Gynaecological operations: do they improve sexual life?

Aim of the study was to determine if gynaecological operations have an effect on sexual function using the current medlined literature. We performed a Medline search using the terms "sexual life/function after operative gynaecological treatment", "sexual life/function after operations for gynaecological problems", "sexual life/function after hysterectomy", "sexual life/function, incontinence" and "sexual life/function, pelvic organ prolapse". Reviews were excluded. We divided the operations into four groups of (1) combined prolapse and incontinence operations, (2) prolapse operations only, (3) incontinence operations only and (4) hysterectomy and compared pre-to postoperative sexual outcome. Thirty-six articles including 4534 patients were identified. Only 13 studies used a validated questionnaire. The other authors used self-designed and non-validated questionnaires or orally posed questions by the examiner to determine sexual function. Prolapse operations particularly posterior repair using levator plication seem to deteriorate sexual function, incontinence procedure have some worsening effect on sexual function and hysterectomy seems to improve sexual function with no differences between subtotal or total hysterectomy. Gynaecological operations do influence sexual function. However, little validated data are available to come to this conclusion.

A graph theoretic approach to testing associations between disparate sources of functional genomic data

The last few years have seen the advent of high-throughput technologies to analyze various properties of the transcriptome and proteome of several organisms. The congruency of these different data sources, or lack thereof, can shed light on the mechanisms that govern cellular function. A central challenge for bioinformatics research is to develop a unified framework for combining the multiple sources of functional genomics information and testing associations between them, thus obtaining a robust and integrated view of the underlying biology. We present a graph theoretic approach to test the significance of the association between multiple disparate sources of functional genomics data by proposing two statistical tests, namely edge permutation and node label permutation tests. We demonstrate the use of the proposed tests by finding significant association between a Gene Ontology-derived "predictome" and data obtained from mRNA expression and phenotypic experiments for Saccharomyces cerevisiae. Moreover, we employ the graph theoretic framework to recast a surprising discrepancy presented in Giaever et al. (2002) between gene expression and knockout phenotype, using expression data from a different set of experiments.

Applied Graph Theory in Computer Vision and Pattern Recognition

This book will serve as a foundation for a variety of useful applications of graph theory to computer vision, pattern recognition, and related areas. It covers a representative set of novel graph-theoretic methods for complex computer vision and pattern recognition tasks. The first part of the book presents the application of graph theory to low-level processing of digital images such as a new method for partitioning a given image into a hierarchy of homogeneous areas using graph pyramids, or a study of the relationship between graph theory and digital topology. Part II presents graph-theoretic learning algorithms for high-level computer vision and pattern recognition applications, including a survey of graph based methodologies for pattern recognition and computer vision, a presentation of a series of computationally efficient algorithms for testing graph isomorphism and related graph matching tasks in pattern recognition and a new graph distance measure to be used for solving graph matching problems. Finally, Part III provides detailed descriptions of several applications of graph-based methods to real-world pattern recognition tasks. It includes a critical review of the main graph-based and structural methods for fingerprint classification, a new method to visualize time series of graphs, and potential applications in computer network monitoring and abnormal event detection.