When good bugs go bad: Epidemiology and antimicrobial resistance profiles of Corynebacterium striatum, an emerging multidrug-resistant, opportunistic pathogen

ABSTRACT Infections with Corynebacterium striatum have been described in the literature over the last 2 decades, with the majority being bacteremia, central line infections, and occasionally, endocarditis. In recent years, the frequency of C. striatum infections appears to be increasing; a factor likely contributing to this is the increased ease and accuracy of the identification of Corynebacterium spp., including C. striatum , from clinical cultures. The objective of this study was to retrospectively characterize C. striatum isolates recovered from specimens submitted as part of routine patient care at a 1,250-bed, tertiary-care academic medical center. Multiple strain types were recovered, as demonstrated by repetitive-sequence-based PCR. Most of the strains of C. striatum characterized were resistant to antimicrobials commonly used to treat Gram-positive organisms, such as penicillin, ceftriaxone, meropenem, clindamycin, and tetracycline. The MIC 50 for ceftaroline was &gt;32 μg/ml. Although there are no interpretive criteria for susceptibility with telavancin, it appeared to have potent in vitro efficacy against this species, with MIC 50 and MIC 90 values of 0.064 and 0.125 μg/ml, respectively. Finally, as previously reported in case studies, we demonstrated rapid in vitro development of daptomycin resistance in 100% of the isolates tested ( n = 50), indicating that caution should be exhibited when using daptomycin for the treatment of C. striatum infections. C. striatum is an emerging, multidrug-resistant pathogen that can be associated with a variety of infection types. </jats:p

Improved cloud-phase determination of low-level liquid and mixed-phase clouds by enhanced polarimetric lidar

The unambiguous retrieval of cloud phase from polarimetric lidar observations is dependent on the assumption that only cloud scattering processes affect polarization measurements. A systematic bias of the traditional lidar depolarization ratio can occur due to a lidar system's inability to accurately measure the entire backscattered signal dynamic range, and these biases are not always identifiable in traditional polarimetric lidar systems. This results in a misidentification of liquid water in clouds as ice, which has broad implications on evaluating surface energy budgets. The Clouds Aerosol Polarization and Backscatter Lidar at Summit, Greenland employs multiple planes of linear polarization, and photon counting and analog detection schemes, to self evaluate, correct, and optimize signal combinations to improve cloud classification. Using novel measurements of diattenuation that are sensitive to both horizontally oriented ice crystals and counting system nonlinear effects, unambiguous measurements are possible by over constraining polarization measurements. This overdetermined capability for cloud-phase determination allows for system errors to be identified and quantified in terms of their impact on cloud properties. It is shown that lidar system dynamic range effects can cause errors in cloud-phase fractional occurrence estimates on the order of 30 % causing errors in attribution of cloud radiative effects on the order of 10–30 %. This paper presents a method to identify and remove lidar system effects from atmospheric polarization measurements and uses co-located sensors at Summit to evaluate this method. Enhanced measurements are achieved in this work with non-orthogonal polarization retrievals as well as analog and photon counting detection facilitating a more complete attribution of radiative effects linked to cloud properties

In-flight calibration of the Herschel-SPIRE instrument

SPIRE, the Spectral and Photometric Imaging REceiver, is the Herschel Space Observatory's submillimetre camera and spectrometer. It contains a three-band imaging photometer operating at 250, 350 and 500 μm, and an imaging Fourier-transform spectrometer (FTS) covering 194–671 μm (447-1550 GHz). In this paper we describe the initial approach taken to the absolute calibration of the SPIRE instrument using a combination of the emission from the Herschel telescope itself and the modelled continuum emission from solar system objects and other astronomical targets. We present the photometric, spectroscopic and spatial accuracy that is obtainable in data processed through the “standard” pipelines. The overall photometric accuracy at this stage of the mission is estimated as 15% for the photometer and between 15 and 50% for the spectrometer. However, there remain issues with the photometric accuracy of the spectra of low flux sources in the longest wavelength part of the SPIRE spectrometer band. The spectrometer wavelength accuracy is determined to be better than 1/10th of the line FWHM. The astrometric accuracy in SPIRE maps is found to be 2 arcsec when the latest calibration data are used. The photometric calibration of the SPIRE instrument is currently determined by a combination of uncertainties in the model spectra of the astronomical standards and the data processing methods employed for map and spectrum calibration. Improvements in processing techniques and a better understanding of the instrument performance will lead to the final calibration accuracy of SPIRE being determined only by uncertainties in the models of astronomical standards

HerMES: The submillimeter spectral energy distributions of Herschel/SPIRE-detected galaxies

We present colours of sources detected with the Herschel/SPIRE instrument in deep extragalactic surveys of the Lockman Hole, Spitzer-FLS, and GOODS-N fields in three photometric bands at 250, 350 and 500 μm. We compare these with expectations from the literature and discuss associated uncertainties and biases in the SPIRE data. We identify a 500 μm flux limited selection of sources from the HerMES point source catalogue that appears free from neighbouring/blended sources in all three SPIRE bands. We compare the colours with redshift tracks of various contemporary models. Based on these spectral templates we show that regions corresponding to specific population types and redshifts can be identified better in colour-flux space. The redshift tracks as well as the colour-flux plots imply a majority of detected objects with redshifts at 1 < z < 3.5, somewhat depending on the group of model SEDs used. We also find that a population of sources with S_(250)/S_(350) < 0.8 at fluxes above 50 mJy as observed by SPIRE are not well represented by contemporary models and could consist of a mix of cold and lensed galaxies

HerMES: deep galaxy number counts from a P(D) fluctuation analysis of SPIRE Science Demonstration Phase observations

Dusty, star-forming galaxies contribute to a bright, currently unresolved cosmic far-infrared background. Deep Herschel-Spectral and Photometric Imaging Receiver (SPIRE) images designed to detect and characterize the galaxies that comprise this background are highly confused, such that the bulk lies below the classical confusion limit. We analyse three fields from the Herschel Multi-tiered Extragalactic Survey (HerMES) programme in all three SPIRE bands (250, 350 and 500 μm); parametrized galaxy number count models are derived to a depth of ~2 mJy beam^(−1), approximately four times the depth of previous analyses at these wavelengths, using a probability of deflection [P(D)] approach for comparison to theoretical number count models. Our fits account for 64, 60 and 43 per cent of the far-infrared background in the three bands. The number counts are consistent with those based on individually detected SPIRE sources, but generally inconsistent with most galaxy number count models, which generically overpredict the number of bright galaxies and are not as steep as the P(D)-derived number counts. Clear evidence is found for a break in the slope of the differential number counts at low flux densities. Systematic effects in the P(D) analysis are explored. We find that the effects of clustering have a small impact on the data, and the largest identified systematic error arises from uncertainties in the SPIRE beam