Gossypiboma of the abdomen

Intra-abdominal cysts may rise from a variety of organs. However, foreign-body reaction and cyst formation should be considered in the differential diagnosis. In this report, we describe the finding of a preoperatively undetected gossypiboma. A gossypiboma is a mass within the body that is composed of a cotton matrix; in this case, an unmarked laparotomy sponge. The evaluation, findings, and prevention of gossypiboma are discussed.     

Effect of cerebral ischaemia on the cerebrovascular and cardiovascular response to haemorrhage

Reports studying the combination of low blood pressure and cerebral ischaemia are few, and it remains to be determined how cerebral circulatory insufficiency modifies the cerebral perfusion and the central haemodynamic response to blood loss. We hypothesised that occlusion of arteries to the brain modifies the cerebrovascular and cardiovascular responses to blood loss. Continuous measurements of the cerebral microcirculation with laser Doppler microprobes in the cerebral cortex were performed in anaesthetised pigs during cerebral ischaemia and haemorrhagic hypotension. The response to rapid bleeding (25% of the blood volume) was recorded during normal conditions and during cerebral ischaemia induced by bilateral occlusion of the common carotid arteries. During normal conditions haemorrhage caused insignificant decreases in cerebral microcirculation. Haemorrhage during bilateral carotid artery occlusion, however, caused significantly greater changes in cerebral microcirculation and a greater posthaemorrhagic increase in cerebrovascular resistance shortly after the blood loss. Haemorrhage during bilateral carotid artery occlusion also caused greater reductions in cardiac output and arterial pressure than similar blood loss caused during normal conditions. This study showed a disproportionate decrease in cerebral blood flow with haemorrhage during bilateral carotid occlusion, caused by an immediate increase in cerebrovascular resistance. The results suggest that even a moderate blood loss in patients with impaired cerebral circulation could be dangerous, because normal compensatory mechanisms to haemorrhage are impaired.     

Bio‐inspired algorithms for the optimization of offshore oil production systems

This work describes the development, implementation, and assessment of enhanced variants of three different groups of bio‐inspired methodologies: genetic algorithms, particle swarm optimization, and artificial immune system. The algorithms are implemented on a computational tool for the synthesis and optimization of offshore oil production risers that connect a floating platform at the sea surface to the wellheads at the sea bottom. Optimization procedures using bio‐inspired algorithms for such real‐world engineering problems require the calculation of the objective function through a large number of time‐consuming finite element nonlinear dynamic analyses, for the evaluation of the structural behavior of each candidate configuration. Therefore, the performance of the algorithms may be measured by the smaller number of objective function evaluations associated to a given target fitness value. The results indicate that the artificial immune system approach, incorporating some enhancements presented in this work, is more effective than the genetic algorithms and particle swarm optimization methods, requiring a smaller number of evaluations to obtain better solutions. Copyright © 2012 John Wiley & Sons, Ltd.     

A quadratic buck converter with lossless commutation

High switching frequency associated with soft commutation techniques is a new trend in switching converters. Following this trend, the authors present a buck pulsewidth modulation converter, where the DC voltage conversion ratio has a quadratic dependence on duty cycle, providing a large step-down. By introducing two resonant networks, soft switching is attained, providing highly efficient operating conditions for a wide load range at high switching frequency. Contrary to most of the converters that apply soft-switching techniques, the switches presented are not subjected to high switch voltage or current stresses and, consequently, present low conduction losses. The authors present, for this converter, the principle of operation, theoretical analysis, relevant equations and simulation and experimental results     

The segmentation of the left ventricle of the heart from ultrasound data using deep learning architectures and derivative-based search methods

We present a new supervised learning model designed for the automatic segmentation of the left ventricle (LV) of the heart in ultrasound images. We address the following problems inherent to supervised learning models: 1) the need of a large set of training images; 2) robustness to imaging conditions not present in the training data; and 3) complex search process. The innovations of our approach reside in a formulation that decouples the rigid and nonrigid detections, deep learning methods that model the appearance of the LV, and efficient derivative-based search algorithms. The functionality of our approach is evaluated using a data set of diseased cases containing 400 annotated images (from 12 sequences) and another data set of normal cases comprising 80 annotated images (from two sequences), where both sets present long axis views of the LV. Using several error measures to compute the degree of similarity between the manual and automatic segmentations, we show that our method not only has high sensitivity and specificity but also presents variations with respect to a gold standard (computed from the manual annotations of two experts) within interuser variability on a subset of the diseased cases. We also compare the segmentations produced by our approach and by two state-of-the-art LV segmentation models on the data set of normal cases, and the results show that our approach produces segmentations that are comparable to these two approaches using only 20 training images and increasing the training set to 400 images causes our approach to be generally more accurate. Finally, we show that efficient search methods reduce up to tenfold the complexity of the method while still producing competitive segmentations. In the future, we plan to include a dynamical model to improve the performance of the algorithm, to use semisupervised learning methods to reduce even more the dependence on rich and large training sets, and to design a shape model less dependent on the training set.