An upper limit on the contribution of accreting white dwarfs to the type Ia supernova rate

There is wide agreement that Type Ia supernovae (used as standard candles for cosmology) are associated with the thermonuclear explosions of white dwarf stars. The nuclear runaway that leads to the explosion could start in a white dwarf gradually accumulating matter from a companion star until it reaches the Chandrasekhar limit, or could be triggered by the merger of two white dwarfs in a compact binary system. The X-ray signatures of these two possible paths are very different. Whereas no strong electromagnetic emission is expected in the merger scenario until shortly before the supernova, the white dwarf accreting material from the normal star becomes a source of copious X-rays for ~1e7 yr before the explosion. This offers a means of determining which path dominates. Here we report that the observed X-ray flux from six nearby elliptical galaxies and galaxy bulges is a factor of ~30-50 less than predicted in the accretion scenario, based upon an estimate of the supernova rate from their K-band luminosities. We conclude that no more than ~5 per cent of Type Ia supernovae in early type galaxies can be produced by white dwarfs in accreting binary systems, unless their progenitors are much younger than the bulk of the stellar population in these galaxies, or explosions of sub-Chandrasekhar white dwarfs make a significant contribution to the supernova rate.Comment: 10 pages, 1 tabl

An FPGA Implementation to Detect Selective Cationic Antibacterial Peptides

Exhaustive prediction of physicochemical properties of peptide sequences is used in different areas of biological research. One example is the identification of selective cationic antibacterial peptides (SCAPs), which may be used in the treatment of different diseases. Due to the discrete nature of peptide sequences, the physicochemical properties calculation is considered a high-performance computing problem. A competitive solution for this class of problems is to embed algorithms into dedicated hardware. In the present work we present the adaptation, design and implementation of an algorithm for SCAPs prediction into a Field Programmable Gate Array (FPGA) platform. Four physicochemical properties codes useful in the identification of peptide sequences with potential selective antibacterial activity were implemented into an FPGA board. The speed-up gained in a single-copy implementation was up to 108 times compared with a single Intel processor cycle for cycle. The inherent scalability of our design allows for replication of this code into multiple FPGA cards and consequently improvements in speed are possible. Our results show the first embedded SCAPs prediction solution described and constitutes the grounds to efficiently perform the exhaustive analysis of the sequence-physicochemical properties relationship of peptides