Pneumonia Caused by Klebsiella spp. in 46 Horses.

BackgroundKlebsiella spp. are implicated as a common cause of bacterial pneumonia in horses, but few reports describe clinical presentation and disease progression.Hypothesis/objectivesTo describe the signalment, clinicopathologic data, radiographic and ultrasonographic findings, antimicrobial susceptibility, outcome, and pathologic lesions associated with Klebsiella spp. pneumonia in horses.AnimalsForty-six horses from which Klebsiella spp. was isolated from the lower respiratory tract.MethodsRetrospective study. Medical records from 1993 to 2013 at the William R. Pritchard Veterinary Medical Teaching Hospital, University of California, Davis were reviewed. Exact logistic regression was performed to determine if any variables were associated with survival to hospital discharge.ResultsSurvival in horses <1 year old was 73%. Overall survival in adults was 63%. For adults in which Klebsiella pneumoniae was the primary isolate, survival was 52%. Mechanical ventilation preceded development of pneumonia in 11 horses. Complications occurred in 25/46 horses, with thrombophlebitis and laminitis occurring most frequently. Multi-drug resistance was found in 47% of bacterial isolates. Variables that significantly impacted survival included hemorrhagic nasal discharge, laminitis, and thoracic radiographs with a sharp demarcation between marked caudal pulmonary alveolar infiltration and more normal-appearing caudodorsal lung.Conclusions and clinical importanceKlebsiella spp. should be considered as a differential diagnosis for horses presenting with hemorrhagic pneumonia and for horses developing pneumonia after mechanical ventilation. Multi-drug resistance is common. Prognosis for survival generally is fair, but is guarded for adult horses in which K. pneumoniae is isolated as the primary organism

Multiscale 3D Shape Analysis using Spherical Wavelets

©2005 Springer. The original publication is available at www.springerlink.com: http://dx.doi.org/10.1007/11566489_57DOI: 10.1007/11566489_57Shape priors attempt to represent biological variations within a population. When variations are global, Principal Component Analysis (PCA) can be used to learn major modes of variation, even from a limited training set. However, when significant local variations exist, PCA typically cannot represent such variations from a small training set. To address this issue, we present a novel algorithm that learns shape variations from data at multiple scales and locations using spherical wavelets and spectral graph partitioning. Our results show that when the training set is small, our algorithm significantly improves the approximation of shapes in a testing set over PCA, which tends to oversmooth data

Low-energy Selective Laser Trabeculoplasty Repeated Annually: Rationale for the COAST Trial

The recent Laser in Glaucoma and Ocular Hypertension Trial provided the evidentiary basis for a paradigm shift away from the historical medication-first approach to glaucoma--which has numerous limitations, the most important of which is poor adherence to therapy --and toward a laser-first approach. Now 20 years after its commercialization, selective laser trabeculoplasty (SLT) is routinely performed consistently with its initial description, with energy titrated to the appearance of fine, champagne-like cavitation bubbles. A recent data set suggested that lower energy SLT, applied as primary therapy and repeated annually irrespective of intraocular pressure--rather than pro re nata when its effect wanes and irrespective of intraocular pressure rises --yields longer medication-free survival than standard energy SLT repeated pro re nata. A new study--Clarifying the Optimal Application of SLT Therapy --has been initiated to explore this preliminary finding in a pair of consecutive randomized trials. Herein, we provide an evidence-based rationale for the use of low-energy SLT repeated annually as primary therapy for mild to moderate primary open-angle glaucoma or high-risk ocular hypertension

A TV-Gaussian prior for infinite-dimensional Bayesian inverse problems and its numerical implementations

Many scientific and engineering problems require to perform Bayesian inferences in function spaces, in which the unknowns are of infinite dimension. In such problems, choosing an appropriate prior distribution is an important task. In particular we consider problems where the function to infer is subject to sharp jumps which render the commonly used Gaussian measures unsuitable. On the other hand, the so-called total variation (TV) prior can only be defined in a finite dimensional setting, and does not lead to a well-defined posterior measure in function spaces. In this work we present a TV-Gaussian (TG) prior to address such problems, where the TV term is used to detect sharp jumps of the function, and the Gaussian distribution is used as a reference measure so that it results in a well-defined posterior measure in the function space. We also present an efficient Markov Chain Monte Carlo (MCMC) algorithm to draw samples from the posterior distribution of the TG prior. With numerical examples we demonstrate the performance of the TG prior and the efficiency of the proposed MCMC algorithm

Multi-resolution texture classification based on local image orientation

The aim of this paper is to evaluate quantitatively the discriminative power of the image orientation in the texture classification process. In this regard, we have evaluated the performance of two texture classification schemes where the image orientation is extracted using the partial derivatives of the Gaussian function. Since the texture descriptors are dependent on the observation scale, in this study the main emphasis is placed on the implementation of multi-resolution texture analysis schemes. The experimental results were obtained when the analysed texture descriptors were applied to standard texture databases

Maximum Likelihood Estimation in Gaussian Chain Graph Models under the Alternative Markov Property

The AMP Markov property is a recently proposed alternative Markov property for chain graphs. In the case of continuous variables with a joint multivariate Gaussian distribution, it is the AMP rather than the earlier introduced LWF Markov property that is coherent with data-generation by natural block-recursive regressions. In this paper, we show that maximum likelihood estimates in Gaussian AMP chain graph models can be obtained by combining generalized least squares and iterative proportional fitting to an iterative algorithm. In an appendix, we give useful convergence results for iterative partial maximization algorithms that apply in particular to the described algorithm.Comment: 15 pages, article will appear in Scandinavian Journal of Statistic

Thermal tides in the Martian middle atmosphere as seen by the Mars Climate Sounder

The first systematic observations of the middle atmosphere of Mars (35–80km) with the Mars Climate Sounder (MCS) show dramatic patterns of diurnal thermal variation, evident in retrievals of temperature and water ice opacity. At the time of writing, the data set of MCS limb retrievals is sufficient for spectral analysis within a limited range of latitudes and seasons. This analysis shows that these thermal variations are almost exclusively associated with a diurnal thermal tide. Using a Martian general circulation model to extend our analysis, we show that the diurnal thermal tide dominates these patterns for all latitudes and all seasons