Aditya Murthy, Ph.D.
Associate Professor, Centre For Neuroscience, Indian Institute of Science, Bangalore, India
Since Karl Lashley’s seminal work on the formulation of serial order, numerous models assume simultaneous representation of competitive elements of a sequence, to account for serial order effects in different types of behavior like typing, speech, etc. Such models follow two basic assumptions: (1) more than one plan representation can be simultaneously active in a planning layer; (2) the most active plan is chosen in another layer called the competitive choice layer. Using the oculomotor system I will describe behavioral and neurophysiological experiments that tests the two critical predictions of such queuing models, providing evidence that basal ganglia in monkeys and humans instantiate a form of queuing that transforms parallel movement representations into more serial representations, allowing for the expression of sequential saccadic eye movements.
Shantikumar Nair, Ph.D.
Professor & Director, Amrita Center for Nanosciences & Molecular Medicine, Amrita University, India
Spatially Distributed and Hierarchical Nanomaterials in Biotechnology
Although nano materials are well investigated in biotechnology in their zero-, one- and two-dimensional forms, three-dimensional nanomaterials are relatively less investigated for their biological applications. Three dimensional nano materials are much more complex with several structural and hierarchical variables controlling their mechanical, chemical and biological functionality. In this talk examples are given of some complex three dimensional systems including, scaffolds, aggregates, fabrics and membranes. Essentially three types of hierarchies are considered: one-dimensional hierarchy, two-dimensional hierarchy and three-dimensional hierarchy each giving rise to unique behaviors.
Jaydeep Unni, Ph.D.
Sr. Project Manager, Robert Bosch Healthcare Systems, Palo Alto, CA
Remote Patient Monitoring – Challenges and Opportunities
Remote Patient Monitoring (RPM) is gaining importance and acceptance with rising number of chronic disease conditions and with increase in the aging population. As instances of Heart diseases, Diabetes etc are increasing the demand for these technologies are increasing. RPM devices typically collect patient vital sign data and in some case also patient responses to health related questions. Thus collected data is then transmitted through various modalities (wireless/Bluetooth/cellular) to Hospitals/Doctor’s office for clinical evaluation. With these solutions Doctors are able to access patient’s vital data ‘any time any where’ thus enabling them to intervene on a timely and effective manner. For older adult population chronic disease management, post-acute care management and safety monitoring are areas were RPM finds application. That said, there are significant challenges in adoption of Remote Patient Monitoring including patient willingness and compliance for adoption, affordability, availability of simpler/smarter technology to mention a few. But experts contend that if implemented correctly Remote Patient Monitoring can contain healthcare expenditure by reducing avoidable hospitalization while greatly improving quality of care.
Satheesh Babu T. G., Ph.D.
Associate Professor, Department of Sciences, School of Engineering, Amrita University, Coimbatore, India
Nanomaterials for ‘enzyme-free’ biosensing
Enzyme based sensors have many draw backs such as poor storage stability, easily affected by the change in pH and temperature and involves complicated enzyme immobilization procedures. To address this limitation, an alternative approach without the use of enzyme, “non-enzymatic” has been tried recently. Choosing the right catalyst for direct electrochemical oxidation / reduction of a target molecule is the key step in the fabrication of non-enzymatic sensors.
Non-enzymatic sensors for glucose, creatinine, vitamins and cholesterol are fabricated using different nanomaterials, such as nanotubes, nanowires and nanoparticles of copper oxide, titanium dioxide, tantalum oxide, platinum, gold and graphenes. These sensors selectively catalyse the targeted analyte with very high sensitivity. These nanomaterials based sensors combat the drawbacks of enzymatic sensors.
