The above figures show the mechanical design for low cost electronic knee prosthesis. This design is based on Goldfarb model and uses a lead screw and Maxon EC30 motor for linear actuation, The linear actuator can be replaced with an electrohydraulic one. The main aim is to create a simple and yet reliable sensor feedback system for electronic knee. Inertial measurement units and force sensors are used to provide primary feedback to the micro controller, which uses Cypress PSoC 4, designed for rapid prototyping. The prototype uses aluminium and carbon fiber keeping the overall weight of this knee below 1.8kg. Pylon acts as a base for housing the electronic circuitry

• Adaptive output feedback control system design for low cost electronic knee prosthesis. Proceedings in Advances in Intelligent and Soft Computing, Springer, July-2015, DOI: 10.1007/978-81-322-2517-1_52
• Real time acquisition of stump angle as a feedback signal for development of low cost electronic knee prosthesis, Indian Journal of Science of Technology, Jan-2016, DOI: 10.17485/ijst/2016/v9i2/74727

A rapid and single pot synthesis of polysaccharide stabilized silver nanoparticles (Ag NPs) has been achieved. The ability of Ag NPs to catalyze the reduction of hydrogen peroxide (H2O2) is successfully explored for the development of an optical fiber H2O2 sensor in the concentration range of 10−2 to 10−6 M.

A facile, one pot, completely green, and cheap route for the synthesis of gold nanoparticles (AuNPs) has been developed for the catalytic application of AuNPs for 4-nitrophenol reduction and room temperature ethanol vapor sensing.

A simple low cost and portable optical biosensor has been fabricated for the detection of Hg2+ based on inhibition of acid phosphatase activity. Poly-dimethoxysiloxane (PDMS) sensor chip module containing reaction and detection wells was fabricated. Acid phosphatase was immobilized at the bottom of reaction well. The detection was based on the measurement of transmitted light intensity through the yellow colored solution of p-nitrophenol (λ405) liberated as a result of enzymatic reaction and was measured in terms of volts.

A miniaturized optical biosensor for the detection of Hg2+ based on acid phosphatase inhibition

In the present study, a simple low cost and portable optical biosensor has been fabricated for the detection of Hg2+ based on inhibition of acid phosphatase activity. Poly-dimethoxysiloxane (PDMS) sensor chip module containing reaction and detection wells was fabricated. Acid phosphatase from the seeds of Macrotyloma uniflorum was immobilized at the bottom of reaction well to carry out the enzymatic reaction using p-nitrophenyl phosphate as a synthetic substrate. The detection was based on the measurement of transmitted light intensity through the yellow coloured solution of p-nitrophenol (λ405) liberated as a result of enzymatic reaction and was measured in terms of volts. The optical system was successfully employed for the detection of Hg2+ based on inhibition of enzyme activity. Response of the sensor was found to be linear in the range of 0.01–10 mM. The biosensor was stable up to 20 days of storage at 4 °C without any appreciable loss in activity.

Keywords

Optical biosensor; Acid phosphatase; Inhibition based; Portable device

Protein-protein interactions (PPIs) play a vital role in the biological processes involved in the cell functions and disease pathways. The experimental methods known to predict PPIs require tremendous efforts and the results are often hindered by the presence of a large number of false positives. Herein, we demonstrate the use of a new Genetic Programming (GP) based Symbolic Regression (SR) approach for predicting PPIs related to a disease. In a case study, a dataset consisting of one hundred and thirty five PPI complexes related to cancer was used to construct a generic PPI predicting model with good PPI prediction accuracy and generalization ability. A high correlation coefficient (CC) of 0.893, low root mean square error (RMSE) and mean absolute percentage error (MAPE) values of 478.221 and 0.239, respectively were achieved for both the training and test set outputs. To validate the discriminatory nature of the model, it was applied on a dataset of diabetes complexes where it yielded significantly low CC values. Thus, the GP model developed here serves a dual purpose: (a) a predictor of the binding energy of cancer related PPI complexes, and (b)a classifier for discriminating PPI complexes related to cancer from those of other diseases.

Article · Oct 2016 · IEEE/ACM Transactions on Computational Biology and Bioinformatics

Digital access to chemical journals resulted in a vast array of molecular information that is now available in the supplementary material files in PDF format. However, extracting this molecular information, generally from a PDF document format is a daunting task. An approach to harvest 3D molecular data from the supporting information of scientific research articles that are normally available from publisher’s resources is presented. In order to demonstrate the feasibility of extracting truly computable molecules from PDF file formats in a fast and efficient manner, we have developed a Java based application, namely ChemEngine. This program recognizes textual patterns from the supplementary data and generates standard molecular structure data (bond matrix, atomic coordinates) that can be subjected to a multitude of computational processes automatically. The methodology has been demonstrated via several case studies on different formats of coordinates data stored in supplementary information files, wherein ChemEngine selectively harvested the atomic coordinates and interpreted them as molecules with high accuracy. The reusability of extracted molecular coordinate data was demonstrated by computing Single Point Energies that were in close agreement with the original computed data provided with the articles. It is envisaged that the methodology will enable large scale conversion of molecular information from supplementary files available in the PDF format into a collection of ready- to- compute molecular data to create an automated workflow for advanced computational processes. Software along with source codes and instructions available at https://sourceforge.net/projects/chemengine/files/?source=navbar. (doi:10.1186/s13321-016-0175-x) contains supplementary material, which is available to authorized users.

In order to understand the molecular mechanism underlying any disease, knowledge about the interacting proteins in the disease pathway is essential. The number of revealed protein-protein interactions (PPI) is still very limited compared to the available protein sequences of different organisms. Experiment based high-throughput technologies though provide some data about these interactions, those are often fairly noisy. Computational techniques for predicting protein–protein interactions therefore assume significance.

1296 binary fingerprints that encode a combination of structural and geometric properties were developed using the crystallographic data of 15,000 protein complexes in the pdb server. In a case study, these fingerprints were created for proteins implicated in the Type 2 diabetes mellitus disease. The fingerprints were input into a SVM based model for discriminating disease proteins from non disease proteins yielding a classification accuracy of 78.2% (AUC value of 0.78) on an external data set composed of proteins retrieved via text mining of diabetes related literature. A PPI network was constructed and analysed to explore new disease targets. The integrated approach exemplified here has a potential for identifying disease related proteins, functional annotation and other proteomics studies

A new series of spirochromone annulated chalcone conjugates were synthesized and evaluated for their antitubercular activity against Mycobacterium tuberculosis H37Rv strain. These compounds were subjected to molecular modeling studies using docking and chemoinformatics based approaches. The docking simulations were performed against a range of known receptors for chalcone derived compounds to reveal MTB phosphotyrosine phosphatase B [MtbPtpB] protein as the most probable target based on the high binding affinity scores. Five compounds exhibit significant inhibition, showing minimum inhibitory concentration values i.e. MIC values ranging from 3.13–12.5 μg mL−1. Further analysis of the synthesized compounds with known and in-house developed chemoinformatics tools unequivocally established their potential as anti-tubercular compounds. QSAR modeling revealed a quantitative relationship between biological activities and frontier molecular orbital energies of synthesized compounds. The predictive model can be employed further for virtual screening of new compounds in this series.