Aug
12
Mon
2013
Delegate Talk: Development of a Phototrophic Microbial Fuel Cell with sacrificial electrodes and a novel proton exchange matrix @ Sathyam Hall
Aug 12 @ 2:40 pm – 2:55 pm

ajithAjith Madhavan
Assistant Professor, School of Biotechnology, Amrita University


Development of a Phototrophic Microbial Fuel Cell with sacrificial electrodes and a novel proton exchange matrix

If micro organisms can solve Sudoku and possibly have feelings, who is to say that they cannot also solve the planet’s energy crisis? Mr. Madhavan employs micro organisms to produce energy using microbial fuel cell (MFC). Micro organisms go through a series of cycles and pathways in order to survive, including the Electron Transport Pathway (ETP) in which bacteria release electrons which can be tapped as energy. In a two-chambered MFC, micro organisms interact with an anode in one chamber and in the presence of an oxidizing agent in the cathodic chamber scavenges electrons from the cathode. The two chambers are connected by an external circuit and connected to a load. In between the two chambers is a proton exchange membrane (PEM) which transports protons from the second chamber to the first and acts as a barrier for electrons. Therefore, a renewable source of energy can be maintained by just providing your bacterial culture with the proper nutrients to thrive and remain happy and satisfied (assuming they have emotions).

Mr. Madhavan has done extensive work on such MFCs and has experimented with various micro organisms and substrates to achieve high energy production. The phototropic MFC Mr. Madhavan designed using Synechococcus elongates using waste water as a substrate was able to generate approximately 10 mȦ and 1 volt of electricity. Other research in this area has even shown that using human urine can be used as a substrate for certain bacteria to produce enough energy to charge a mobile phone.

Although this microbial technology seems to be the “next big thing” (despite their small size) when it comes to renewable energy sources there is still a lot of work to be done before these bacteria batteries hit the market. As of now the MFCs are still much less efficient than solar cells and the search for the perfect bacteria and substrate continues.

Aug
13
Tue
2013
Invited Talk: Applying Machine learning for Automated Identification of Patient Cohorts @ Sathyam Hall
Aug 13 @ 2:40 pm – 3:05 pm

SriSairamSrisairam Achuthan, Ph.D.
Senior Scientific Programmer, Research Informatics Division, Department of Information Sciences, City of Hope, CA, USA


Applying Machine learning for Automated Identification of Patient Cohorts

Srisairam Achuthan, Mike Chang, Ajay Shah, Joyce Niland

Patient cohorts for a clinical study are typically identified based on specific selection criteria. In most cases considerable time and effort are spent in finding the most relevant criteria that could potentially lead to a successful study. For complex diseases, this process can be more difficult and error prone since relevant features may not be easily identifiable. Additionally, the information captured in clinical notes is in non-coded text format. Our goal is to discover patterns within the coded and non-coded fields and thereby reveal complex relationships between clinical characteristics across different patients that would be difficult to accomplish manually. Towards this, we have applied machine learning techniques such as artificial neural networks and decision trees to determine patients sharing similar characteristics from available medical records. For this proof of concept study, we used coded and non-coded (i.e., clinical notes) patient data from a clinical database. Coded clinical information such as diagnoses, labs, medications and demographics recorded within the database were pooled together with non-coded information from clinical notes including, smoking status, life style (active / inactive) status derived from clinical notes. The non-coded textual information was identified and interpreted using a Natural Language Processing (NLP) tool I2E from Linguamatics.