BackgroundMultiple independent culture-based studies have identified the presence of Pseudomonas aeruginosa in respiratory samples as a positive risk factor for bronchiolitis obliterans syndrome (BOS). Yet, culture-independent microbiological techniques have identified a negative association between Pseudomonas species and BOS. Our objective was to investigate whether there may be a unifying explanation for these apparently dichotomous results.MethodsWe performed bronchoscopies with bronchoalveolar lavage (BAL) on lung transplant recipients (46 procedures in 33 patients) and 26 non-transplant control subjects. We analyzed bacterial communities in the BAL fluid using qPCR and pyrosequencing of 16S rRNA gene amplicons and compared the culture-independent data with the clinical metadata and culture results from these subjects.FindingsRoute of bronchoscopy (via nose or via mouth) was not associated with changes in BAL microbiota (p = 0.90). Among the subjects with positive Pseudomonas bacterial culture, P. aeruginosa was also identified by culture-independent methods. In contrast, a distinct Pseudomonas species, P. fluorescens, was often identified in asymptomatic transplant subjects by pyrosequencing but not detected via standard bacterial culture. The subject populations harboring these two distinct pseudomonads differed significantly with respect to associated symptoms, BAL neutrophilia, bacterial DNA burden and microbial diversity. Despite notable differences in culturability, a global database search of UM Hospital Clinical Microbiology Laboratory records indicated that P. fluorescens is commonly isolated from respiratory specimens.InterpretationWe have reported for the first time that two prominent and distinct Pseudomonas species (P. fluorescens and P. aeruginosa) exist within the post-transplant lung microbiome, each with unique genomic and microbiologic features and widely divergent clinical associations, including presence during acute infection.
In this experiment older and younger adults were compared on their ability to position a cursor with an electromechanical mouse. Distance of the movement, size of the target, and relative emphasis on the speed or accuracy of the movement were manipulated. The study was designed to isolate and evaluate the effects of age-related differences in the noise-to-force ratio, perceptual feedback efficiency, strategy differences, and the ability to produce force as explanations for age-related differences in movement control. This was done by using two types of movement tasks and by analyzing movement performance according to stages of movement. The study showed that all four factors, when isolated, are significantly different for the two age groups. However, in the task component where all factors could simultaneously affect performance, the age-related difference in performance was less than the difference in either the measure of noise-to-force ratio or perceptual efficiency. Analysis of the submovement structure revealed how older adults compensated for the greater noise and less perceptual efficiency by adjusting the velocity and number of submovements. These findings are discussed in light of the optimized submovement model.
A Classification of Visual Representations hy do we often prefer glancing at a graph to studying a table of numbers? What might be a better graphic than either a graph or table for seeing bow a biological process unfolds with time? To begin to answer these kinds of questions we examine the cognitive structure of graphics and report a strtictural classification of visual representations. McCormick, DeFanti, and Brown [16] define visualization as "the study of mechanisms in computers and in humans which allow them in concert to perceive, use, and commtinicate visual information." Thus, visualization includes the study of both image synthesis and image understanding. Given this broad focus, it is not stiiprising that visualization spans many academic di.sciplines, scientific fields, and multiple domains of inquiry. However, if visualization is to continue to advance as an interdisciplinary science, it must become more than a grab bag of techniques for displaying data. Our research focuses on classifying visual information. Classification lies at the heart of every scientific field. C^llassifications structure domains of systematic inquiry and provide concepts for developing theories to identify anomalies and to predict future research needs.Extant taxonomies of graphs and images can be characterized as either ñincüonal or structural. Functional taxonomies focus on the intended use and purpose of the graphic material. For example, consider the functional classification developed by . One of the maiti categories is technical diagrams used for maintaining, operating, and troubleshooting complex equipment. Other examples of functional classifications can be found in Tiifte [22]. A functional classification does not reflect the physical structure of images, nor is it intended to correspond to an underlying representation in memory [ 1 ].In contrast, structural categories are well learned and are derived from exemplar learning. They focus on the form of the image rather than its content. Rankin [18] and Bertin [2] developed such structural categories of graphs. Rankin used the number of dimensions and graph forms to determine bis clas-36 December 1994/Vol.37, No.12 < © ACM 0002-0782/94/1200 $3.50 COMMUNICATIONS OP TNH
We present a careful evaluation of the sensory characteristics of the CyberGlove model CG1801 whole-hand input device. In particular, we conducted an experimental study that investigated the level of sensitivity of the sensors, their performance in recognizing angles, and factors that affected accuracy of recognition of flexion measurements. Among our results, we show that hand size differences among the subjects of the study did not have a statistical effect on the accuracy of the device. We also analyzed the effect of different software calibration approaches on accuracy of the sensors.
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