Molecular Genetic Typing of Staphylococcus aureus from Cows, Goats, Sheep, Rabbits and Chickens

End of project reportsS. aureus can also cause a number of infections in animals such as tick-associated pyaemia in lambs, staphylococcosis in rabbits, septicaemia, abscesses and chondronecrosis in chickens and pneumonia and osteomyelitis complex in turkeys. S. aureus is the most frequent cause of bovine mastitis, a disease that is of economic importance worldwide (Beck et al., 1992). Typically staphylococcal mastitis is chronic in nature, with subclinical mastitis being the most common form

Magnetic Fields in Stellar Jets

Although several lines of evidence suggest that jets from young stars are driven magnetically from accretion disks, existing observations of field strengths in the bow shocks of these flows imply that magnetic fields play only a minor role in the dynamics at these locations. To investigate this apparent discrepancy we performed numerical simulations of expanding magnetized jets with stochastically variable input velocities with the AstroBEAR MHD code. Because the magnetic field B is proportional to the density n within compression and rarefaction regions, the magnetic signal speed drops in rarefactions and increases in the compressed areas of velocity-variable flows. In contrast, B ~ n^0.5 for a steady-state conical flow with a toroidal field, so the Alfven speed in that case is constant along the entire jet. The simulations show that the combined effects of shocks, rarefactions, and divergent flow cause magnetic fields to scale with density as an intermediate power 1 > p > 0.5. Because p > 0.5, the Alfven speed in rarefactions decreases on average as the jet propagates away from the star. This behavior is extremely important to the flow dynamics because it means that a typical Alfven velocity in the jet close to the star is significantly larger than it is in the rarefactions ahead of bow shocks at larger distances, the one place where the field is a measurable quantity. We find that the observed values of weak fields at large distances are consistent with strong fields required to drive the observed mass loss close to the star. For a typical stellar jet the crossover point inside which velocity perturbations of 30 - 40 km/s no longer produce shocks is ~ 300 AU from the source

On the structure and stability of magnetic tower jets

Modern theoretical models of astrophysical jets combine accretion, rotation, and magnetic fields to launch and collimate supersonic flows from a central source. Near the source, magnetic field strengths must be large enough to collimate the jet requiring that the Poynting flux exceeds the kinetic-energy flux. The extent to which the Poynting flux dominates kinetic energy flux at large distances from the engine distinguishes two classes of models. In magneto-centrifugal launch (MCL) models, magnetic fields dominate only at scales $\lesssim 100$ engine radii, after which the jets become hydrodynamically dominated (HD). By contrast, in Poynting flux dominated (PFD) magnetic tower models, the field dominates even out to much larger scales. To compare the large distance propagation differences of these two paradigms, we perform 3-D ideal MHD AMR simulations of both HD and PFD stellar jets formed via the same energy flux. We also compare how thermal energy losses and rotation of the jet base affects the stability in these jets. For the conditions described, we show that PFD and HD exhibit observationally distinguishable features: PFD jets are lighter, slower, and less stable than HD jets. Unlike HD jets, PFD jets develop current-driven instabilities that are exacerbated as cooling and rotation increase, resulting in jets that are clumpier than those in the HD limit. Our PFD jet simulations also resemble the magnetic towers that have been recently created in laboratory astrophysical jet experiments.Comment: 16 pages, 11 figures, published in ApJ: ApJ, 757, 6

Time-Dependent MHD Shocks and Line Emission: The Case of the DG Tau Jet

The line emission from a growing number of Herbig-Haro jets can be observed and resolved at angular distances smaller than a few arcseconds from the central source. The interpretation of this emission is problematic, since the simplest model of a cooling jet cannot sustain it. It has been suggested that what one actually observes are shocked regions with a filling factor of $\sim 1$. In this framework, up to now, comparisons with observations have been based on stationary shock models. Here we introduce for the first time the self-consistent dynamics of such shocks and we show that considering their properties at different times, i.e. locations, we can reproduce observational data of the DG Tau microjet. In particular, we can interpret the spatial behavior of the [SII]6716/6731 and [NII]/[OI]6583/6300 line intensity ratios adopting a set of physical parameters that yield values of mass loss rates and magnetic fields consistent with previous estimates. We also obtain the values of the mean ionization fraction and electron density along the jet, compare these values with the ones derived from observations using the sulfur doublet to constrain the electron density (e.g. Bacciotti et al. 1995).Comment: 6 pages, 3 figure

Hubble Space Telescope Images of the Subarcsecond Jet in DG Tau

We have applied a new restoration technique to archival [O~I], H$\alpha$, and continuum HST images of DG~Tau. The restored [O~I] and H$\alpha$ images show that DG~Tau has a jet with a projected length of 25~AU and width $\leq$10~AU, and is already collimated at a projected distance of $\sim$~40~AU (0\farcs25) from the star. Such a narrow width and short collimation distance for a stellar jet places important constraints on theoretical models of jet formation.Comment: 6 pages, 3 figures included. All in postscript, please read instructions at the beginning of the file. Accepted by the Ap.J. Letter

Asymptotic Learning Curve and Renormalizable Condition in Statistical Learning Theory

Bayes statistics and statistical physics have the common mathematical structure, where the log likelihood function corresponds to the random Hamiltonian. Recently, it was discovered that the asymptotic learning curves in Bayes estimation are subject to a universal law, even if the log likelihood function can not be approximated by any quadratic form. However, it is left unknown what mathematical property ensures such a universal law. In this paper, we define a renormalizable condition of the statistical estimation problem, and show that, under such a condition, the asymptotic learning curves are ensured to be subject to the universal law, even if the true distribution is unrealizable and singular for a statistical model. Also we study a nonrenormalizable case, in which the learning curves have the different asymptotic behaviors from the universal law

From the 1990s climate change has decreased cool season catchment precipitation reducing river heights in Australia’s southern Murray-Darling Basin

The Murray-Darling Basin (MDB) is Australia's major agricultural region. The southern MDB receives most of its annual catchment runoff during the cool season (April-September). Focusing on the Murrumbidgee River measurements at Wagga Wagga and further downstream at Hay, cool season river heights are available year to year. The 27-year period April-September Hay and Wagga Wagga river heights exhibit decreases between 1965 and 1991 and 1992-2018 not matched by declining April-September catchment rainfall. However, permutation tests of means and variances of late autumn (April-May) dam catchment precipitation and net inflows, produced p-values indicating a highly significant decline since the early 1990s. Consequently, dry catchments in late autumn, even with average cool season rainfall, have reduced dam inflows and decreased river heights downstream from Wagga Wagga, before water extraction for irrigation. It is concluded that lower April-September mean river heights at Wagga Wagga and decreased river height variability at Hay, since the mid-1990s, are due to combined lower April-May catchment precipitation and increased mean temperatures. Machine learning attribute detection revealed the southern MDB drivers as the southern annular mode (SAM), inter-decadal Pacific oscillation (IPO), Indian Ocean dipole (IOD) and global sea-surface temperature (GlobalSST). Continued catchment drying and warming will drastically reduce future water availability