Automated detection of lung nodules in computed tomography images: a review

Lung nodules refer to a range of lung abnormalities the detection of which can facilitate early treatment for lung patients. Lung nodules can be detected by radiologists through examining lung images. Automated detection systems that locate nodules of various sizes within lung images can assist radiologists in their decision making. This paper presents a study of the existing methods on automated lung nodule detection. It introduces a generic structure for lung nodule detection that can be used to represent and describe the existing methods. The structure consists of a number of components including: acquisition, pre-processing, lung segmentation, nodule detection, and false positives reduction. The paper describes the algorithms used to realise each component in different systems. It also provides a comparison of the performance of the existing approaches.

The use of drug detection dogs in Sydney, Australia

Introduction and Aims. At present there is little research into the use of drug detection dogs. The present study sought to explore the use of detection dogs in Sydney, Australia, utilising multiple data sources.

Design and Methods. Data were taken from interviews with 100 regular ecstasy users and 20 key experts as part of the 2006 New South Wales arm of the Ecstasy and Related Drugs Reporting System, and secondary data sources.

Results. The majority of regular ecstasy users reported taking some form of precaution if made aware that dogs would be at an event they were attending. A small proportion of the sample reported consuming their drugs when coming into contact with detection dogs. One group of key experts viewed the use of detection dogs as useful; one group disliked the use of detection dogs though cooperated with law enforcement when dogs were used; and one group considered that detection dogs contribute to greater harm. Secondary data sources further suggested that the use of detection dogs do not significantly assist police in identifying and apprehending drug suppliers.

Discussion and Conclusions. The present study suggests that regular ecstasy users do not see detection dogs as an obstacle to their drug use. Future research is necessary to explore in greater depth the experiences that drug users have with detection dogs; the effect detection dogs may have on deterring drug consumption; whether encounters with detection dogs contribute to drug-related harm; and the cost–benefit analysis of this law enforcement exercise.

Connecting with the horizon : developing teachers' appreciation of mathematical structure

A professional learning program for teachers of junior secondary mathematics regarding the content and pedagogy of senior secondary mathematics is the context for this study of teachers’ mathematical and pedagogical knowledge. The analysis of teachers’ reflections on their learning explored teachers’ understanding of mathematical connections and their appreciation of mathematical structure. The findings indicate that a professional learning program about senior secondary mathematics can enable practicing teachers to deepen and broaden their knowledge for teaching junior secondary mathematics and develop their practice to support their students’ present and future learning of mathematics. Further research is needed about professional learning approaches and tasks that may enable teachers to imbed and develop awareness of structure in their practice.

Hyper-community detection in the blogosphere

Most existing work on learning community structure in social network is graph-based whose links among the members are often represented as an adjacency matrix, encoding direct pairwise associations between members. In this paper, we propose a method to group online communities in blogosphere based on the topics learnt from the content blogged. We then consider a different type of online community formulation - the sentiment-based grouping of online communities. The problem of sentiment-based clustering for community structure discovery is rich with many interesting open aspects to be explored. We propose a novel approach for addressing hyper-community detection based on users' sentiment. We employ a nonparametric clustering to automatically discover hidden hyper-communities and present the results obtained from a large dataset.

Topic transition detection using hierarchical hidden Markov and semi-Markov models

In this paper we introduce a probabilistic framework to exploit hierarchy, structure sharing and duration information for topic transition detection in videos. Our probabilistic detection framework is a combination of a shot classification step and a detection phase using hierarchical probabilistic models. We consider two models in this paper: the extended Hierarchical Hidden Markov Model (HHMM) and the Coxian Switching Hidden semi-Markov Model (S-HSMM) because they allow the natural decomposition of semantics in videos, including shared structures, to be modeled directly, and thus enabling efficient inference and reducing the sample complexity in learning. Additionally, the S-HSMM allows the duration information to be incorporated, consequently the modeling of long-term dependencies in videos is enriched through both hierarchical and duration modeling. Furthermore, the use of the Coxian distribution in the S-HSMM makes it tractable to deal with long sequences in video. Our experimentation of the proposed framework on twelve educational and training videos shows that both models outperform the baseline cases (flat HMM and HSMM) and performances reported in earlier work in topic detection. The superior performance of the S-HSMM over the HHMM verifies our belief that duration information is an important factor in video content modeling.

Polymorphic malware detection using Hierarchical Hidden Markov Model

Binary signatures have been widely used to detect malicious software on the current Internet. However, this approach is unable to achieve the accurate identification of polymorphic malware variants, which can be easily generated by the malware authors using code generation engines. Code generation engines randomly produce varying code sequences but perform the same desired malicious functions. Previous research used flow graph and signature tree to identify polymorphic malware families. The key difficulty of previous research is the generation of precisely defined state machine models from polymorphic variants. This paper proposes a novel approach, using Hierarchical Hidden Markov Model (HHMM), to provide accurate inductive inference of the malware family. This model can capture the features of self-similar and hierarchical structure of polymorphic malware family signature sequences. To demonstrate the effectiveness and efficiency of this approach, we evaluate it with real malware samples. Using more than 15,000 real malware, we find our approach can achieve high true positives, low false positives, and low computational cost.

Event extraction using behaviors of sentiment signals and burst structure in social media

Significant world events often cause the behavioral convergence of the expression of shared sentiment. This paper examines the use of the blogosphere as a framework to study user psychological behaviors, using their sentiment responses as a form of ‘sensor’ to infer real-world events of importance automatically. We formulate a novel temporal sentiment index function using quantitative measure of the valence value of bearing words in blog posts in which the set of affective bearing words is inspired from psychological research in emotion structure. The annual local minimum and maximum of the proposed sentiment signal function are utilized to extract significant events of the year and corresponding blog posts are further analyzed using topic modeling tools to understand their content. The paper then examines the correlation of topics discovered in relation to world news events reported by the mainstream news service provider, Cable News Network, and by using the Google search engine. Next, aiming at understanding sentiment at a finer granularity over time, we propose a stochastic burst detection model, extended from the work of Kleinberg, to work incrementally with stream data. The proposed model is then used to extract sentimental bursts occurring within a specific mood label (for example, a burst of observing ‘shocked’). The blog posts at those time indices are analyzed to extract topics, and these are compared to real-world news events. Our comprehensive set of experiments conducted on a large-scale set of 12 million posts from Livejournal shows that the proposed sentiment index function coincides well with significant world events while bursts in sentiment allow us to locate finer-grain external world events.

Structure and biodegradation mechanism of milled Bombyx mori silk particles

The aim of this study was to understand the structure and biodegradation relationships of silk particles intended for targeted biomedical applications. Such a study is also useful in understanding structural remodelling of silk debris that may be generated from silk-based implants. Ultrafine silk particles were prepared using a combination of efficient wet-milling and spray-drying processes with no addition of chemicals other than those used in degumming. Milling reduced the intermolecular stacking forces within the β-sheet crystallites without changing the intramolecular binding energy. Because of the rough morphology and the ultrafine size of the particles, degradation of silk particles by protease XIV was increased by about 3-fold compared to silk fibers. Upon biodegradation, the thermal degradation temperature of silk increased, which was attributed to the formation of tight aggregates by the hydrolyzed residual macromolecules. A model of the biodegradation mechanism of silk particles was developed based on the experimental data. The model explains the process of disintegration of β-sheets, supported by quantitative secondary structural analysis and microscopic images. © 2012 American Chemical Society.

Towards automated detection of depression from brain structural magnetic resonance images

Introduction : Depression is a major issue worldwide and is seen as a significant health problem. Stigma and patient denial, clinical experience, time limitations, and reliability of psychometrics are barriers to the clinical diagnoses of depression. Thus, the establishment of an automated system that could detect such abnormalities would assist medical experts in their decision-making process. This paper reviews existing methods for the automated detection of depression from brain structural magnetic resonance images (sMRI).Methods : Relevant sources were identified from various databases and online sites using a combination of keywords and terms including depression, major depressive disorder, detection, classification, and MRI databases. Reference lists of chosen articles were further reviewed for associated publications.Results : The paper introduces a generic structure for representing and describing the methods developed for the detection of depression from sMRI of the brain. It consists of a number of components including acquisition and preprocessing, feature extraction, feature selection, and classification.Conclusion : Automated sMRI-based detection methods have the potential to provide an objective measure of depression, hence improving the confidence level in the diagnosis and prognosis of depression.

Guided wave based methods for damage detection : experimental study on concrete joint

Since Guided wave (GW) is sensitive to small damage and can propagate a relatively longer distance with relatively less attenuation, GW-based method has been found as an effective and efficient way to detect incipient damages. In this study, a full-scale concrete joint was constructed to further verify the effectiveness of GW-based method on real civil structures. GW tests were conducted in three stages, including baseline, serviceability and damage conditions. The waves are excited by one actuator and received by several sensors, which are made up of independent piezoelectric elements. Experimental results show that the mehod is promising for damage identification in practices.

Chemiluminescence detection of piperazine designer drugs and related compounds using tris(2,2?-bipyridine)ruthenium(III)

 An exploration of the chemiluminescence from reactions of a large number of benzyl and phenylpiperazine analytes with tris(2,2’-bipyridyl)ruthenium(III) was carried out providing information towards the emission intensity of this chemiluminescent reagent and the structure of analytes it interacts with.

Australian pre-service secondary teacher education courses : challenges and Dilemmas

Real-time lane detection on suburban streets using visual cue integration

The detection of lane boundaries on suburban streets using images obtained from video constitutes a challenging task. This is mainly due to the difficulties associated with estimating the complex geometric structure of lane boundaries, the quality of lane markings as a result of wear, occlusions by traffic, and shadows caused by road-side trees and structures. Most of the existing techniques for lane boundary detection employ a single visual cue and will only work under certain conditions and where there are clear lane markings. Also, better results are achieved when there are no other onroad objects present. This paper extends our previous work and discusses a novel lane boundary detection algorithm specifically addressing the abovementioned issues through the integration of two visual cues. The first visual cue is based on stripe-like features found on lane lines extracted using a two-dimensional symmetric Gabor filter. The second visual cue is based on a texture characteristic determined using the entropy measure of the predefined neighbourhood around a lane boundary line. The visual cues are then integrated using a rulebased classifier which incorporates a modified sequential covering algorithm to improve robustness. To separate lane boundary lines from other similar features, a road mask is generated using road chromaticity values estimated from CIE L*a*b* colour transformation. Extraneous points around lane boundary lines are then removed by an outlier removal procedure based on studentized residuals. The lane boundary lines are then modelled with Bezier spline curves. To validate the algorithm, extensive experimental evaluation was carried out on suburban streets and the results are presented. 

Chemiluminescence detection with water-soluble iridium(III) complexes containing a sulfonate-functionalised ancillary ligand

The chemiluminescence from four cyclometalated iridium(III) complexes containing an ancillary bathophenanthroline-disulfonate ligand exhibited a wide range of emission colours (green to red), and in some cases intensities that are far greater than the commonly employed benchmark reagent, [Ru(bpy)3](2+). A similar complex incorporating a sulfonated triazolylpyridine-based ligand enabled the emission to be shifted into the blue region of the spectrum, but the responses with this complex were relatively poor. DFT calculations of electronic structure and emission spectra support the experimental findings.