A Bioinformatics Filtering Strategy for Identifying Radiation Response Biomarker Candidates

The number of biomarker candidates is often much larger than the number of clinical patient data points available, which motivates the use of a rational candidate variable filtering methodology. The goal of this paper is to apply such a bioinformatics filtering process to isolate a modest number (<10) of key interacting genes and their associated single nucleotide polymorphisms involved in radiation response, and to ultimately serve as a basis for using clinical datasets to identify new biomarkers. In step 1, we surveyed the literature on genetic and protein correlates to radiation response, in vivo or in vitro, across cellular, animal, and human studies. In step 2, we analyzed two publicly available microarray datasets and identified genes in which mRNA expression changed in response to radiation. Combining results from Step 1 and Step 2, we identified 20 genes that were common to all three sources. As a final step, a curated database of protein interactions was used to generate the most statistically reliable protein interaction network among any subset of the 20 genes resulting from Steps 1 and 2, resulting in identification of a small, tightly interacting network with 7 out of 20 input genes. We further ranked the genes in terms of likely importance, based on their location within the network using a graph-based scoring function. The resulting core interacting network provides an attractive set of genes likely to be important to radiation response

Electricity Generation from Synthetic Wastewater Treatment in Microbial Fuel Cell

Background and Objectives: Microbial fuel cell (MFC) used for electricity generation and wastewater treatment, simultaneously. In MFC, microorganisms act as a catalyst to convert chemical energy stored in organic materials into electrical energy. This study was performed with the aim of electricity generation from synthetic wastewater treatment in microbial fuel cell. Methods: A dual chambered microbial fuel cell was operated in continuous flow for 720 hours at temperature 20±4ºC at different organic loading rates and hydraulic retention times. Results: Organic loading rate and hydraulic retention time were effective factors for the power production and removal of organic loading rate. Maximum COD removal efficiency was observed as 49% for a period of 1.5 to 2.5 hours that this range of time can be used as optimum retention time for operation of microbial fuel cell reactor. Maximum voltage and power production were obtained 700 mV and 1700 mW/m2, respectively. Conclusion: Considering the advantages such as direct electricity generation from wastewater and considerable removal efficiency of organic loading rate, wastewater treatment in a microbial fuel cell, application of industrial scale microbial fuel cell for wastewater treatment is recommended after complementary studies and economic assessment