Decision support system for age-related macular degeneration using discrete wavelet transform

Age-related macular degeneration (AMD) affects the central vision and subsequently may lead to visual loss in people over 60 years of age. There is no permanent cure for AMD, but early detection and successive treatment may improve the visual acuity. AMD is mainly classified into dry and wet type; however, dry AMD is more common in aging population. AMD is characterized by drusen, yellow pigmentation, and neovascularization. These lesions are examined through visual inspection of retinal fundus images by ophthalmologists. It is laborious, time-consuming, and resource-intensive. Hence, in this study, we have proposed an automated AMD detection system using discrete wavelet transform (DWT) and feature ranking strategies. The first four-order statistical moments (mean, variance, skewness, and kurtosis), energy, entropy, and Gini index-based features are extracted from DWT coefficients. We have used five (t test, Kullback–Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance, receiver operating characteristics curve-based, and Wilcoxon) feature ranking strategies to identify optimal feature set. A set of supervised classifiers namely support vector machine (SVM), decision tree, k -nearest neighbor ( k -NN), Naive Bayes, and probabilistic neural network were used to evaluate the highest performance measure using minimum number of features in classifying normal and dry AMD classes. The proposed framework obtained an average accuracy of 93.70 %, sensitivity of 91.11 %, and specificity of 96.30 % using KLD ranking and SVM classifier. We have also formulated an AMD Risk Index using selected features to classify the normal and dry AMD classes using one number. The proposed system can be used to assist the clinicians and also for mass AMD screening programs.

Perception of prescription medicine sample packs among Australian professional, government, industry, and consumer organizations, based on automated textual analysis of one-on-one interviews

Background Prescription medicine samples provided by pharmaceutical companies are predominantly newer and more expensive products. The range of samples provided to practices may not represent the drugs that the doctors desire to have available. Few studies have used a qualitative design to explore the reasons behind sample use. Objective The aim of this study was to explore the opinions of a variety of Australian key informants about prescription medicine samples, using a qualitative methodology. Methods Twenty-three organizations involved in quality use of medicines in Australia were identified, based on the authors' previous knowledge. Each organization was invited to nominate 1 or 2 representatives to participate in semistructured interviews utilizing seeding questions. Each interview was recorded and transcribed verbatim. Leximancer v2.25 text analysis software (Leximancer Pty Ltd., Jindalee, Queensland, Australia) was used for textual analysis. The top 10 concepts from each analysis group were interrogated back to the original transcript text to determine the main emergent opinions. Results A total of 18 key interviewees representing 16 organizations participated. Samples, patient, doctor, and medicines were the major concepts among general opinions about samples. The concept drug became more frequent and the concept companies appeared when marketing issues were discussed. The Australian Pharmaceutical Benefits Scheme and cost were more prevalent in discussions about alternative sample distribution models, indicating interviewees were cognizant of budgetary implications. Key interviewee opinions added richness to the single-word concepts extracted by Leximancer. Conclusions Participants recognized that prescription medicine samples have an influence on quality use of medicines and play a role in the marketing of medicines. They also believed that alternative distribution systems for samples could provide benefits. The cost of a noncommercial system for distributing samples or starter packs was a concern. These data will be used to design further research investigating alternative models for distribution of samples.

Phage library screening for the rapid identification and in vivo testing of candidate genes for a DNA vaccine against Mycoplasma mycoides subsp. mycoides small colony biotype

A new strategy for rapidly selecting and testing genetic vaccines has been developed, in which a whole genome library is cloned into a bacteriophage λ ZAP Express vector which contains both prokaryotic (Plac) and eukaryotic (PCMV) promoters upstream of the insertion site. The phage library is plated on Escherichia coli cells, immunoblotted, and probed with hyperimmune and/or convalescent-phase antiserum to rapidly identify vaccine candidates. These are then plaque purified and grown as liquid lysates, and whole bacteriophage particles are then used directly to immunize the host, following which PCMV-driven expression of the candidate vaccine gene occurs. In the example given here, a semirandom genome library of the bovine pathogen Mycoplasma mycoides subsp. mycoides small colony (SC) biotype was cloned into λ ZAP Express, and two strongly immunodominant clones, λ-A8 and λ-B1, were identified and subsequently tested for vaccine potential against M. mycoides subsp. mycoides SC biotype-induced mycoplasmemia. Sequencing and immunoblotting indicated that clone λ-A8 expressed an isopropyl-β-d-thiogalactopyranoside (IPTG)-inducible M. mycoides subsp. mycoides SC biotype protein with a 28-kDa apparent molecular mass, identified as a previously uncharacterized putative lipoprotein (MSC_0397). Clone λ-B1 contained several full-length genes from the M. mycoides subsp. mycoides SC biotype pyruvate dehydrogenase region, and two IPTG-independent polypeptides, of 29 kDa and 57 kDa, were identified on immunoblots. Following vaccination, significant anti-M. mycoides subsp. mycoides SC biotype responses were observed in mice vaccinated with clones λ-A8 and λ-B1. A significant stimulation index was observed following incubation of splenocytes from mice vaccinated with clone λ-A8 with whole live M. mycoides subsp. mycoides SC biotype cells, indicating cellular proliferation. After challenge, mice vaccinated with clone λ-A8 also exhibited a reduced level of mycoplasmemia compared to controls, suggesting that the MSC_0397 lipoprotein has a protective effect in the mouse model when delivered as a bacteriophage DNA vaccine. Bacteriophage-mediated immunoscreening using an appropriate vector system offers a rapid and simple technique for the identification and immediate testing of putative candidate vaccines from a variety of pathogens.

Discovery of a lipid synthesising organ in the auditory system of an insect

Weta possess typical Ensifera ears. Each ear comprises three functional parts: two equally sized tympanal membranes, an underlying system of modified tracheal chambers, and the auditory sensory organ, the crista acustica. This organ sits within an enclosed fluid-filled channel-previously presumed to be hemolymph. The role this channel plays in insect hearing is unknown. We discovered that the fluid within the channel is not actually hemolymph, but a medium composed principally of lipid from a new class. Three-dimensional imaging of this lipid channel revealed a previously undescribed tissue structure within the channel, which we refer to as the olivarius organ. Investigations into the function of the olivarius reveal de novo lipid synthesis indicating that it is producing these lipids in situ from acetate. The auditory role of this lipid channel was investigated using Laser Doppler vibrometry of the tympanal membrane, which shows that the displacement of the membrane is significantly increased when the lipid is removed from the auditory system. Neural sensitivity of the system, however, decreased upon removal of the lipid-a surprising result considering that in a typical auditory system both the mechanical and auditory sensitivity are positively correlated. These two results coupled with 3D modelling of the auditory system lead us to hypothesize a model for weta audition, relying strongly on the presence of the lipid channel. This is the first instance of lipids being associated with an auditory system outside of the Odentocete cetaceans, demonstrating convergence for the use of lipids in hearing.

Pollen image classification using the classifynder system: Algorithm comparison and a case study on New Zealand honey

We describe an investigation into how Massey University’s Pollen Classifynder can accelerate the understanding of pollen and its role in nature. The Classifynder is an imaging microscopy system that can locate, image and classify slide based pollen samples. Given the laboriousness of purely manual image acquisition and identification it is vital to exploit assistive technologies like the Classifynder to enable acquisition and analysis of pollen samples. It is also vital that we understand the strengths and limitations of automated systems so that they can be used (and improved) to compliment the strengths and weaknesses of human analysts to the greatest extent possible. This article reviews some of our experiences with the Classifynder system and our exploration of alternative classifier models to enhance both accuracy and interpretability. Our experiments in the pollen analysis problem domain have been based on samples from the Australian National University’s pollen reference collection (2,890 grains, 15 species) and images bundled with the Classifynder system (400 grains, 4 species). These samples have been represented using the Classifynder image feature set.We additionally work through a real world case study where we assess the ability of the system to determine the pollen make-up of samples of New Zealand honey. In addition to the Classifynder’s native neural network classifier, we have evaluated linear discriminant, support vector machine, decision tree and random forest classifiers on these data with encouraging results. Our hope is that our findings will help enhance the performance of future releases of the Classifynder and other systems for accelerating the acquisition and analysis of pollen samples.

A comparison of classification algorithms within the Classifynder pollen imaging system

We describe an investigation into how Massey University's Pollen Classifynder can accelerate the understanding of pollen and its role in nature. The Classifynder is an imaging microscopy system that can locate, image and classify slide based pollen samples. Given the laboriousness of purely manual image acquisition and identification it is vital to exploit assistive technologies like the Classifynder to enable acquisition and analysis of pollen samples. It is also vital that we understand the strengths and limitations of automated systems so that they can be used (and improved) to compliment the strengths and weaknesses of human analysts to the greatest extent possible. This article reviews some of our experiences with the Classifynder system and our exploration of alternative classifier models to enhance both accuracy and interpretability. Our experiments in the pollen analysis problem domain have been based on samples from the Australian National University's pollen reference collection (2890 grains, 15 species) and images bundled with the Classifynder system (400 grains, 4 species). These samples have been represented using the Classifynder image feature set. In addition to the Classifynder's native neural network classifier, we have evaluated linear discriminant, support vector machine, decision tree and random forest classifiers on these data with encouraging results. Our hope is that our findings will help enhance the performance of future releases of the Classifynder and other systems for accelerating the acquisition and analysis of pollen samples. © 2013 AIP Publishing LLC.

Visual control for automated aircraft collision avoidance systems

This thesis presents a new vision-based decision and control strategy for automated aircraft collision avoidance that can be realistically applied to the See and Avoid problem. The effectiveness of the control strategy positions the research as a major contribution toward realising the simultaneous operation of manned and unmanned aircraft within civilian airspace. Key developments include novel classical and visual predictive control frameworks, and a performance evaluation technique aligned with existing aviation practise and applicable to autonomous systems. The overall approach is demonstrated through experimental results on a small multirotor unmanned aircraft, and through high fidelity probabilistic simulation studies.

Automated sensory data alignment for environmental and epidermal change monitoring

In this paper we present research adapting a state of the art condition-invariant robotic place recognition algorithm to the role of automated inter- and intra-image alignment of sensor observations of environmental and skin change over time. The approach involves inverting the typical criteria placed upon navigation algorithms in robotics; we exploit rather than attempt to fix the limited camera viewpoint invariance of such algorithms, showing that approximate viewpoint repetition is realistic in a wide range of environments and medical applications. We demonstrate the algorithms automatically aligning challenging visual data from a range of real-world applications: ecological monitoring of environmental change, aerial observation of natural disasters including flooding, tsunamis and bushfires and tracking wound recovery and sun damage over time and present a prototype active guidance system for enforcing viewpoint repetition. We hope to provide an interesting case study for how traditional research criteria in robotics can be inverted to provide useful outcomes in applied situations.

Evolutionary game design : automated game design comes of age

The "Humies" awards are an annual competition held in conjunction with the Genetic and Evolutionary Computation Conference (GECCO), in which cash prizes totalling $10,000 are awarded to the most human-competitive results produced by any form of evolutionary computation published in the previous year. This article describes the gold medal-winning entry from the 2012 "Humies" competition, based on the LUDI system for playing, evaluating and creating new board games. LUDI was able to demonstrate human-competitive results in evolving novel board games that have gone on to be commercially published, one of which, Yavalath, has been ranked in the top 2.5% of abstract board games ever invented. Further evidence of human-competitiveness was demonstrated in the evolved games implicitly capturing several principles of good game design, outperforming human designers in at least one case, and going on to inspire a new sub-genre of games.

Prediction of GMA welding characteristic parameter by artificial neural network system

Nowadays, demand for automated Gas metal arc welding (GMAW) is growing and consequently need for intelligent systems is increased to ensure the accuracy of the procedure. To date, welding pool geometry has been the most used factor in quality assessment of intelligent welding systems. But, it has recently been found that Mahalanobis Distance (MD) not only can be used for this purpose but also is more efficient. In the present paper, Artificial Neural Networks (ANN) has been used for prediction of MD parameter. However, advantages and disadvantages of other methods have been discussed. The Levenberg–Marquardt algorithm was found to be the most effective algorithm for GMAW process. It is known that the number of neurons plays an important role in optimal network design. In this work, using trial and error method, it has been found that 30 is the optimal number of neurons. The model has been investigated with different number of layers in Multilayer Perceptron (MLP) architecture and has been shown that for the aim of this work the optimal result is obtained when using MLP with one layer. Robustness of the system has been evaluated by adding noise into the input data and studying the effect of the noise in prediction capability of the network. The experiments for this study were conducted in an automated GMAW setup that was integrated with data acquisition system and prepared in a laboratory for welding of steel plate with 12 mm in thickness. The accuracy of the network was evaluated by Root Mean Squared (RMS) error between the measured and the estimated values. The low error value (about 0.008) reflects the good accuracy of the model. Also the comparison of the predicted results by ANN and the test data set showed very good agreement that reveals the predictive power of the model. Therefore, the ANN model offered in here for GMA welding process can be used effectively for prediction goals.

Establishing a participatory library model: A Grounded Theory study

Purpose The purpose of this research is to explore the idea of the participatory library in higher education settings. This research aims to address the question, what is a participatory university library? Design/methodology/approach Grounded theory approach was adopted. In-depth individual interviews were conducted with two diverse groups of participants including ten library staff members and six library users. Data collection and analysis were carried out simultaneously and complied with Straussian grounded theory principles and techniques. Findings Three core categories representing the participatory library were found including “community”, “empowerment”, and “experience”. Each category was thoroughly delineated via sub-categories, properties, and dimensions that all together create a foundation for the participatory library. A participatory library model was also developed together with an explanation of model building blocks that provide a deeper understanding of the participatory library phenomenon. Research limitations The research focuses on a specific library system, i.e., academic libraries. Therefore, the research results may not be very applicable to public, special, and school library contexts. Originality/value This is the first empirical study developing a participatory library model. It provides librarians, library managers, researchers, library students, and the library community with a holistic picture of the contemporary library.

Application of higher-order spectra for automated grading of diabetic maculopathy

Diabetic macular edema (DME) is one of the most common causes of visual loss among diabetes mellitus patients. Early detection and successive treatment may improve the visual acuity. DME is mainly graded into non-clinically significant macular edema (NCSME) and clinically significant macular edema according to the location of hard exudates in the macula region. DME can be identified by manual examination of fundus images. It is laborious and resource intensive. Hence, in this work, automated grading of DME is proposed using higher-order spectra (HOS) of Radon transform projections of the fundus images. We have used third-order cumulants and bispectrum magnitude, in this work, as features, and compared their performance. They can capture subtle changes in the fundus image. Spectral regression discriminant analysis (SRDA) reduces feature dimension, and minimum redundancy maximum relevance method is used to rank the significant SRDA components. Ranked features are fed to various supervised classifiers, viz. Naive Bayes, AdaBoost and support vector machine, to discriminate No DME, NCSME and clinically significant macular edema classes. The performance of our system is evaluated using the publicly available MESSIDOR dataset (300 images) and also verified with a local dataset (300 images). Our results show that HOS cumulants and bispectrum magnitude obtained an average accuracy of 95.56 and 94.39 % for MESSIDOR dataset and 95.93 and 93.33 % for local dataset, respectively.

LibVM: An architecture for shared library sandboxing

Many software applications extend their functionality by dynamically loading libraries into their allocated address space. However, shared libraries are also often of unknown provenance and quality and may contain accidental bugs or, in some cases, deliberately malicious code. Most sandboxing techniques which address these issues require recompilation of the libraries using custom tool chains, require significant modifications to the libraries, do not retain the benefits of single address-space programming, do not completely isolate guest code, or incur substantial performance overheads. In this paper we present LibVM, a sandboxing architecture for isolating libraries within a host application without requiring any modifications to the shared libraries themselves, while still retaining the benefits of a single address space and also introducing a system call inter-positioning layer that allows complete arbitration over a shared library’s functionality. We show how to utilize contemporary hardware virtualization support towards this end with reasonable performance overheads and, in the absence of such hardware support, our model can also be implemented using a software-based mechanism. We ensure that our implementation conforms as closely as possible to existing shared library manipulation functions, minimizing the amount of effort needed to apply such isolation to existing programs. Our experimental results show that it is easy to gain immediate benefits in scenarios where the goal is to guard the host application against unintentional programming errors when using shared libraries, as well as in more complex scenarios, where a shared library is suspected of being actively hostile. In both cases, no changes are required to the shared libraries themselves.

Automated topometric graph generation from floor plan analysis

The world is rich with information such as signage and maps to assist humans to navigate. We present a method to extract topological spatial information from a generic bitmap floor plan and build a topometric graph that can be used by a mobile robot for tasks such as path planning and guided exploration. The algorithm first detects and extracts text in an image of the floor plan. Using the locations of the extracted text, flood fill is used to find the rooms and hallways. Doors are found by matching SURF features and these form the connections between rooms, which are the edges of the topological graph. Our system is able to automatically detect doors and differentiate between hallways and rooms, which is important for effective navigation. We show that our method can extract a topometric graph from a floor plan and is robust against ambiguous cases most commonly seen in floor plans including elevators and stairwells.