MVME-RCMFDE framework for discerning hyper-responsivity in Autism Spectrum Disorders.

Background: Autism Spectrum Disorder (ASD), characterized by impaired sensory processing, has a wide range of clinical heterogeneity, which handicaps effective therapeutic interventions. Therefore, it is imperative to develop potential mechanisms for delineating clinically meaningful subgroups, so as to provide individualised medical treatment. In this study, an attempt is being made to differentiate the hyper-responsive subgroup from ASD by analysing the complexity pattern of Visual Evoked Potentials (VEPs), recorded from a group of 30 ASD participants, in the presence of vertical achromatic sinewave gratings at varying contrast conditions of low (5%), medium (50%) and high (90%).

A noise-robust sparse approach to the time-frequency representation of visual evoked potentials.

Background: Visual evoked potential (VEP) offers a promising research strategy in the effort to characterise brain disorders. Pertinent signal processing techniques enable the development of potential applications of VEP. A joint time-frequency (TF) representation provides more comprehensive information about the underlying complex structures of these signals than individual time or frequency analysis.

Randomly amplified polymorphic DNA analysis of isolated from bovine and bubaline mastitis in Karnataka.

Background And Aim: In recent times, non-aureus staphylococci (NAS) have emerged as the major organisms isolated from mastitis cases in dairy animals, with a predominance of and . As compared to , much less is known about the molecular types or the spatiotemporal epidemiology of these NAS species. In the present study, randomly amplified polymorphic DNA (RAPD) was employed to detect genetic polymorphisms, intraspecies diversity, and epidemiology of strains (n=37) isolated from bovine and bubaline mastitis cases in the state of Karnataka.

A hybrid method for artifact removal of visual evoked EEG.

Background: The visual evoked Electroencephalogram (EEG) signals are useful indicators to explore the hidden neural circuitry in human brain. But these signals are highly contaminated with a plethora of artifacts arising from power interference, eye, muscle and cardiac movements. Since the interference components include neural activity also, the existing techniques result in the distortion of the underlying cerebral signals.