Crystal structure analysis and molecular dynamics simulations of arginase from Thermus thermophilus.

Arginase is a manganese-dependent metalloenzyme that catalyzes the hydrolysis of L-arginine to L-ornithine and urea. The product L-ornithine is an important component which has wide applications in the healthcare and pharmaceutical industry. Enzymatic biosynthesis of L-ornithine is one of the effective methods in which arginase is used as a bio-catalyst. Here, we report the crystal structure of arginase from Thermus thermophilus (TtArginase) in three different crystal forms. All structures were solved by molecular replacement and refined at 2.0 Å, 2.3 Å and 2.91 Å resolution respectively. TtArginase is compared with other structural homologs and the putative catalytic site residues were identified. To understand the thermophilic nature of TtArginase, the sequence and structural factors of TtArginase was compared with its mesophilic counterpart Bacillus subtilis arginase (BsArginase). To get insights on structural stability, molecular dynamics (MD) simulations were carried for TtArginase and BsArginase at three different temperatures (300 K, 333 K and 353 K). The results indicate that TtArginase is comparatively more stable than BsArginase. MD simulations were carried out in the absence of the metal ions at the active site which revealed high plasticity of the active site. The results suggest that metal ions are critical not only for the catalytic function, but also required for the maintenance of the proper active site geometry. Since arginase can be employed for large-scale industrial production of L-ornithine, the structural details of thermophilic arginases such as TtArginase will be helpful to engineer the protein to optimize its enzymatic action in a variety of conditions.Communicated by Ramaswamy H. Sarma.

Apnoea detection using ECG signal based on machine learning classifiers and its performances.

Sleep apnoea is a common disorder affecting sleep quality by obstructing the respiratory airway. This disorder can also be correlated to certain diseases like stroke, depression, neurocognitive disorder, non-communicable disease, etc. We implemented machine learning techniques for detecting sleep apnoea to make the diagnosis easier, feasible, convenient, and cost-effective. Electrocardiography signals are the main input used here to detect sleep apnoea. The considered ECG signal undergoes pre-processing to remove noise and other artefacts. Next to pre-processing, extraction of time and frequency domain features is carried out after finding out the R-R intervals from the pre-processed signal. The power spectral density is calculated by using the Welch method for extracting the frequency-domain features. The extracted features are fed to different machine learning classifiers like Support Vector Machine, Decision Tree, k-nearest Neighbour, and Random Forest, for detecting sleep apnoea and performances are analysed. The result shows that the K-NN classifier obtains the highest accuracy of 92.85% compared to other classifiers based on 10 extracted features. The result shows that the proposed method of signal processing and machine learning techniques can be reliable and a promising method for detecting sleep apnoea with a reduced number of features.

Simultaneous measurements for the interlink of electro-thermo-mechano-electro characteristics in shape memory springs.

This experimental study aimed to obtain the actuation properties of Shape Memory Alloy (SMA) spring actuators available under the commercial names of Biometal® NiTiCu and KRL® NiTi NiTiCu, NiTiFe. An objective is to characterize the electro-thermo-mechano-electro behavior of shape memory spring through experimentation via simultaneous measurement techniques. These measurements are synchronized by embedding an impedance analyzer and data acquisition module through a unique program. Moreover, to obtain the mathematical model by parts of the SMA spring and equivalent circuit analysis of active spring through their electro-thermo-mechano-electro characteristics exhibited during shape memory effect. Based on the experimental results, the SMA spring's equivalent circuit is considered a series resistance - inductance circuit. It was found from the experimental results that the Biometal® actuator produced more resistance, inductance variation and offered more displacement as compared to KRL® actuators.

Differential resistance feedback control of a self-sensing shape memory alloy actuated system.

There is a growing trend towards miniaturization, and with it comes an increasing need for miniature sensors and actuators for control. Moreover situations occur wherein implementation of external physical sensor is impossible, here self-sensing lends its hand appropriately. Though self-sensing actuation (SSA) is extensively studied in piezoelectric, exploring this property in shape memory alloy is still under study. A simple scheme is developed which allows differential resistance measurement of antagonistic shape memory alloy actuated wires to concurrently sense and actuate in a closed loop system. The usefulness of the proposed scheme is experimentally verified by designing a one link manipulator arm and is performed in a real time tracking control. In a practical implementation of the self-sensing actuator a newly proposed signal processing electronic circuit is used for direct differential resistance feedback control upto a bandwidth of 1.8 Hz. The control design uses fuzzy PID which requires no detailed information about the constitutive model of SMA. At an operating frequency of 1 Hz, the result of the self-sensing feedback control with an angular tracking accuracy of ±0.06° over a movement range of ±15° is demonstrated.