We have used wide-field ultraviolet, optical, and far-infrared photometric images of Pleiades reflection nebulosity to analyze dust properties and the three-dimensional nebular geometry. Scattered light data were taken from 1650 and 2200 Å Wide-Field Imaging Survey Polarimeter images and a large 4400 Å mosaic of Burrell Schmidt CCD frames. Dust thermal emission maps were extracted from IRAS data. The scattering geometry analysis is complicated by the blending of light from many stars and the likely presence of more than one scattering layer. Despite these complications, we conclude that most of the scattered light comes from dust in front of the stars in at least two scattering layers, one far in front and extensive, the other nearer the stars and confined to areas of heavy nebulosity. The first layer can be approximated as an optically thin, foreground slab whose line-of-sight separation from the stars averages ~0.7 pc. The second layer is also optically thin in most locations and may lie at less than half the separation of the first layer, perhaps with some material among or behind the stars. The association of nebulosities peripheral to the main condensation around the brightest stars is not clear. Models with standard grain properties cannot account for the faintness of the scattered UV light relative to the optical. Some combination of significant changes in grain model albedo and phase function asymmetry values is required. Our best-performing model has a UV albedo of 0.22 ± 0.07 and a scattering asymmetry of 0.74 ± 0.06. Hypothetical optically thick dust clumps missed by interstellar sight line measurements have little effect on the nebular colors but might shift the interpretation of our derived scattering properties from individual grains to the bulk medium.
We present CO, H2, H i and HISA (H i self-absorption) distributions from a set of simulations of grand design spirals including stellar feedback, self-gravity, heating and cooling. We replicate the emission of the second galactic quadrant by placing the observer inside the modelled galaxies and post-process the simulations using a radiative transfer code, so as to create synthetic observations. We compare the synthetic data cubes to observations of the second quadrant of the Milky Way to test the ability of the current models to reproduce the basic chemistry of the Galactic interstellar medium (ISM), as well as to test how sensitive such galaxy models are to different recipes of chemistry and/or feedback. We find that models which include feedback and self-gravity can reproduce the production of CO with respect to H2 as observed in our Galaxy, as well as the distribution of the material perpendicular to the Galactic plane. While changes in the chemistry/feedback recipes do not have a huge impact on the statistical properties of the chemistry in the simulated galaxies, we find that the inclusion of both feedback and self-gravity are crucial ingredients, as our test without feedback failed to reproduce all of the observables. Finally, even though the transition from H2 to CO seems to be robust, we find that all models seem to underproduce molecular gas, and have a lower molecular to atomic gas fraction than is observed. Nevertheless, our fiducial model with feedback and self-gravity has shown to be robust in reproducing the statistical properties of the basic molecular gas components of the ISM in our Galaxy.
In this review, I consider a number of basic questions about cold Galactic H i: What are the physical properties of the cold atomic phase, and how are they maintained? How much mass is there in the cold H i and in the other phases in the disk? Are the true populations of H2, cold H i, and warm H i distinctly separated from each other in temperature-density space, or do some phase properties overlap? What is the geometric structure of cold interstellar clouds? What is the phase structure? What brings these structures about? What is the role of cold H i in the phase dynamics of the interstellar medium, in star formation, and in the evolution of the Galaxy as a whole? Some of these questions have more complete answers than others at the present time. I conclude with a few comments on the directions of current research and desirable future observations.
The aim of this study was to replicate, in patients with multiple sclerosis (MS), the three-cluster cognitive-behavioral classification proposed by Turk and Rudy. Sixty-two patients attending a tertiary MS rehabilitation center completed the Pain Impact Rating questionnaire measuring activity interference, pain intensity, social support, and emotional distress. The General Health Questionnaire-28 and the Multiple Sclerosis Impact Scale-29 assessed disability and restriction in participation. Cluster analysis classified patients into three cognitive-behavioral groups (40.4%, 'adaptive copers'; 36.5%, 'dysfunctional'; and 23.1%, 'interpersonally distressed'). Patients in groups with higher levels of activity interference, emotional distress due to pain, and lower perceived levels of social support had significantly higher levels of depression on the General Health Questionnaire-28 (P<0.003), and reported a greater impact on their physical and psychological functioning (P<0.001) on Multiple Sclerosis Impact Scale-29 subscales. Possible cut-points were identified to aid clinicians in classifying patients into clusters for individualized treatment. More research is needed to improve the understanding of pain and the potential use of cognitive-behavioral clusters in patients with MS. These may be useful in the development of tailored early intervention, which may reduce pain-related disability and contribute to patient's overall well being.
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