Fuzzy methods for analysis of microarrays and networks

Bioinformatics involves analyses of biological data such as DNA sequences, microarrays and protein-protein interaction (PPI) networks. Its two main objectives are the identification of genes or proteins and the prediction of their functions. Biological data often contain uncertain and imprecise information. Fuzzy theory provides useful tools to deal with this type of information, hence has played an important role in analyses of biological data. In this thesis, we aim to develop some new fuzzy techniques and apply them on DNA microarrays and PPI networks. We will focus on three problems: (1) clustering of microarrays; (2) identification of disease-associated genes in microarrays; and (3) identification of protein complexes in PPI networks. The first part of the thesis aims to detect, by the fuzzy C-means (FCM) method, clustering structures in DNA microarrays corrupted by noise. Because of the presence of noise, some clustering structures found in random data may not have any biological significance. In this part, we propose to combine the FCM with the empirical mode decomposition (EMD) for clustering microarray data. The purpose of EMD is to reduce, preferably to remove, the effect of noise, resulting in what is known as denoised data. We call this method the fuzzy C-means method with empirical mode decomposition (FCM-EMD). We applied this method on yeast and serum microarrays, and the silhouette values are used for assessment of the quality of clustering. The results indicate that the clustering structures of denoised data are more reasonable, implying that genes have tighter association with their clusters. Furthermore we found that the estimation of the fuzzy parameter m, which is a difficult step, can be avoided to some extent by analysing denoised microarray data. The second part aims to identify disease-associated genes from DNA microarray data which are generated under different conditions, e.g., patients and normal people. We developed a type-2 fuzzy membership (FM) function for identification of diseaseassociated genes. This approach is applied to diabetes and lung cancer data, and a comparison with the original FM test was carried out. Among the ten best-ranked genes of diabetes identified by the type-2 FM test, seven genes have been confirmed as diabetes-associated genes according to gene description information in Gene Bank and the published literature. An additional gene is further identified. Among the ten best-ranked genes identified in lung cancer data, seven are confirmed that they are associated with lung cancer or its treatment. The type-2 FM-d values are significantly different, which makes the identifications more convincing than the original FM test. The third part of the thesis aims to identify protein complexes in large interaction networks. Identification of protein complexes is crucial to understand the principles of cellular organisation and to predict protein functions. In this part, we proposed a novel method which combines the fuzzy clustering method and interaction probability to identify the overlapping and non-overlapping community structures in PPI networks, then to detect protein complexes in these sub-networks. Our method is based on both the fuzzy relation model and the graph model. We applied the method on several PPI networks and compared with a popular protein complex identification method, the clique percolation method. For the same data, we detected more protein complexes. We also applied our method on two social networks. The results showed our method works well for detecting sub-networks and give a reasonable understanding of these communities.

Recommending people in social networks using data mining

This thesis improves the process of recommending people to people in social networks using new clustering algorithms and ranking methods. The proposed system and methods are evaluated on the data collected from a real life social network. The empirical analysis of this research confirms that the proposed system and methods achieved improvements in the accuracy and efficiency of matching and recommending people, and overcome some of the problems that social matching systems usually suffer.

Money laundering and FATF compliance by the international community

This paper examines the anti-money laundering systems of Australia, the United Arab Emirates (UAE), the United Kingdom (UK) and the United States of America (USA), the extent to which they have implemented the Financial Action Task Force (FATF) recommendations, and how compliance with these recommendations is affected by local cultural and economic factors. The paper makes use of FATF evaluation reports to compare the countries’ compliance; it examines some of the underlying cultural considerations and culture-specific ethical issues that affect the extent of compliance, and how cultural and ethical considerations may affect good governance. The findings indicate that the UK and the USA are the most advanced with regards to their compliance with the FATF recommendations and Australia and the UAE less so. The UAE is in particular found to be least compliant. We relate this finding to previous work on how a country’s legal and financial systems develop in line with its religion, culture and socio-economic situation, and examine how such local factors have affected the UAE’s financial and anti-money laundering and combating the financing of terrorism (AML/CFT) systems. This research will be of interest to policy-makers and government agencies involved in addressing money laundering and its successful detection and prosecution.

Improved foreground detection via block-based classifier cascade with probabilistic decision integration

Background subtraction is a fundamental low-level processing task in numerous computer vision applications. The vast majority of algorithms process images on a pixel-by-pixel basis, where an independent decision is made for each pixel. A general limitation of such processing is that rich contextual information is not taken into account. We propose a block-based method capable of dealing with noise, illumination variations, and dynamic backgrounds, while still obtaining smooth contours of foreground objects. Specifically, image sequences are analyzed on an overlapping block-by-block basis. A low-dimensional texture descriptor obtained from each block is passed through an adaptive classifier cascade, where each stage handles a distinct problem. A probabilistic foreground mask generation approach then exploits block overlaps to integrate interim block-level decisions into final pixel-level foreground segmentation. Unlike many pixel-based methods, ad-hoc postprocessing of foreground masks is not required. Experiments on the difficult Wallflower and I2R datasets show that the proposed approach obtains on average better results (both qualitatively and quantitatively) than several prominent methods. We furthermore propose the use of tracking performance as an unbiased approach for assessing the practical usefulness of foreground segmentation methods, and show that the proposed approach leads to considerable improvements in tracking accuracy on the CAVIAR dataset.

Analyzing the effectiveness of graph metrics for anomaly detection in online social networks

Online social networks can be modelled as graphs; in this paper, we analyze the use of graph metrics for identifying users with anomalous relationships to other users. A framework is proposed for analyzing the effectiveness of various graph theoretic properties such as the number of neighbouring nodes and edges, betweenness centrality, and community cohesiveness in detecting anomalous users. Experimental results on real-world data collected from online social networks show that the majority of users typically have friends who are friends themselves, whereas anomalous users’ graphs typically do not follow this common rule. Empirical analysis also shows that the relationship between average betweenness centrality and edges identifies anomalies more accurately than other approaches.

Can a community pharmacy sleep assessment tool aid the identification of patients at risk of sleep disorders in the community : a pilot study

Background: When experiencing sleep problems for the first time, consumers may often approach community pharmacists for advice as they are easily accessible health care professionals in the community. In Australian community pharmacies there are no specific tools available for use by pharmacists to assist with the assessment and handling of consumers with sleep enquiries. Objective: To assess the feasibility of improving the detection of sleep disorders within the community through the pilot of a newly developed Community Pharmacy Sleep Assessment Tool (COP-SAT). Method: The COP-SAT was designed to incorporate elements from a number of existing, standardized, and validated clinical screening measures. The COP-SAT was trialed in four Australian community pharmacies over a 4-week period. Key findings: A total of 241 community pharmacy consumers were assessed using the COP-SAT. A total of 74 (30.7%) were assessed as being at risk of insomnia, 26 (10.7%) were at risk of daytime sleepiness, 19 (7.9%) were at risk of obstructive sleep apnea, and 121 (50.2%) were regular snorers. A total of 116 (48.1%) participants indicated that they consume caffeine before bedtime, of which 55 (47%) had associated symptoms of sleep onset insomnia. Moreover, 85 (35%) consumed alcohol before bedtime, of which 50 (58%) experienced fragmented sleep, 50 (58%) were regular snorers, and nine (10.6%) had apnea symptoms. The COP-SAT was feasible in the community pharmacy setting. The prevalence of sleep disorders in the sampled population was high, but generally consistent with previous studies on the general population. Conclusion: A large proportion of participants reported sleep disorder symptoms, and a link was found between the consumption of alcohol and caffeine substances at bedtime and associated symptoms. While larger studies are needed to assess the clinical properties of the tool, the results of this feasibility study have demonstrated that the COP-SAT may be a practical tool for the identification of patients at risk of developing sleep disorders in the community.

An MRF based abnormal event detection approach using motion and appearance features

Abnormal event detection has attracted a lot of attention in the computer vision research community during recent years due to the increased focus on automated surveillance systems to improve security in public places. Due to the scarcity of training data and the definition of an abnormality being dependent on context, abnormal event detection is generally formulated as a data-driven approach where activities are modeled in an unsupervised fashion during the training phase. In this work, we use a Gaussian mixture model (GMM) to cluster the activities during the training phase, and propose a Gaussian mixture model based Markov random field (GMM-MRF) to estimate the likelihood scores of new videos in the testing phase. Further-more, we propose two new features: optical acceleration, and the histogram of optical flow gradients; to detect the presence of any abnormal objects and speed violations in the scene. We show that our proposed method outperforms other state of the art abnormal event detection algorithms on publicly available UCSD dataset.

Community wide interventions for increasing physical activity

Background Multi-strategic community wide interventions for physical activity are increasingly popular but their ability to achieve population level improvements is unknown. Objectives To evaluate the effects of community wide, multi-strategic interventions upon population levels of physical activity. Search methods We searched the Cochrane Public Health Group Segment of the Cochrane Register of Studies,The Cochrane Library, MEDLINE, MEDLINE in Process, EMBASE, CINAHL, LILACS, PsycINFO, ASSIA, the British Nursing Index, Chinese CNKI databases, EPPI Centre (DoPHER, TRoPHI), ERIC, HMIC, Sociological Abstracts, SPORTDiscus, Transport Database and Web of Science (Science Citation Index, Social Sciences Citation Index, Conference Proceedings Citation Index). We also scanned websites of the EU Platform on Diet, Physical Activity and Health; Health-Evidence.org; the International Union for Health Promotion and Education; the NIHR Coordinating Centre for Health Technology (NCCHTA); the US Centre for Disease Control and Prevention (CDC) and NICE and SIGN guidelines. Reference lists of all relevant systematic reviews, guidelines and primary studies were searched and we contacted experts in the field. The searches were updated to 16 January 2014, unrestricted by language or publication status. Selection criteria Cluster randomised controlled trials, randomised controlled trials, quasi-experimental designs which used a control population for comparison, interrupted time-series studies, and prospective controlled cohort studies were included. Only studies with a minimum six-month follow up from the start of the intervention to measurement of outcomes were included. Community wide interventions had to comprise at least two broad strategies aimed at physical activity for the whole population. Studies which randomised individuals from the same community were excluded. Data collection and analysis At least two review authors independently extracted the data and assessed the risk of bias. Each study was assessed for the setting, the number of included components and their intensity. The primary outcome measures were grouped according to whether they were dichotomous (per cent physically active, per cent physically active during leisure time, and per cent physically inactive) or continuous (leisure time physical activity time (time spent)), walking (time spent), energy expenditure (as metabolic equivalents or METS)). For dichotomous measures we calculated the unadjusted and adjusted risk difference, and the unadjusted and adjusted relative risk. For continuous measures we calculated percentage change from baseline, unadjusted and adjusted. Main results After the selection process had been completed, 33 studies were included. A total of 267 communities were included in the review (populations between 500 and 1.9 million). Of the included studies, 25 were set in high income countries and eight were in low income countries. The interventions varied by the number of strategies included and their intensity. Almost all of the interventions included a component of building partnerships with local governments or non-governmental organisations (NGOs) (29 studies). None of the studies provided results by socio-economic disadvantage or other markers of equity. However, of those included studies undertaken in high income countries, 14 studies were described as being provided to deprived, disadvantaged or low socio-economic communities. Nineteen studies were identified as having a high risk of bias, 10 studies were unclear, and four studies had a low risk of bias. Selection bias was a major concern with these studies, with only five studies using randomisation to allocate communities. Four studies were judged as being at low risk of selection bias although 19 studies were considered to have an unclear risk of bias. Twelve studies had a high risk of detection bias, 13 an unclear risk and four a low risk of bias. Generally, the better designed studies showed no improvement in the primary outcome measure of physical activity at a population level. All four of the newly included, and judged to be at low risk of bias, studies (conducted in Japan, United Kingdom and USA) used randomisation to allocate the intervention to the communities. Three studies used a cluster randomised design and one study used a stepped wedge design. The approach to measuring the primary outcome of physical activity was better in these four studies than in many of the earlier studies. One study obtained objective population representative measurements of physical activity by accelerometers, while the remaining three low-risk studies used validated self-reported measures. The study using accelerometry, conducted in low income, high crime communities of USA, emphasised social marketing, partnership with police and environmental improvements. No change in the seven-day average daily minutes of moderate to vigorous physical activity was observed during the two years of operation. Some program level effect was observed with more people walking in the intervention community, however this result was not evident in the whole community. Similarly, the two studies conducted in the United Kingdom (one in rural villages and the other in urban London; both using communication, partnership and environmental strategies) found no improvement in the mean levels of energy expenditure per person per week, measured from one to four years from baseline. None of the three low risk studies reporting a dichotomous outcome of physical activity found improvements associated with the intervention. Overall, there was a noticeable absence of reporting of benefit in physical activity for community wide interventions in the included studies. However, as a group, the interventions undertaken in China appeared to have the greatest possibility of success with high participation rates reported. Reporting bias was evident with two studies failing to report physical activity measured at follow up. No adverse events were reported.The data pertaining to cost and sustainability of the interventions were limited and varied. Authors' conclusions Although numerous studies have been undertaken, there is a noticeable inconsistency of the findings in the available studies and this is confounded by serious methodological issues within the included studies. The body of evidence in this review does not support the hypothesis that the multi-component community wide interventions studied effectively increased physical activity for the population, although some studies with environmental components observed more people walking. Plain language summary Community wide interventions for increasing physical activity Not having enough physical activity leads to poorer health. Regular physical activity can reduce the risk of chronic disease and improve one's health and wellbeing. The lack of physical activity is a common and in some cases a growing health problem. To address this, 33 studies have used improvement activities directed at communities, using more than one approach in a single program. When we first looked at the available research in 2011 we observed that there was a lack of good studies which could show whether this approach was beneficial or not. Some studies claimed that community wide programs improved physical activities and other studies did not. In this update we found four new studies that were of good quality; however none of these four studies increased physical activity levels for the population. Some studies reported program level effects such as observing more people walking, however the population level of physical activity had not increased. This review found that community wide interventions are very difficult to undertake, and it appears that they usually fail to provide a measurable benefit in physical activity for a population. It is apparent that many of the interventions failed to reach a substantial portion of the community, and we speculate that some single strategies included in the combination may lack individual effectiveness. Laički sažetak Intervencije u zajednici za povećanje tjelesne aktivnosti Nedostatna tjelesna aktivnost povezana je s lošijim zdravljem.Redovita tjelesna aktivnost može umanjiti rizik od kroničnih bolesti te poboljšati zdravlje i kvalitetu života pojedinca.Manjak tjelesne aktivnosti čest je problem, a učestalost tog problema se povećava.Cochrane sustavni pregled je analizirao 33 studije koje su istražile programe za povećanje tjelesne aktivnosti u zajednici, u kojima se koristilo više od jednog pristupa.Kad su prvi put pregledani dokazi iz istraživanja koja su bila dostupna 2011. godine, utvrđeno je da nema dovoljno dobrih studija koje bi mogle pokazati je li takav pristup koristan ili ne.Primjerice, neke studije tvrde da programi za povećanje tjelesne aktivnosti u zajednici poboljšavaju tjelesnu aktivnost pojedinaca u zajednici, a druge studije tvrde suprotno.U ovom obnovljenom sustavnom pregledu pronađene su 4 nove studije koje su bile visoke kvalitete, ail nijedna od tih studija nije pokazala da je istraživana intervencija dovela do povećanja tjelesne aktivnosti u zajednici.Neke su studije opisale učinak na način da je opisano da je uočeno da više ljudi u zajednici hoda, međutim, ukupna razina tjelesne aktivnosti u promatranoj populaciji nije se povećala.Ovaj sustavni pregled je utvrdio da je intervencije za povećanje tjelesne aktivnosti u zajednici teško provesti i čini se da one obično ne uspijevaju u svojoj namjeri da na mjerljiv način povećaju tjelesnu aktivnost u populaciji.Čini se da mnoge intervencije nisu uspjele doseći veći broj stanovnika u zajednici pa se može smatrati da neke od strategija uključene u analizirane kombinacije nisu zasebno učinkovite.

Deterrence of drug driving: The impact of the ACT drug driving legislation and detection techniques

The study sought to explore the initial impact of the ACT's implementation of roadside oral fluid drug screening program. The results suggest that a number of individuals reported intentions to drug drive in the future. The classical deterrence theory variables of certainty of apprehension, severity and swiftness of sanctions were not predictive of intentions to drug drive in the future. In contrast, having avoided apprehension and having known of others that have avoided apprehension were predictive of intentions to drug drive in the future. Increasing perceptions of the certainty of apprehension, increased testing frequency, and increased awareness of the oral fluid drug screening program could potentially lead to reductions of drug driving and result in safer road environment for all ACT community members.

Fault detection of wind turbine drivetrain utilizing power-speed characteristics

Wind energy, being the fastest growing renewable energy source in the present world, requires a large number of wind turbines to transform wind energy into electricity. One factor driving the cost of this energy is the reliable operation of these turbines. Therefore, it is a growing requirement within the wind farm community, to monitor the operation of the wind turbines on a continuous basis so that a possible fault can be detected ahead of time. As the wind turbine operates in an environment of constantly changing wind speed, it is a challenging task to design a fault detection technique which can accommodate the stochastic operational behavior of the turbines. Addressing this issue, this paper proposes a novel fault detection criterion which is robust against operational uncertainty, as well as having the ability to quantify severity level specifically of the drivetrain abnormality within an operating wind turbine. A benchmark model of wind turbine has been utilized to simulate drivetrain fault condition and effectiveness of the proposed technique has been tested accordingly. From the simulation result it can be concluded that the proposed criterion exhibits consistent performance for drivetrain faults for varying wind speed and has linear relationship with the fault severity level.