Epidemiological and molecular characterization of rubella virus isolated in Sao Paulo, Brazil during 1997-2004

Rubella virus (RV) infection during the early stages of pregnancy can lead to serious birth defects, known as the congenital rubella syndrome (CRS). In 2003, the Pan American Health Organization (PAHO) adopted a resolution calling for the elimination of rubella and the congenital rubella syndrome (CRS) in the Americas by the year 2010. Brazil will have implemented the recommended PAHO strategy for elimination and interruption of endemic rubella virus transmission. The characterization of genotypes during the final stages of rubella elimination is important for determining whether new rubella isolates represent endemic transmission or importations. Samples (blood, urine, cerebrospinal fluid, and throat swabs) collected from patients with symptoms suggestive of rubella infection in 19972004 were isolated in cell culture and genotyped. Twenty-eight sequences were analyzed and two genotypes were identified: 1a and 1G. The information reported in this paper will contribute to understanding the molecular epidemiology of RV in Sao Paulo, Brazil. J. Med. Virol. 84:18311838, 2012. (c) 2012 Wiley Periodicals, Inc.

Phylogenetic analysis of rubella virus strains during an outbreak in Sao Paulo, 2007-2008

Rubella virus (RV) is an important human pathogen that causes rubella, an acute contagious disease. It also causes severe birth defects collectively known as congenital rubella syndrome when infection occurs during the first trimester of pregnancy. Here, we present the phylogenetic analysis of RV that circulated in Sao Paulo during the 20072008 outbreak. Samples collected from patients diagnosed with rubella were isolated in cell culture and sequenced. RV RNA was obtained from samples or RV-infected cell cultures and amplified by reverse transcriptase-polymerase chain reaction. Sequences were assigned to genotypes by phylogenetic analysis using RV reference sequences. Seventeen sequences were analyzed, and three genotypes were identified: 1a, 1G, and 2B. Genotypes 1a and 1G, which were isolated in 2007, were responsible for sporadic rubella cases in Sao Paulo. Thereafter, in late 2007, the epidemiological conditions changed, resulting in a large RV outbreak with the clear dominance of genotype 2B. The results of this study provide new approaches for monitoring the progress of elimination of rubella from Sao Paulo, Brazil. J. Med. Virol. 84:16661671, 2012. (c) 2012 Wiley Periodicals, Inc.

A portable approach for the surveillance of dengue virus-infected mosquitoes

Dengue virus is the most significant human viral pathogen spread by the bite of an infected mosquito. With no vaccine or antiviral therapy currently available, disease prevention relies largely on surveillance and mosquito control. Preventing the onset of dengue outbreaks and effective vector management would be considerably enhanced through surveillance of dengue virus prevalence in natural mosquito populations. However, current approaches to the identification of virus in field-caught mosquitoes require relatively slow and labor intensive techniques such as virus isolation or RT-PCR involving specialized facilities and personnel. A rapid and portable method for detecting dengue virus-infected mosquitoes is described. Using a hand held battery operated homogenizer and a dengue diagnostic rapid strip the viral protein NS1 was detected as a marker of dengue virus infection. This method could be performed in less than 30 min in the field, requiring no downstream processing, and is able to detect a single infected mosquito in a pool of at least 50 uninfected mosquitoes. The method described in this study allows rapid, real-time monitoring of dengue virus presence in mosquito populations and could be a useful addition to effective monitoring and vector control responses.

Electronic workplace surveillance and employee privacy : a comparative analysis of privacy protection in Australia and the United States

More than a century ago in their definitive work “The Right to Privacy” Samuel D. Warren and Louis D. Brandeis highlighted the challenges posed to individual privacy by advancing technology. Today’s workplace is characterised by its reliance on computer technology, particularly the use of email and the Internet to perform critical business functions. Increasingly these and other workplace activities are the focus of monitoring by employers. There is little formal regulation of electronic monitoring in Australian or United States workplaces. Without reasonable limits or controls, this has the potential to adversely affect employees’ privacy rights. Australia has a history of legislating to protect privacy rights, whereas the United States has relied on a combination of constitutional guarantees, federal and state statutes, and the common law. This thesis examines a number of existing and proposed statutory and other workplace privacy laws in Australia and the United States. The analysis demonstrates that existing measures fail to adequately regulate monitoring or provide employees with suitable remedies where unjustifiable intrusions occur. The thesis ultimately supports the view that enacting uniform legislation at the national level provides a more effective and comprehensive solution for both employers and employees. Chapter One provides a general introduction and briefly discusses issues relevant to electronic monitoring in the workplace. Chapter Two contains an overview of privacy law as it relates to electronic monitoring in Australian and United States workplaces. In Chapter Three there is an examination of the complaint process and remedies available to a hypothetical employee (Mary) who is concerned about protecting her privacy rights at work. Chapter Four provides an analysis of the major themes emerging from the research, and also discusses the draft national uniform legislation. Chapter Five details the proposed legislation in the form of the Workplace Surveillance and Monitoring Act, and Chapter Six contains the conclusion.

Towards continuous surveillance of fruit flies using sensor networks and machine vision

In Australia, the Queensland fruit fly (B. tryoni), is the most destructive insect pest of horticulture, attacking nearly all fruit and vegetable crops. This project has researched and prototyped a system for monitoring fruit flies so that authorities can be alerted when a fly enters a crop in a more efficient manner than is currently used. This paper presents the idea of our sensor platform design as well as the fruit fly detection and recognition algorithm by using machine vision techniques. Our experiments showed that the designed trap and sensor platform is capable to capture quality fly images, the invasive flies can be successfully detected and the average precision of the Queensland fruit fly recognition is 80% from our experiment.

Towards automatic power line detection for a UAV surveillance system using pulse coupled neural filter and an improved Hough transform

Spatial information captured from optical remote sensors on board unmanned aerial vehicles (UAVs) has great potential in automatic surveillance of electrical infrastructure. For an automatic vision-based power line inspection system, detecting power lines from a cluttered background is one of the most important and challenging tasks. In this paper, a novel method is proposed, specifically for power line detection from aerial images. A pulse coupled neural filter is developed to remove background noise and generate an edge map prior to the Hough transform being employed to detect straight lines. An improved Hough transform is used by performing knowledge-based line clustering in Hough space to refine the detection results. The experiment on real image data captured from a UAV platform demonstrates that the proposed approach is effective for automatic power line detection.

Improved detection and tracking of objects in surveillance video

Surveillance networks are typically monitored by a few people, viewing several monitors displaying the camera feeds. It is then very difficult for a human operator to effectively detect events as they happen. Recently, computer vision research has begun to address ways to automatically process some of this data, to assist human operators. Object tracking, event recognition, crowd analysis and human identification at a distance are being pursued as a means to aid human operators and improve the security of areas such as transport hubs. The task of object tracking is key to the effective use of more advanced technologies. To recognize an event people and objects must be tracked. Tracking also enhances the performance of tasks such as crowd analysis or human identification. Before an object can be tracked, it must be detected. Motion segmentation techniques, widely employed in tracking systems, produce a binary image in which objects can be located. However, these techniques are prone to errors caused by shadows and lighting changes. Detection routines often fail, either due to erroneous motion caused by noise and lighting effects, or due to the detection routines being unable to split occluded regions into their component objects. Particle filters can be used as a self contained tracking system, and make it unnecessary for the task of detection to be carried out separately except for an initial (often manual) detection to initialise the filter. Particle filters use one or more extracted features to evaluate the likelihood of an object existing at a given point each frame. Such systems however do not easily allow for multiple objects to be tracked robustly, and do not explicitly maintain the identity of tracked objects. This dissertation investigates improvements to the performance of object tracking algorithms through improved motion segmentation and the use of a particle filter. A novel hybrid motion segmentation / optical flow algorithm, capable of simultaneously extracting multiple layers of foreground and optical flow in surveillance video frames is proposed. The algorithm is shown to perform well in the presence of adverse lighting conditions, and the optical flow is capable of extracting a moving object. The proposed algorithm is integrated within a tracking system and evaluated using the ETISEO (Evaluation du Traitement et de lInterpretation de Sequences vidEO - Evaluation for video understanding) database, and significant improvement in detection and tracking performance is demonstrated when compared to a baseline system. A Scalable Condensation Filter (SCF), a particle filter designed to work within an existing tracking system, is also developed. The creation and deletion of modes and maintenance of identity is handled by the underlying tracking system; and the tracking system is able to benefit from the improved performance in uncertain conditions arising from occlusion and noise provided by a particle filter. The system is evaluated using the ETISEO database. The dissertation then investigates fusion schemes for multi-spectral tracking systems. Four fusion schemes for combining a thermal and visual colour modality are evaluated using the OTCBVS (Object Tracking and Classification in and Beyond the Visible Spectrum) database. It is shown that a middle fusion scheme yields the best results and demonstrates a significant improvement in performance when compared to a system using either mode individually. Findings from the thesis contribute to improve the performance of semi-automated video processing and therefore improve security in areas under surveillance.

The use of narrative text for injury surveillance research : a systematic review

Objective: To summarise the extent to which narrative text fields in administrative health data are used to gather information about the event resulting in presentation to a health care provider for treatment of an injury, and to highlight best practise approaches to conducting narrative text interrogation for injury surveillance purposes.----- Design: Systematic review----- Data sources: Electronic databases searched included CINAHL, Google Scholar, Medline, Proquest, PubMed and PubMed Central.. Snowballing strategies were employed by searching the bibliographies of retrieved references to identify relevant associated articles.----- Selection criteria: Papers were selected if the study used a health-related database and if the study objectives were to a) use text field to identify injury cases or use text fields to extract additional information on injury circumstances not available from coded data or b) use text fields to assess accuracy of coded data fields for injury-related cases or c) describe methods/approaches for extracting injury information from text fields.----- Methods: The papers identified through the search were independently screened by two authors for inclusion, resulting in 41 papers selected for review. Due to heterogeneity between studies metaanalysis was not performed.----- Results: The majority of papers reviewed focused on describing injury epidemiology trends using coded data and text fields to supplement coded data (28 papers), with these studies demonstrating the value of text data for providing more specific information beyond what had been coded to enable case selection or provide circumstantial information. Caveats were expressed in terms of the consistency and completeness of recording of text information resulting in underestimates when using these data. Four coding validation papers were reviewed with these studies showing the utility of text data for validating and checking the accuracy of coded data. Seven studies (9 papers) described methods for interrogating injury text fields for systematic extraction of information, with a combination of manual and semi-automated methods used to refine and develop algorithms for extraction and classification of coded data from text. Quality assurance approaches to assessing the robustness of the methods for extracting text data was only discussed in 8 of the epidemiology papers, and 1 of the coding validation papers. All of the text interrogation methodology papers described systematic approaches to ensuring the quality of the approach.----- Conclusions: Manual review and coding approaches, text search methods, and statistical tools have been utilised to extract data from narrative text and translate it into useable, detailed injury event information. These techniques can and have been applied to administrative datasets to identify specific injury types and add value to previously coded injury datasets. Only a few studies thoroughly described the methods which were used for text mining and less than half of the studies which were reviewed used/described quality assurance methods for ensuring the robustness of the approach. New techniques utilising semi-automated computerised approaches and Bayesian/clustering statistical methods offer the potential to further develop and standardise the analysis of narrative text for injury surveillance.