Appointments, organization and communications /

Mode of access: Internet.

Reports and Communications in Medicine and Surgery

Mode of access: Internet.

Social Support Networks and Self-Efficacy of Peruvian Women Diagnosed with Cancer: A Biocultural Analysis of Health Behavior Constructs

Thesis (Ph.D.)--University of Washington, 2016-06

Mobile message services and communications policy

Something of a design after-thought, mobile phone SMS (Short-Message Services) have been enthusiastically adopted by consumers worldwide, who have created a new text culture. SMS is now being deployed to provide a range of services and transactions, as well as playing a critical role in offering an interactive path for television broadcasting. In this paper we offer a case study of a lucrative, new industry developing internationally at the intersection of telecommunications, broadcasting, and information services—namely, premium rate SMS/MMS. To explore the issues at stake we focus on an Australian case study of policy responses to the development of premium rate mobile messaging services in the 2002-2005 period. In the first part, we give a brief history of premium rate telecommunications. Secondly, we characterise premium rate mobile message services and examine their emergence. Thirdly, we discuss the responses of Australian policy-makers and industry to these services. Fourthly, we place the Australian experience in international context, and indicate common issues. Finally, we draw some conclusions from the peregrinations of mobile message services for regulators grappling with communications policy frameworks.

Detecting and sorting targeting peptides wtih neural networks and support vector machines

This paper presents a composite multi-layer classifier system for predicting the subcellular localization of proteins based on their amino acid sequence. The work is an extension of our previous predictor PProwler v1.1 which is itself built upon the series of predictors SignalP and TargetP. In this study we outline experiments conducted to improve the classifier design. The major improvement came from using Support Vector machines as a "smart gate" sorting the outputs of several different targeting peptide detection networks. Our final model (PProwler v1.2) gives MCC values of 0.873 for non-plant and 0.849 for plant proteins. The model improves upon the accuracy of our previous subcellular localization predictor (PProwler v1.1) by 2% for plant data (which represents 7.5% improvement upon TargetP).

Blood type classification using computer vision and machine learning

In emergency situations, where time for blood transfusion is reduced, the O negative blood type (the universal donor) is administrated. However, sometimes even the universal donor can cause transfusion reactions that can be fatal to the patient. As commercial systems do not allow fast results and are not suitable for emergency situations, this paper presents the steps considered for the development and validation of a prototype, able to determine blood type compatibilities, even in emergency situations. Thus it is possible, using the developed system, to administer a compatible blood type, since the first blood unit transfused. In order to increase the system’s reliability, this prototype uses different approaches to classify blood types, the first of which is based on Decision Trees and the second one based on support vector machines. The features used to evaluate these classifiers are the standard deviation values, histogram, Histogram of Oriented Gradients and fast Fourier transform, computed on different regions of interest. The main characteristics of the presented prototype are small size, lightweight, easy transportation, ease of use, fast results, high reliability and low cost. These features are perfectly suited for emergency scenarios, where the prototype is expected to be used.