700 °C which is one of the highest power density in an all-protonic solid oxide fuel cell. This observation represents an important step towards commercially viable SOFC technology.

Management of painful sickle cell crisis in pregnancy.

A case of sickle cell disease crisis is described in a Saudi patient at 28 weeks of gestation. She was successfully managed with intravenous fluids, oxygen, opiates and blood transfusion.