Cloud fluid models of gas dynamics and star formation in galaxies

The large dynamic range of star formation in galaxies, and the apparently complex environmental influences involved in triggering or suppressing star formation, challenges the understanding. The key to this understanding may be the detailed study of simple physical models for the dominant nonlinear interactions in interstellar cloud systems. One such model is described, a generalized Oort model cloud fluid, and two simple applications of it are explored. The first of these is the relaxation of an isolated volume of cloud fluid following a disturbance. Though very idealized, this closed box study suggests a physical mechanism for starbursts, which is based on the approximate commensurability of massive cloud lifetimes and cloud collisional growth times. The second application is to the modeling of colliding ring galaxies. In this case, the driving processes operating on a dynamical timescale interact with the local cloud processes operating on the above timescale. The results is a variety of interesting nonequilibrium behaviors, including spatial variations of star formation that do not depend monotonically on gas density

CO Observations of the Interacting Galaxy Pair NGC 5394/95

Peer reviewe

Spitzer Space Telescope IRAC and MIPS observations of the interacting galaxies Ic 2163 and NGC 2207: clumpy emission

Peer reviewe

Observations and models of star formation in the tidal features of interacting galaxies

Multi-color surface photometry (BVri) is presented for the tidal features in a sample of interacting galaxies. Large color variations are found between the morphological components and within the individual components. The blue colors in the primary and the tidal features are most dramatic in B-V, and not in V-i, indicating that star formation instead of metallicity or age dominates the colors. Color variations between components is larger in systems shortly after interaction begins and diminishes to a very low level in systems which are merged. Photometric models for interacting systems are presented which suggest that a weak burst of star formation in the tidal features could cause the observed color distributions. Dynamical models indicate that compression occurs during the development of tidal features causing an increase in the local density by a factor of between 1.5 and 5. Assuming this density increase can be related to the star formation rate by a Schmidt law, the density increases observed in the dynamical models may be responsible for the variations in color seen in some of the interacting systems. Limitations of the dynamical models are also discussed

Circulating Biologically Active Adrenomedullin Predicts Organ Failure and Mortality in Sepsis

BACKGROUND: Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. Biologically active adrenomedullin (bio-ADM) is an emerging biomarker for sepsis. We explored whether bio-ADM concentration could predict severity, organ failure, and 30-day mortality in septic patients. METHODS: In 215 septic patients (109 patients with sepsis; 106 patients with septic shock), bio-ADM concentration was measured at diagnosis of sepsis, using sphingotest bio-ADM (Sphingotec GmbH, Hennigsdorf, Germany) and analyzed in terms of sepsis severity, vasopressor use, and 30-day mortality. The number of organ failures, sequential (sepsis-related) organ failure assessment (SOFA) score, and 30-day mortality were compared according to bio-ADM quartiles. RESULTS: Bio-ADM concentration was significantly higher in patients with septic shock, vasopressor use, and non-survivors than in patients with solitary sepsis, no vasopressor use, and survivors, respectively (all P<0.0001). Bio-ADM quartiles were associated with the number of organ failures (P<0.0001), as well as SOFA cardiovascular, renal, coagulation, and liver subscores (all P<0.05). The 30-day mortality rate showed a stepwise increase in each bio-ADM quartile (all P<0.0001). Bio-ADM concentration and SOFA score equally predicted the 30-day mortality (area under the curve: 0.827 vs 0.830). CONCLUSIONS: Bio-ADM could serve as a useful and objective biomarker to predict severity, organ failure, and 30-day mortality in septic patients