We analyze the three-year SDSS-II Superernova (SN) Survey data and identify a sample of 1070 photometric SN Ia candidates based on their multi-band light curve data. This sample consists of SN candidates with no spectroscopic confirmation, with a subset of 210 candidates having spectroscopic redshifts of their host galaxies measured, while the remaining 860 candidates are purely photometric in their identification. We describe a method for estimating the efficiency and purity of photometric SN Ia classification when spectroscopic confirmation of only a limited sample is available, and demonstrate that SN Ia candidates from SDSS-II can be identified photometrically with ∼ 91% efficiency and with a contamination of ∼ 6%. Although this is the largest uniform sample of SN candidates to date for studying photometric identification, we find that a larger spectroscopic sample of contaminating sources is required to obtain a better characterization of the background events. A Hubble diagram using SN candidates with no spectroscopic confirmation, but with host galaxy spectroscopic redshifts, yields a distance modulus dispersion that is only ∼ 20 − 40% larger than that of the spectroscopicallyconfirmed SN Ia sample alone with no significant bias. A Hubble diagram with purely photometric classification and redshift-distance measurements, however, exhibit biases that require further investigation for precision cosmology.

Microglia have become a therapeutic target of many inflammation-mediated diseases in the central nervous system (CNS). Recently, microRNA (miRNA) has been proposed as an important regulator of immune responses. Specifically,...