A π-π Bonding-Assisted Molecular-Wiring of Folded-Cytochrome c and Naphthoquinone and Its Electron-Relay-Based Bioelectrocatalytic H2O2 Reduction Reaction Visualized by Redox-Competitive Scanning Electrochemical Microscopy

The electron-transfer (ET) reaction of cytochrome c (Cytc) protein with biomolecules is a cutting-edge research area of interest in understanding the functionalities of natural systems. Several electrochemical biomimicking studies based on Cytc-protein-modified electrodes prepared via electrostatic interaction and covalent bonding approaches have been reported. Indeed, natural enzymes involve multiple types of bonding, such as hydrogen, ionic, covalent, and π-π, etc. In this work, we explore a Cytc-protein chemically modified glassy carbon electrode (GCE/CB@NQ/Cytc) prepared via π-π bonding using graphitic carbon as an underlying surface and an aromatic organic molecule, naphthoquinone (NQ), as a cofactor for an effective ET reaction. A simple drop-casting technique-based preparation of GCE/CB@NQ showed a distinct surface-confined redox peak at a standard electrode potential (E°) = -0.2 V vs Ag/AgCl (surface excess = 21.3 nmol cm-2) in pH 7 phosphate buffer solution. A control experiment of modification of NQ on an unmodified GCE failed to show any such unique feature. For the preparation of GCE/CB@NQ/Cytc, a dilute solution of Cytc-pH 7 phosphate buffer was drop-cast on the GCE/CB@NQ surface, wherein the protein folding and denaturalization-based complication and its associated ET functionalities were avoided. Molecular dynamics simulation studies show the complexation of NQ with Cytc at the protein binding sites. The protein-bound surface shows an efficient and selective bioelectrocatalytic reduction performance of H2O2, as demonstrated using cyclic voltammetry and amperometric i-t techniques. Finally, the redox-competition scanning electrochemical microscopy (RC-SECM) technique was adopted for in situ visualization of the electroactive adsorbed surface. The RC-SECM images clearly show the regions of highly bioelectrocatalytic active sites of Cytc-proteins bound to NQ molecules on a graphitic carbon surface. The binding of Cytc with NQ has significant implications for studying the biological electron transport mechanism, and the proposed method provides the requisite framework for such a study.

An Enhanced Deep Recurrent Neural Network for Autism Spectrum Disorder Diagnosis

Autism Spectrum Disorder is one of the major investigation area in current era. There are many research works introduced earlier for handling the Autism Spectrum Disorders. However those research works doesn’t achieve the expected accuracy level. The accuracy and prediction efficiency can be increased by building a better classification system using Deep Learning. This paper focuses on the deep learning technique for Autism Diagnosis and the domain identification. In the proposed work, an Enhanced Deep Recurrent Neural Network has been developed for the detection of ASD at all ages. It attempts to predict the autism spectrum in the children along with prediction of areas which can predict the autism in the prior level. The main advantage of EDRNN is to provide higher accuracy in classification and domain identification. Here Artificial Algal Algorithm is used for identifying the most relevant features from the existing feature set. This model was evaluated for the data that followed Indian Scale for Assessment of Autism. The results obtained for the proposed EDRNN has better accuracy, sensitivity, specificity, recall and precision.