Imaging the spectral reflectance properties of bipolar radiofrequency-fused bowel tissue

Delivery of radiofrequency (RF) electrical energy is used during surgery to heat and seal tissue, such as vessels, allowing resection without blood loss. Recent work has suggested that this approach may be extended to allow surgical attachment of larger tissue segments for applications such as bowel anastomosis. In a large series of porcine surgical procedures bipolar RF energy was used to resect and re-seal the small bowel in vivo with a commercial tissue fusion device (Ligasure; Covidien PLC, USA). The tissue was then imaged with a multispectral imaging laparoscope to obtain a spectral datacube comprising both fused and healthy tissue. Maps of blood volume, oxygen saturation and scattering power were derived from the measured reflectance spectra using an optimised light-tissue interaction model. A 60% increase in reflectance of visible light (460-700 nm) was observed after fusion, with the tissue taking on a white appearance. Despite this the distinctive shape of the haemoglobin absorption spectrum was still noticeable in the 460-600 nm wavelength range. Scattering power increased in the fused region in comparison to normal serosa, while blood volume and oxygen saturation decreased. Observed fusion-induced changes in the reflectance spectrum are consistent with the biophysical changes induced through tissue denaturation and increased collagen cross-linking. The multispectral imager allows mapping of the spatial extent of these changes and classification of the zone of damaged tissue. Further analysis of the spectral data in parallel with histopathological examination of excised specimens will allow correlation of the optical property changes with microscopic alterations in tissue structure. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only

Capacitated Center Problems with Two-Sided Bounds and Outliers

In recent years, the capacitated center problems have attracted a lot of research interest. Given a set of vertices $V$, we want to find a subset of vertices $S$, called centers, such that the maximum cluster radius is minimized. Moreover, each center in $S$ should satisfy some capacity constraint, which could be an upper or lower bound on the number of vertices it can serve. Capacitated $k$-center problems with one-sided bounds (upper or lower) have been well studied in previous work, and a constant factor approximation was obtained. We are the first to study the capacitated center problem with both capacity lower and upper bounds (with or without outliers). We assume each vertex has a uniform lower bound and a non-uniform upper bound. For the case of opening exactly $k$ centers, we note that a generalization of a recent LP approach can achieve constant factor approximation algorithms for our problems. Our main contribution is a simple combinatorial algorithm for the case where there is no cardinality constraint on the number of open centers. Our combinatorial algorithm is simpler and achieves better constant approximation factor compared to the LP approach

Statistical Signatures of Photon Localization

The realization that electron localization in disordered systems (Anderson localization) is ultimately a wave phenomenon has led to the suggestion that photons could be similarly localized by disorder. This conjecture attracted wide interest because the differences between photons and electrons - in their interactions, spin statistics, and methods of injection and detection - may open a new realm of optical and microwave phenomena, and allow a detailed study of the Anderson localization transition undisturbed by the Coulomb interaction. To date, claims of three-dimensional photon localization have been based on observations of the exponential decay of the electromagnetic wave as it propagates through the disordered medium. But these reports have come under close scrutiny because of the possibility that the decay observed may be due to residual absorption, and because absorption itself may suppress localization. Here we show that the extent of photon localization can be determined by a different approach - measurement of the relative size of fluctuations of certain transmission quantities. The variance of relative fluctuations accurately reflects the extent of localization, even in the presence of absorption. Using this approach, we demonstrate photon localization in both weakly and strongly scattering quasi-one-dimensional dielectric samples and in periodic metallic wire meshes containing metallic scatterers, while ruling it out in three-dimensional mixtures of aluminum spheres.Comment: 5 pages, including 4 figure

Raman spectroscopic evidence of tissue restructuring in heat-induced tissue fusion

Heat-induced tissue fusion via radio-frequency (RF) energy has gained wide acceptance clinically and here we present the first optical-Raman-spectroscopy study on tissue fusion samples in vitro. This study provides direct insights into tissue constituent and structural changes on the molecular level, exposing spectroscopic evidence for the loss of distinct collagen fibre rich tissue layers as well as the denaturing and restructuring of collagen crosslinks post RF fusion. These findings open the door for more advanced optical feedback-control methods and characterization during heat-induced tissue fusion, which will lead to new clinical applications of this promising technology. (© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery – Part I: model planning

<p>Abstract</p> <p>Background</p> <p>Different methods have recently been proposed for predicting morbidity in intensive care units (ICU). The aim of the present study was to critically review a number of approaches for developing models capable of estimating the probability of morbidity in ICU after heart surgery. The study is divided into two parts. In this first part, popular models used to estimate the probability of class membership are grouped into distinct categories according to their underlying mathematical principles. Modelling techniques and intrinsic strengths and weaknesses of each model are analysed and discussed from a theoretical point of view, in consideration of clinical applications.</p> <p>Methods</p> <p>Models based on Bayes rule, <it>k-</it>nearest neighbour algorithm, logistic regression, scoring systems and artificial neural networks are investigated. Key issues for model design are described. The mathematical treatment of some aspects of model structure is also included for readers interested in developing models, though a full understanding of mathematical relationships is not necessary if the reader is only interested in perceiving the practical meaning of model assumptions, weaknesses and strengths from a user point of view.</p> <p>Results</p> <p>Scoring systems are very attractive due to their simplicity of use, although this may undermine their predictive capacity. Logistic regression models are trustworthy tools, although they suffer from the principal limitations of most regression procedures. Bayesian models seem to be a good compromise between complexity and predictive performance, but model recalibration is generally necessary. <it>k</it>-nearest neighbour may be a valid non parametric technique, though computational cost and the need for large data storage are major weaknesses of this approach. Artificial neural networks have intrinsic advantages with respect to common statistical models, though the training process may be problematical.</p> <p>Conclusion</p> <p>Knowledge of model assumptions and the theoretical strengths and weaknesses of different approaches are fundamental for designing models for estimating the probability of morbidity after heart surgery. However, a rational choice also requires evaluation and comparison of actual performances of locally-developed competitive models in the clinical scenario to obtain satisfactory agreement between local needs and model response. In the second part of this study the above predictive models will therefore be tested on real data acquired in a specialized ICU.</p

Genome-Wide Association Analysis of Oxidative Stress Resistance in Drosophila melanogaster

Background: Aerobic organisms are susceptible to damage by reactive oxygen species. Oxidative stress resistance is a quantitative trait with population variation attributable to the interplay between genetic and environmental factors. Drosophila melanogaster provides an ideal system to study the genetics of variation for resistance to oxidative stress. Methods and Findings: We used 167 wild-derived inbred lines of the Drosophila Genetic Reference Panel for a genomewide association study of acute oxidative stress resistance to two oxidizing agents, paraquat and menadione sodium bisulfite. We found significant genetic variation for both stressors. Single nucleotide polymorphisms (SNPs) associated with variation in oxidative stress resistance were often sex-specific and agent-dependent, with a small subset common for both sexes or treatments. Associated SNPs had moderately large effects, with an inverse relationship between effect size and allele frequency. Linear models with up to 12 SNPs explained 67–79 % and 56–66 % of the phenotypic variance for resistance to paraquat and menadione sodium bisulfite, respectively. Many genes implicated were novel with no known role in oxidative stress resistance. Bioinformatics analyses revealed a cellular network comprising DNA metabolism and neuronal development, consistent with targets of oxidative stress-inducing agents. We confirmed associations of seven candidate genes associated with natural variation in oxidative stress resistance through mutational analysis. Conclusions: We identified novel candidate genes associated with variation in resistance to oxidative stress that hav