Folding of prestin's anion-binding site and the mechanism of outer hair cell electromotility.

Prestin responds to transmembrane voltage fluctuations by changing its cross-sectional area, a process underlying the electromotility of outer hair cells and cochlear amplification. Prestin belongs to the SLC26 family of anion transporters yet is the only member capable of displaying electromotility. Prestin's voltage-dependent conformational changes are driven by the putative displacement of residue R399 and a set of sparse charged residues within the transmembrane domain, following the binding of a Cl anion at a conserved binding site formed by the amino termini of the TM3 and TM10 helices.

Measuring the impact of the digital economy in developing countries: A systematic review and meta- analysis.

The digital economy, driven by Information and Communication Technology (ICT), has emerged as a significant contributor to economies worldwide. However, accurately defining and measuring its impact on national economies remains a complex endeavor. This paper explores the definition, measurement, role, and impacts of the digital economy across various economies.

Folding of Prestin's Anion-Binding Site and the Mechanism of Outer Hair Cell Electromotility.

Prestin responds to transmembrane voltage fluctuations by changing its cross-sectional area, a process underlying the electromotility of outer hair cells and cochlear amplification. Prestin belongs to the SLC26 family of anion transporters yet is the only member capable of displaying electromotility. Prestin's voltage-dependent conformational changes are driven by the putative displacement of residue R399 and a set of sparse charged residues within the transmembrane domain, following the binding of a Cl anion at a conserved binding site formed by amino termini of the TM3 and TM10 helices.

Factors That Control the Force Needed to Unfold a Membrane Protein in Silico Depend on the Mode of Denaturation.

Single-molecule force spectroscopy methods, such as AFM and magnetic tweezers, have proved extremely beneficial in elucidating folding pathways for soluble and membrane proteins. To identify factors that determine the force rupture levels in force-induced membrane protein unfolding, we applied our near-atomic-level molecular dynamics package to study the vertical and lateral pulling of bacteriorhodopsin (bR) and GlpG, respectively. With our algorithm, we were able to selectively alter the magnitudes of individual interaction terms and identify that, for vertical pulling, hydrogen bond strength had the strongest effect, whereas other non-bonded protein and membrane-protein interactions had only moderate influences, except for the extraction of the last helix where the membrane-protein interactions had a stronger influence.

A systematic review and Meta-data analysis on the applications of Deep Learning in Electrocardiogram.

Unlabelled: The success of deep learning over the traditional machine learning techniques in handling artificial intelligence application tasks such as image processing, computer vision, object detection, speech recognition, medical imaging and so on, has made deep learning the buzz word that dominates Artificial Intelligence applications. From the last decade, the applications of deep learning in physiological signals such as electrocardiogram (ECG) have attracted a good number of research. However, previous surveys have not been able to provide a systematic comprehensive review including biometric ECG based systems of the applications of deep learning in ECG with respect to domain of applications.