Deep Learning Algorithms Improve Automated Identification of Chagas Disease Vectors

The elegant mechanisms by which naturally occurring selfish genetic elements, such as transposable elements, meiotic drive genes, homing endonuclease genes and Wolbachia, spread at the expense of their hosts provide some of the most fascinating and remarkable subjects in evolutionary genetics. These elements also have enormous untapped potential to be used in the control of some of the world's most devastating diseases. Effective gene drive systems for spreading genes that can block the transmission of insect-borne pathogens are much needed. Here we explore the potential of natural gene drive systems and discuss the artificial constructs that could be envisaged for this purpose.

Vector-borne diseases are a global problem--a trend that may only increase if global temperature rises and demographic trends continue--and their economic and social impact are enormous. Insecticides play a vital role in the fight against these diseases by controlling the vectors themselves in order to improve public health; however, resistance to commonly used insecticides is on the rise. This perspective outlines the major classes of disease vector control agents and the mechanisms of resistance that have evolved, arguing that effective resistance management strategies must carefully monitor resistance in field populations and use combinations of the limited modes of action available to best effect. Moreover, the development of novel insecticide classes for control of adult mosquitoes and other vectors becomes increasingly important. Copyright (c) 2007 Society of Chemical Industry.

Artificial neural networks now are used in many fields. They have become well established as viable, multipurpose, robust computational methodologies with solid theoretic support and with strong potential to be effective in any discipline, especially medicine. For example, neural networks can extract new medical information from raw data, build computer models that are useful for medical decision-making, and aid in the distribution of medical expertise. Because many important neural network applications currently are emerging, the authors have prepared this article to bring a clearer understanding of these biologically inspired computing paradigms to anyone interested in exploring their use in medicine. They discuss the historical development of neural networks and provide the basic operational mathematics for the popular multilayered perceptron. The authors also describe good training, validation, and testing techniques, and discuss measurements of performance and reliability, including the use of bootstrap methods to obtain confidence intervals. Because it is possible to predict outcomes for individual patients with a neural network, the authors discuss the paradigm shift that is taking place from previous "bin-model" approaches, in which patient outcome and management is assumed from the statistical groups in which the patient fits. The authors explain that with neural networks it is possible to mediate predictions for individual patients with prevalence and misclassification cost considerations using receiver operating characteristic methodology. The authors illustrate their findings with examples that include prostate carcinoma detection, coronary heart disease risk prediction, and medication dosing. The authors identify and discuss obstacles to success, including the need for expanded databases and the need to establish multidisciplinary teams. The authors believe that these obstacles can be overcome and that neural networks have a very important role in future medical decision support and the patient management systems employed in routine medical practice. Copyright 2001 American Cancer Society.