Oscillation enhanced search for new interaction with neutrinos

We discuss the measurement of new physics in long baseline neutrino oscillation experiments. Through the neutrino oscillation, the probability to detect the new physics effects such as flavor violation is enhanced by the interference with the weak interaction. We carefully explain the situations that the interference can take place. Assuming a neutrino factory and an upgraded conventional beam, we estimate the feasibility to observe new physics numerically and point out that we can search new interactions using some channels, for example $\nu_{\mu} \to \nu_{\mu}$, in these experiments. We also discuss several models which induce the effective interactions interfering with the weak interaction, and show that some new physics effects are large enough to be observed in the oscillation enhanced way.Comment: 25 pages, 20 figure

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