Repeatability of confocal microscopy measurements on the central and peripheral cornea

Purpose To assess confocal microscopy repeatability (ConfoScan3, Nidek, Italy) when assessing the morphology of the limbus, midperipheral and central cornea. Method The central, mid-peripheral and limbal cornea (temporal and nasal) of the right eye of 8 subjects were examined with a ConfoScan3 in two different visits, at least six months apart. Bland-Altman repeatability was measured for 29 parameters: basal cell density and size, anterior and posterior keratocyte densities (AKD/PKD), endothelial cell density, polymegethism, pleomorphism, mean area and sides of endothelial cells - in the five different corneal areas examined. Results As a percentage of the mean absolute values, repeatability of 0–10% was classified as “excellent”, between 10–30% as “acceptable” and over 30% as “poor”. Repeatability was excellent for 14% of parameters and acceptable for 52% of parameters. The number of endothelial cell sides in the central cornea demonstrated the best repeatability (2.0%) whilst mid-temporal PKD showed the poorest repeatability (53.7%). Conclusions Confocal microscopy is at least an adequately repeatablemethodof evaluating the various corneal layers at different locations. Our dataset supports the ongoing use of the technique in research and clinical practice.

Dynamic spatiotemporal trends of dengue transmission in the Asia-Pacific region, 1955–2004

BACKGROUND: Dengue fever (DF) is one of the most important emerging arboviral human diseases. Globally, DF incidence has increased by 30-fold over the last fifty years, and the geographic range of the virus and its vectors has expanded. The disease is now endemic in more than 120 countries in tropical and subtropical parts of the world. This study examines the spatiotemporal trends of DF transmission in the Asia-Pacific region over a 50-year period, and identified the disease's cluster areas. METHODOLOGY AND FINDINGS: The World Health Organization's DengueNet provided the annual number of DF cases in 16 countries in the Asia-Pacific region for the period 1955 to 2004. This fifty-year dataset was divided into five ten-year periods as the basis for the investigation of DF transmission trends. Space-time cluster analyses were conducted using scan statistics to detect the disease clusters. This study shows an increasing trend in the spatiotemporal distribution of DF in the Asia-Pacific region over the study period. Thailand, Vietnam, Laos, Singapore and Malaysia are identified as the most likely clusters (relative risk = 13.02) of DF transmission in this region in the period studied (1995 to 2004). The study also indicates that, for the most part, DF transmission has expanded southwards in the region. CONCLUSIONS: This information will lead to the improvement of DF prevention and control strategies in the Asia-Pacific region by prioritizing control efforts and directing them where they are most needed.

Statistical modelling of district-level residential electricity use in NSW, Australia

Electricity network investment and asset management require accurate estimation of future demand in energy consumption within specified service areas. For this purpose, simple models are typically developed to predict future trends in electricity consumption using various methods and assumptions. This paper presents a statistical model to predict electricity consumption in the residential sector at the Census Collection District (CCD) level over the state of New South Wales, Australia, based on spatial building and household characteristics. Residential household demographic and building data from the Australian Bureau of Statistics (ABS) and actual electricity consumption data from electricity companies are merged for 74 % of the 12,000 CCDs in the state. Eighty percent of the merged dataset is randomly set aside to establish the model using regression analysis, and the remaining 20 % is used to independently test the accuracy of model prediction against actual consumption. In 90 % of the cases, the predicted consumption is shown to be within 5 kWh per dwelling per day from actual values, with an overall state accuracy of -1.15 %. Given a future scenario with a shift in climate zone and a growth in population, the model is used to identify the geographical or service areas that are most likely to have increased electricity consumption. Such geographical representation can be of great benefit when assessing alternatives to the centralised generation of energy; having such a model gives a quantifiable method to selecting the 'most' appropriate system when a review or upgrade of the network infrastructure is required.

Unsupervised temporal ensemble alignment for rapid annotation

This paper presents a novel framework for the unsupervised alignment of an ensemble of temporal sequences. This approach draws inspiration from the axiom that an ensemble of temporal signals stemming from the same source/class should have lower rank when "aligned" rather than "misaligned". Our approach shares similarities with recent state of the art methods for unsupervised images ensemble alignment (e.g. RASL) which breaks the problem into a set of image alignment problems (which have well known solutions i.e. the Lucas-Kanade algorithm). Similarly, we propose a strategy for decomposing the problem of temporal ensemble alignment into a similar set of independent sequence problems which we claim can be solved reliably through Dynamic Time Warping (DTW). We demonstrate the utility of our method using the Cohn-Kanade+ dataset, to align expression onset across multiple sequences, which allows us to automate the rapid discovery of event annotations.

Mining specific features for acquiring user information needs

Term-based approaches can extract many features in text documents, but most include noise. Many popular text-mining strategies have been adapted to reduce noisy information from extracted features; however, text-mining techniques suffer from low frequency. The key issue is how to discover relevance features in text documents to fulfil user information needs. To address this issue, we propose a new method to extract specific features from user relevance feedback. The proposed approach includes two stages. The first stage extracts topics (or patterns) from text documents to focus on interesting topics. In the second stage, topics are deployed to lower level terms to address the low-frequency problem and find specific terms. The specific terms are determined based on their appearances in relevance feedback and their distribution in topics or high-level patterns. We test our proposed method with extensive experiments in the Reuters Corpus Volume 1 dataset and TREC topics. Results show that our proposed approach significantly outperforms the state-of-the-art models.

Not as it seems : using Deleuzian concepts of the imaginary to rethink children’s drawings

Deleuze (1990) states in Negotiations that signs are realized in ideas. Although Deleuze referred to cinema, his thinking about signs and ideas can apply to drawings. Cinema is moving imagery and drawing is static, however both are informed and constructed from realized ideas that continue to shift beyond the artifact. Theories about children’s drawings have historically pertained to establishing schematic universalities rather than acknowledging the agglomerative connections they make to the multiple things occurring around a drawing as it is created. Universal schemas however persist within early childhood art discourses despite the growth of critical theory research into other aspects of childhood. Deleuze’s assertions about the signs and classifications of cinema help to contest notions of schematic development, i.e. children should progress through particular iconic drawing stages at particular ages. Deleuze’s quotes and thoughts on the imaginary and imagination are referenced to interrogate ‘scientific’ knowledges and the gathering of evidential truths about children’s intellectual growth and development. Four examples from a dataset of drawings from a pilot study, undertaken by the author that tested the methodological potential of intergenerational collaborative drawing in early childhood settings, facilitate focused discussion on the above contestations.

Managing and analysing big audio data for environmental monitoring

Environmental monitoring is becoming critical as human activity and climate change place greater pressures on biodiversity, leading to an increasing need for data to make informed decisions. Acoustic sensors can help collect data across large areas for extended periods making them attractive in environmental monitoring. However, managing and analysing large volumes of environmental acoustic data is a great challenge and is consequently hindering the effective utilization of the big dataset collected. This paper presents an overview of our current techniques for collecting, storing and analysing large volumes of acoustic data efficiently, accurately, and cost-effectively.

The value of political connections : evidence from Korean chaebols

This thesis examines the value of political connections for business groups by constructing a unique dataset that allows us to identify the form and extent of the connections. Results show firms' membership to family-controlled business groups (South Korean chaebol) play a key role in determining the value of political connections. Politically connected chaebol firms experience substantial price increases following the establishment of the connection than other firms, but the reverse is found for other (non-family-controlled) connected business groups.

Developing intercultural understanding through Asian-Australian children’s literature.

This PETAA paper discusses how the cross-curriculum priority concerned with developing Asia literacy, namely ‘Asia and Australia’s engagement with Asia’, can be significantly advanced through the study of children’s literature. The discussion proceeds from a brief overview of the historical development of Asia literacy to its current place within the Australian Curriculum. It then considers the potential of literature for assisting students and teachers in realising this priority through the Asian-Australian Children’s Literature and Publishing dataset, a research project on AustLit. Finally, it discusses a small selection of texts – two picture books and a novel – with suggestions or prompts for raising students’ intercultural understanding.

Clinician-driven automated classification of limb fractures from free-text radiology reports

The aim of this research is to report initial experimental results and evaluation of a clinician-driven automated method that can address the issue of misdiagnosis from unstructured radiology reports. Timely diagnosis and reporting of patient symptoms in hospital emergency departments (ED) is a critical component of health services delivery. However, due to disperse information resources and vast amounts of manual processing of unstructured information, a point-of-care accurate diagnosis is often difficult. A rule-based method that considers the occurrence of clinician specified keywords related to radiological findings was developed to identify limb abnormalities, such as fractures. A dataset containing 99 narrative reports of radiological findings was sourced from a tertiary hospital. The rule-based method achieved an F-measure of 0.80 and an accuracy of 0.80. While our method achieves promising performance, a number of avenues for improvement were identified using advanced natural language processing (NLP) techniques.

Automatic de-identification of electronic health records : an Australian perspective

We present an approach to automatically de-identify health records. In our approach, personal health information is identified using a Conditional Random Fields machine learning classifier, a large set of linguistic and lexical features, and pattern matching techniques. Identified personal information is then removed from the reports. The de-identification of personal health information is fundamental for the sharing and secondary use of electronic health records, for example for data mining and disease monitoring. The effectiveness of our approach is first evaluated on the 2007 i2b2 Shared Task dataset, a widely adopted dataset for evaluating de-identification techniques. Subsequently, we investigate the robustness of the approach to limited training data; we study its effectiveness on different type and quality of data by evaluating the approach on scanned pathology reports from an Australian institution. This data contains optical character recognition errors, as well as linguistic conventions that differ from those contained in the i2b2 dataset, for example different date formats. The findings suggest that our approach compares to the best approach from the 2007 i2b2 Shared Task; in addition, the approach is found to be robust to variations of training size, data type and quality in presence of sufficient training data.

Moving object detection in aerial video based on spatiotemporal saliency

In this paper, the problem of moving object detection in aerial video is addressed. While motion cues have been extensively exploited in the literature, how to use spatial information is still an open problem. To deal with this issue, we propose a novel hierarchical moving target detection method based on spatiotemporal saliency. Temporal saliency is used to get a coarse segmentation, and spatial saliency is extracted to obtain the object’s appearance details in candidate motion regions. Finally, by combining temporal and spatial saliency information, we can get refined detection results. Additionally, in order to give a full description of the object distribution, spatial saliency is detected in both pixel and region levels based on local contrast. Experiments conducted on the VIVID dataset show that the proposed method is efficient and accurate.

Online self-supervised multi-instance segmentation of dynamic objects

This paper presents a method for the continuous segmentation of dynamic objects using only a vehicle mounted monocular camera without any prior knowledge of the object’s appearance. Prior work in online static/dynamic segmentation is extended to identify multiple instances of dynamic objects by introducing an unsupervised motion clustering step. These clusters are then used to update a multi-class classifier within a self-supervised framework. In contrast to many tracking-by-detection based methods, our system is able to detect dynamic objects without any prior knowledge of their visual appearance shape or location. Furthermore, the classifier is used to propagate labels of the same object in previous frames, which facilitates the continuous tracking of individual objects based on motion. The proposed system is evaluated using recall and false alarm metrics in addition to a new multi-instance labelled dataset to evaluate the performance of segmenting multiple instances of objects.

Quantitative genetic parameters for body traits at different ages in a cultured stock of giant freshwater prawn (Macrobrachium rosenbergii) selected for fast growth

The aim of the current study was to estimate heritabilities and correlations for body traits at different ages (Weeks 10 and 18 after stocking) in a giant freshwater prawn (Macrobrachium rosenbergii) population selected for fast growth rate in Vietnam. The dataset consisted of 4650 body records (2432 and 2218 records collected at Weeks 10 and 18, respectively) in the full pedigree comprising a total of 18 387 records. Variance and covariance components were estimated using restricted maximum likelihood fitting a multi-trait animal model. Estimates of heritability for body traits (bodyweight, body length, cephalothorax length, abdominal length, cephalothorax width and abdominal width) were moderate and ranged from 0.06 to 0.11 and from 0.11 to 0.22 at Weeks 10 and 18, respectively. Body-trait heritabilities estimated at Week 10 were not significantly lower than at Week 18. Genetic correlations between body traits within age and genetic correlations for body traits between ages were generally high. Our results suggested that selection for high growth rate in GFP can be undertaken successfully before full market size has been reached.

Occupy Oakland and #oo : uses of Twitter within the Occupy movement

Social media have become crucial tools for political activists and protest movements, providing another channel for promoting messages and garnering support. Twitter, in particular, has been identified as a noteworthy medium for protests in countries including Iran and Egypt to receive global attention. The Occupy movement, originating with protests in, and the physical occupation of, Wall Street, and inspiring similar demonstrations in other U.S. cities and around the world, has been intrinsically linked with social media through location-specific hashtags: #ows for Occupy Wall Street, #occupysf for San Francisco, and so on. While the individual protests have a specific geographical focus-highlighted by the physical occupation of parks, buildings, and other urban areas-Twitter provides a means for these different movements to be linked and promoted through tweets containing multiple hashtags. It also serves as a channel for tactical communications during actions and as a space in which movement debates take place. This paper examines Twitter's use within the Occupy Oakland movement. We use a mixture of ethnographic research through interviews with activists and participant observation of the movements' activities, and a dataset of public tweets containing the #oo hashtag from early 2012. This research methodology allows us to develop a more accurate and nuanced understanding of how movement activists use Twitter by cross-checking trends in the online data with observations and activists' own reported use of Twitter. We also study the connections between a geographically focused movement such as Occupy Oakland and related, but physically distant, protests taking place concurrently in other cities. This study forms part of a wider research project, Mapping Movements, exploring the politics of place, investigating how social movements are composed and sustained, and the uses of online communication within these movements.