Brain decoding based on functional magnetic resonance imaging using machine learning : a comparative study

Brain decoding of functional Magnetic Resonance Imaging data is a pattern analysis task that links brain activity patterns to the experimental conditions. Classifiers predict the neural states from the spatial and temporal pattern of brain activity extracted from multiple voxels in the functional images in a certain period of time. The prediction results offer insight into the nature of neural representations and cognitive mechanisms and the classification accuracy determines our confidence in understanding the relationship between brain activity and stimuli. In this paper, we compared the efficacy of three machine learning algorithms: neural network, support vector machines, and conditional random field to decode the visual stimuli or neural cognitive states from functional Magnetic Resonance data. Leave-one-out cross validation was performed to quantify the generalization accuracy of each algorithm on unseen data. The results indicated support vector machine and conditional random field have comparable performance and the potential of the latter is worthy of further investigation.

Reactive plasmas as a versatile nanofabrication tool

The underlying physics of the application of low-temperature, low-pressure reactive plasmas in various nanoassembly processes is described. From the viewpoint of the "cause and effect" approach, this Colloquium focuses on the benefits and challenges of using plasma-based systems in nanofabrication of nanostructured silicon films, low-dimensional semiconducting quantum structures, ordered carbon nanotip arrays, highly crystalline TiO2 coatings, and nanostructured hydroxyapatite bioceramics. Other examples and future prospects of plasma-aided nanofabrication are also discussed. © 2005 The American Physical Society.

Series in vector spherical harmonics : an efficient tool for solution of nonlinear problems in spherical plasmas

The series expansion of the plasma fields and currents in vector spherical harmonics has been demonstrated to be an efficient technique for solution of nonlinear problems in spherically bounded plasmas. Using this technique, it is possible to describe the nonlinear plasma response to the rotating high-frequency magnetic field applied to the magnetically confined plasma sphere. The effect of the external magnetic field on the current drive and field configuration is studied. The results obtained are important for continuous current drive experiments in compact toruses. © 2000 American Institute of Physics.

Development and pilot study of the Primary Care Practice Improvement Tool (PC-PIT) : an innovative approach

Objective To assess the usability and validity of the Primary Care Practice Improvement Tool (PC-PIT), a practice performance improvement tool based on 13 key elements identified by a systematic review. It was co-created with a range of partners and designed specifically for primary health care. Design This pilot study examined the PC-PIT using a formative assessment framework and mixed-methods research design. Setting and participants Six high-functioning general practices in Queensland, Australia, between February and July 2013. A total of 28 staff participated — 10 general practitioners, six practice or community nurses, 12 administrators (four practice managers; one business manager and eight reception or general administrative staff). Main outcome measures Readability, content validity and staff perceptions of the PC-PIT. Results The PC-PIT offers an appropriate and acceptable approach to internal quality improvement in general practice. Quantitative assessment scores and qualitative data from all staff identified two areas in which the PC-PIT required modification: a reduction in the indicative reading age, and simplification of governance-related terms and concepts. Conclusion The PC-PIT provides an innovative approach to address the complexity of organisational improvement in general practice and primary health care. This initial validation will be used to develop a suite of supporting, high-quality and free-to-access resources to enhance the use of the PC-PIT in general practice. Based on these findings, a national trial is now underway.

Synthesis of orchestrators from service choreographies

Interaction topologies in service-oriented systems are usually classified into two styles: choreographies and orchestrations. In a choreography, services interact in a peer-to-peer manner and no service plays a privileged role. In contrast, interactions in an orchestration occur between one particular service, the orchestrator, and a number of subordinated services. Each of these topologies has its trade-offs. This paper considers the problem of migrating a service-oriented system from a choreography style to an orchestration style. Specifically, the paper presents a tool chain for synthesising orchestrators from choreographies. Choreographies are initially represented as communicating state machines. Based on this representation, an algorithm is presented that synthesises the behaviour of an orchestrator, which is also represented as a state machine. Concurrent regions are then identified in the synthesised state machine to obtain a more compact representation in the form of a Petri net. Finally, it is shown how the resulting Petri nets can be transformed into notations supported by commercial tools, such as the Business Process Modelling Notation (BPMN).

Facebook Versus Ghaddafi : social networking as a tool for democratic change in Libya

The emergence of the Internet is one of the most significant leaps in the history of humanity. Information, knowledge and culture are exchanged among masses of people through interconnected information platforms. These platforms enable our culture to be analysed and rewritten, and fundamentally opens our perceptions to a wide variety of concepts and beliefs. The connected networks of the Internet have shaped a virtual — but communicative — space where people can cross borders freely within a realm characterised by the ability to go anywhere, see anything, learn, compare and understand. This chapter focuses on the Libyan experience with social networking platforms in actualising democratic change in the uprising of 17 February 2011. After briefly outlining the political and economic situation under the regime of Colonel Mummar Ghaddafi, the chapter discusses the role that social networking platforms played during the struggle of the Libyan people for democratic change. Finally, it points out the positive changes that resulted from the uprising and the potential role that social media might play in the ongoing democratization and development of Libyan society.

An integrated modeling and risk analysis tool for mine water interactions

Poor mine water management can lead to corporate, environmental and social risks. These risks become more pronounced as mining operations move into areas of water scarcity and/or increase climatic variability while also managing increased demand, lower ore grades and increased strip ratios. Therefore, it is vital that mine sites better understand these risks in order to implement management practices to address them. Systems models provide an effective approach to understand complex networks, particularly across multiple scales. Previous work has represented mine water interactions using systems model on a mine site scale. Here, we expand on that work by present an integrated tool that uses a systems modeling approach to represent mine water interactions on a site and regional scale and then analyses the risks associated with events stemming from those interactions. A case study is presented to represent three indicative corporate, environmental and social risks associated with a mine site that exists in a water scarce region. The tool is generic and flexible, and can be used in many scenarios to provide significant potential utility to the mining industry.

miRPlant : an integrated tool for identification of plant miRNA from RNA sequencing data

Background Small RNA sequencing is commonly used to identify novel miRNAs and to determine their expression levels in plants. There are several miRNA identification tools for animals such as miRDeep, miRDeep2 and miRDeep*. miRDeep-P was developed to identify plant miRNA using miRDeep’s probabilistic model of miRNA biogenesis, but it depends on several third party tools and lacks a user-friendly interface. The objective of our miRPlant program is to predict novel plant miRNA, while providing a user-friendly interface with improved accuracy of prediction. Result We have developed a user-friendly plant miRNA prediction tool called miRPlant. We show using 16 plant miRNA datasets from four different plant species that miRPlant has at least a 10% improvement in accuracy compared to miRDeep-P, which is the most popular plant miRNA prediction tool. Furthermore, miRPlant uses a Graphical User Interface for data input and output, and identified miRNA are shown with all RNAseq reads in a hairpin diagram. Conclusions We have developed miRPlant which extends miRDeep* to various plant species by adopting suitable strategies to identify hairpin excision regions and hairpin structure filtering for plants. miRPlant does not require any third party tools such as mapping or RNA secondary structure prediction tools. miRPlant is also the first plant miRNA prediction tool that dynamically plots miRNA hairpin structure with small reads for identified novel miRNAs. This feature will enable biologists to visualize novel pre-miRNA structure and the location of small RNA reads relative to the hairpin. Moreover, miRPlant can be easily used by biologists with limited bioinformatics skills.

Automated diagnosis of Age-related Macular Degeneration using greyscale features from digital fundus images

Age-related Macular Degeneration (AMD) is one of the major causes of vision loss and blindness in ageing population. Currently, there is no cure for AMD, however early detection and subsequent treatment may prevent the severe vision loss or slow the progression of the disease. AMD can be classified into two types: dry and wet AMDs. The people with macular degeneration are mostly affected by dry AMD. Early symptoms of AMD are formation of drusen and yellow pigmentation. These lesions are identified by manual inspection of fundus images by the ophthalmologists. It is a time consuming, tiresome process, and hence an automated diagnosis of AMD screening tool can aid clinicians in their diagnosis significantly. This study proposes an automated dry AMD detection system using various entropies (Shannon, Kapur, Renyi and Yager), Higher Order Spectra (HOS) bispectra features, Fractional Dimension (FD), and Gabor wavelet features extracted from greyscale fundus images. The features are ranked using t-test, Kullback–Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance (CBBD), Receiver Operating Characteristics (ROC) curve-based and Wilcoxon ranking methods in order to select optimum features and classified into normal and AMD classes using Naive Bayes (NB), k-Nearest Neighbour (k-NN), Probabilistic Neural Network (PNN), Decision Tree (DT) and Support Vector Machine (SVM) classifiers. The performance of the proposed system is evaluated using private (Kasturba Medical Hospital, Manipal, India), Automated Retinal Image Analysis (ARIA) and STructured Analysis of the Retina (STARE) datasets. The proposed system yielded the highest average classification accuracies of 90.19%, 95.07% and 95% with 42, 54 and 38 optimal ranked features using SVM classifier for private, ARIA and STARE datasets respectively. This automated AMD detection system can be used for mass fundus image screening and aid clinicians by making better use of their expertise on selected images that require further examination.

An analysis of digital storytelling as a documentation tool for research in the creative and performing arts

This paper examines the capacity of digital storytelling to document research activity in the creative and performing arts. In particular, it seeks to identify the thought processes and methods that underpin this research and to capture them using the digital storytelling medium. Interest in this issue was prompted by the author’s work with the creative and performing artists from the Queensland Conservatorium and the Queensland College of Art as part of the Federal government’s Research Quality Framework (RQF) in 2007. The RQF compelled artists to address what it means to undertake research in their disciplines, to describe this, measure it and quantify it; for many practitioners this represents a significant challenge. These issues continue to be pertinent in the context of the Excellence in Research for Australia (ERA) initiative. This research is significant because it seeks to identify, in layman’s terms, the research methods and thought processes used by artists in their research practice. It seeks to do so free of the encumbrances of the professional doctorate policies, the higher education research quality frameworks, and the dense philosophical debates that have to-date dominated discussions of this issue. The research involves qualitative data collection methods including a detailed literature review, interviews with key practitioners and academics involved in the creative and performing arts, and three case studies. The literature review focuses on publications that explore issues of research practice and method in the creative and performing arts. The case studies involve three Queensland-based artists. Digital stories will be developed (and presented) with Marcus and Mafe using their visual materials and drawing on the issues identified in the literature review and interviews. Emmerson’s DVD provided a point of comparison with the digital stories. (Brief bios are attached)

Xenome--a tool for classifying reads from xenograft samples

Motivation Shotgun sequence read data derived from xenograft material contains a mixture of reads arising from the host and reads arising from the graft. Classifying the read mixture to separate the two allows for more precise analysis to be performed. Results We present a technique, with an associated tool Xenome, which performs fast, accurate and specific classification of xenograft-derived sequence read data. We have evaluated it on RNA-Seq data from human, mouse and human-in-mouse xenograft datasets.