Fenoldopam improves renal hemodynamics impaired by positive end-expiratory pressure

BACKGROUND: Mechanical ventilation with positive end-expiratory pressure (PEEP) can impair renal hemodynamics. Fenoldopam, a dopamine receptor agonist, has been shown, in animal experiments, to improve renal perfusion. The purpose of the current study was to examine the effects of this agent on altered renal hemodynamics secondary to positive pressure ventilation. METHODS: Twelve patients requiring mechanical ventilation of their lungs and PEEP for the treatment of hypoxemia after multiple trauma or visceral surgery were studied. Hemodynamic variables, renal vascular resistance, urine flow, creatinine, inulin and PAH clearance, and excretion of sodium and potassium (NaE and KE) were measured before and after introduction of a level of PEEP high enough to decrease urine flow rate by 25% or more, and after administration of intravenous fenoldopam. RESULTS: No hemodynamic effect resulted from 0.1 microgram.kg-1.min-1, but 0.2 micrograms.kg-1.min-1 fenoldopam decreased both diastolic and mean arterial blood pressure from 66 +/- 37 (mean +/- SEM) to 57 +/- 21 mmHg, and from 83 +/- 3 to 74 +/- 4 mmHg, respectively. Renal vascular resistance was reduced from 54 +/- 12 to 19 +/- 5 dynes.s.cm-5 at 0.2 micrograms.kg-1.min-1. Fenoldopam produced a dose-related increase in renal blood flow and PAH clearance. With 0.2 micrograms.kg-1.min-1 fenoldopam, urine flow increased from 81 +/- 25 to 116 +/- 29 ml/h, NaE from 28 +/- 7 to 85 +/- 70 microM/min, and KE from 65 +/- 12 to 109 +/- 16 microM/min. CONCLUSIONS: The results of the current study indicate that intravenous fenoldopam at a dose of 0.2 micrograms.kg-1.min-1 improves renal hemodynamics and increases Na and K excretion in patients requiring mechanical ventilation of their lungs and PEEP. These effects are probably caused by an increased kidney perfusion secondary to renal artery vasodilation.     

Polymeric materials

Summary The Polymer Section of the Santa Barbara Workshop on Modeling of Materials is briefly reviewed. Motivation and need for modeling in polymer-based materials are outlined and the recommendations resulting from the workshop reported.     

Segmentation by Continuous Latent Semantic Analysis for Multi-structure Model Fitting

International Journal of Computer Vision - In this paper, we propose a novel continuous latent semantic analysis fitting method, to efficiently and effectively estimate the parameters of model...     

Prevention of deposition and agglomeration of particles in suspensions used for direct absorption of concentrated solar radiation

In direct absorption of solar radiation for industrial purposes, by micron-size graphite particles suspended in nitrogen gas, agglomeration and deposition of these particles are the major problems. This paper discusses two methods for limitation of agglomeration and deposition. The first is to create a thermal repulsion force between the suspension and the surface to keep the particles away. The second is the surface coating of the particles with a very thin silicon-base layer of molecular order.

Theoretical calculations showed that the first method required an impracticably high temperature gradient. The second method reduced the risk of deposition by about five times while negligible change occurred in the size distribution of the particles.     

Object detection by global contour shape

We present a method for object class detection in images based on global shape. A distance measure for elastic shape matching is derived, which is invariant to scale and rotation, and robust against non-parametric deformations. Starting from an over-segmentation of the image, the space of potential object boundaries is explored to find boundaries, which have high similarity with the shape template of the object class to be detected. An extensive experimental evaluation is presented. The approach achieves a remarkable detection rate of 83-91% at 0.2 false positives per image on three challenging data sets.     

Out-of-sample extrapolation of learned manifolds

We investigate the problem of extrapolating the embedding of a manifold learned from finite samples to novel out-of-sample data. We concentrate on the manifold learning method called Maximum Variance Unfolding (MVU) for which the extrapolation problem is still largely unsolved. Taking the perspective of MVU learning being equivalent to Kernel PCA, our problem reduces to extending a kernel matrix generated from an unknown kernel function to novel points. Leveraging on previous developments, we propose a novel solution which involves approximating the kernel eigenfunction using Gaussian basis functions. We also show how the width of the Gaussian can be tuned to achieve extrapolation. Experimental results which demonstrate the effectiveness of the proposed approach are also included.     

Compliance by design – Bridging the chasm between auditors and IT architects

System and process auditors assure – from an information processing perspective – the correctness and integrity of the data that is aggregated in a company’s financial statements. To do so, they assess whether a company’s business processes and information systems process financial data correctly. The audit process is a complex endeavor that in practice has to rely on simplifying assumptions. These simplifying assumptions mainly result from the need to restrict the audit scope and to focus it on the major risks. This article describes a generalized audit process. According to our experience with this process, there is a risk that material deficiencies remain undiscovered when said simplifying assumptions are not satisfied. To address this risk of deficiencies, the article compiles thirteen control patterns, which – according to our experience – are particularly suited to help information systems satisfy the simplifying assumptions. As such, use of these proven control patterns makes information systems easier to audit and IT architects can use them to build systems that meet audit requirements by design. Additionally, the practices and advice offered in this interdisciplinary article help bridge the gap between the architects and auditors of information systems and show either role how to benefit from an understanding of the other role’s terminology, techniques, and general work approach.     

Accelerated hypothesis generation for multistructure data via preference analysis

Random hypothesis generation is integral to many robust geometric model fitting techniques. Unfortunately, it is also computationally expensive, especially for higher order geometric models and heavily contaminated data. We propose a fundamentally new approach to accelerate hypothesis sampling by guiding it with information derived from residual sorting. We show that residual sorting innately encodes the probability of two points having arisen from the same model, and is obtained without recourse to domain knowledge (e.g., keypoint matching scores) typically used in previous sampling enhancement methods. More crucially, our approach encourages sampling within coherent structures and thus can very rapidly generate all-inlier minimal subsets that maximize the robust criterion. Sampling within coherent structures also affords a natural ability to handle multistructure data, a condition that is usually detrimental to other methods. The result is a sampling scheme that offers substantial speed-ups on common computer vision tasks such as homography and fundamental matrix estimation. We show on many computer vision data, especially those with multiple structures, that ours is the only method capable of retrieving satisfactory results within realistic time budgets.