Label-free detection of sex determining region Y (SRY) via capacitive biosensor

In this work, we present for the first time, the use of a simple fractal capacitive biosensor for the quantification and detection of sex-determining region Y (SRY) genes. This section of genetic code, which is found on the Y chromosome, finds importance for study as it causes fetuses to develop characteristics of male sex-like gonads when a mutation occurs. It is also an important genetic code in men, and disorders involving the SRY gene can cause infertility and sexual malfunction that lead to a variety of gene mutational disorders. We have therefore designed silicon-based, label-free fractal capacitive biosensors to quantify various proteins and genes. We take advantage of a good dielectric material, Parylene C for enhancing the performance of the sensors. We have integrated these sensors with a simple microchannel for easy handling of fluids on the detection area. The read-out value of an Agilent LCR meter used to measure capacitance of the sensor at a frequency of 1 MHz determined gene specificity and gene quantification. These data revealed that the capacitance measurement of the capacitive biosensor for the SRY gene depended on both the target and the concentration of DNA. The experimental outcomes in the present study can be used to detect DNA and its variations in crucial fields that have a great impact on our daily lives, such as clinical and veterinary diagnostics, industrial and environmental testing and forensic sciences.

A light responsive two-component supramolecular hydrogel: a sensitive platform for the fabrication of humidity sensors

The supramolecular assembly of anionic azobenzene dicarboxylate and cationic cetyltrimethylammonium bromide (CTAB) formed a stimuli responsive hydrogel with a critical gelation concentration (CGC) of 0.33 wt%. This self-sustainable two-component system was able to repair damage upon light irradiation. Moreover, it was successfully employed in the fabrication of highly sensitive humidity sensors for the first time.

Improving EMG based classification of basic hand movements using EMD

This paper presents a pattern recognition approach for the identification of basic hand movements using surface electromyographic (EMG) data. The EMG signal is decomposed using Empirical Mode Decomposition (EMD) into Intrinsic Mode Functions (IMFs) and subsequently a feature extraction stage takes place. Various combinations of feature subsets are tested using a simple linear classifier for the detection task. Our results suggest that the use of EMD can increase the discrimination ability of the conventional feature sets extracted from the raw EMG signal.