A pattern based two-stage text classifier

In a classification problem typically we face two challenging issues, the diverse characteristic of negative documents and sometimes a lot of negative documents that are closed to positive documents. Therefore, it is hard for a single classifier to clearly classify incoming documents into classes. This paper proposes a novel gradual problem solving to create a two-stage classifier. The first stage identifies reliable negatives (negative documents with weak positive characteristics). It concentrates on minimizing the number of false negative documents (recall-oriented). We use Rocchio, an existing recall based classifier, for this stage. The second stage is a precision-oriented “fine tuning”, concentrates on minimizing the number of false positive documents by applying pattern (a statistical phrase) mining techniques. In this stage a pattern-based scoring is followed by threshold setting (thresholding). Experiment shows that our statistical phrase based two-stage classifier is promising.

Investigation of lymphotoxin α genetic variants in migraine

Migraine is a common neurological disease with a genetic basis affecting approximately 12% of the population. Pain during a migraine attack is associated with activation of the trigeminal nerve system, which carries pain signals from the meninges and the blood vessels infusing the meninges to the trigeminal nucleus in the brain stem. The release of inflammatory mediators following cortical spreading depression (CSD) may further promote and sustain the activation and sensitization of meningeal nociceptors, inducing the persistent throbbing headache characterised in migraine. Lymphotoxin α (LTA) is a cytokine secreted by lymphocytes and is a member of the tumour necrosis factor (TNF) family. Genetic variation with the TNF and LTA genes may contribute to threshold brain excitability, propagation of neuronal hyperexcitability and thus initiation and maintenance of a migraine attack. Three LTA variants rs2009658, rs2844482 and rs2229094 were identified in a recent pGWAS study conducted in the Norfolk Island population as being potentially implicated in migraine with nominally significant p values of p = 0.0093, p = 0.0088 and p = 0.033 respectively. To determine whether these SNPs played a role in migraine in a general outbred population these SNPs were gentoyped in a large case control Australian Caucasian population and tested for association with migraine. All three SNPs showed no association in our cohort (p > 0.05). Validation of GWAS data in independent case-controls cohorts is essential to establish risk validity within specific population groups. The importance of cytokines in modulating neural inflammation and pain threshold in addition to other studies showing associations between TNF-α and SNPs in the LTA gene with migraine, suggests that LTA could be an important factor contributing to migraine. Although the present study did not support a role for the tested LTA variants in migraine, investigation of other variants within the LTA gene is still warranted.

The human μ-opioid receptor gene polymorphism (A118G) is associated with head pain severity in a clinical cohort of female migraine with aura patients

Migraine is a painful and debilitating, neurovascular disease. Current migraine head pain treatments work with differing efficacies in migraineurs. The opioid system plays an important role in diverse biological functions including analgesia, drug response and pain reduction. The A118G single nucleotide polymorphism (SNP) in exon 1 of the μ-opioid receptor gene (OPRM1) has been associated with elevated pain responses and decreased pain threshold in a variety of populations. The aim of the current preliminary study was to test whether genotypes of the OPRM1 A118G SNP are associated with head pain severity in a clinical cohort of female migraineurs. This was a preliminary study to determine whether genotypes of the OPRM1 A118G SNP are associated with head pain severity in a clinical cohort of female migraineurs. A total of 153 chronic migraine with aura sufferers were assessed for migraine head pain using the Migraine Disability Assessment Score instrument and classified into high and low pain severity groups. DNA was extracted and genotypes obtained for the A118G SNP. Logistic regression analysis adjusting for age effects showed the A118G SNP of the OPRM1 gene to be significantly associated with migraine pain severity in the test population (P = 0.0037). In particular, G118 allele carriers were more likely to be high pain sufferers compared to homozygous carriers of the A118 allele (OR = 3.125, 95 % CI = 1.41, 6.93, P = 0.0037). These findings suggest that A118G genotypes of the OPRM1 gene may influence migraine-associated head pain in females. Further investigations are required to fully understand the effect of this gene variant on migraine head pain including studies in males and in different migraine subtypes, as well as in response to head pain medication.

Homeostatic epistemology : reliability, coherence and coordination in a Bayesian virtue epistemology

How do agents with limited cognitive capacities flourish in informationally impoverished or unexpected circumstances? Aristotle argued that human flourishing emerged from knowing about the world and our place within it. If he is right, then the virtuous processes that produce knowledge, best explain flourishing. Influenced by Aristotle, virtue epistemology defends an analysis of knowledge where beliefs are evaluated for their truth and the intellectual virtue or competences relied on in their creation. However, human flourishing may emerge from how degrees of ignorance are managed in an uncertain world. Perhaps decision-making in the shadow of knowledge best explains human wellbeing—a Bayesian approach? In this dissertation I argue that a hybrid of virtue and Bayesian epistemologies explains human flourishing—what I term homeostatic epistemology. Homeostatic epistemology supposes that an agent has a rational credence p when p is the product of reliable processes aligned with the norms of probability theory; whereas an agent knows that p when a rational credence p is the product of reliable processes such that: 1) p meets some relevant threshold for belief (such that the agent acts as though p were true and indeed p is true), 2) p coheres with a satisficing set of relevant beliefs and, 3) the relevant set of beliefs is coordinated appropriately to meet the integrated aims of the agent. Homeostatic epistemology recognizes that justificatory relationships between beliefs are constantly changing to combat uncertainties and to take advantage of predictable circumstances. Contrary to holism, justification is built up and broken down across limited sets like the anabolic and catabolic processes that maintain homeostasis in the cells, organs and systems of the body. It is the coordination of choristic sets of reliably produced beliefs that create the greatest flourishing given the limitations inherent in the situated agent.

A flexible Bayesian model for describing temporal variability of N2O emissions from an Australian pasture

Soil-based emissions of nitrous oxide (N2O), a well-known greenhouse gas, have been associated with changes in soil water-filled pore space (WFPS) and soil temperature in many previous studies. However, it is acknowledged that the environment-N2O relationship is complex and still relatively poorly unknown. In this article, we employed a Bayesian model selection approach (Reversible jump Markov chain Monte Carlo) to develop a data-informed model of the relationship between daily N2O emissions and daily WFPS and soil temperature measurements between March 2007 and February 2009 from a soil under pasture in Queensland, Australia, taking seasonal factors and time-lagged effects into account. The model indicates a very strong relationship between a hybrid seasonal structure and daily N2O emission, with the latter substantially increased in summer. Given the other variables in the model, daily soil WFPS, lagged by a week, had a negative influence on daily N2O; there was evidence of a nonlinear positive relationship between daily soil WFPS and daily N2O emission; and daily soil temperature tended to have a linear positive relationship with daily N2O emission when daily soil temperature was above a threshold of approximately 19°C. We suggest that this flexible Bayesian modeling approach could facilitate greater understanding of the shape of the covariate-N2O flux relation and detection of effect thresholds in the natural temporal variation of environmental variables on N2O emission.

Evolution of lure response in tephritid fruit flies : phytochemicals as drivers of sexual selection

The males of many Bactrocera species (Diptera: Tephritidae) respond strongly and positively to a small number of plant-derived chemicals (=male lures). Males that have imbibed the lures commonly have a mating advantage over unfed males, but no female benefits have been demonstrated for females mating with lure-fed males. It has been hypothesized that the strong lure response is a case of runaway selection, where males receive direct benefits and females receive indirect benefits via 'sexy sons', or a case of sensory bias where females have a lower threshold response to lures. To test these hypotheses we studied the effects of lure feeding on male mating, remating and longevity; while for females that had mated with lure-fed males we recorded mating refractoriness, fecundity, egg viability and longevity. We used Bactrocera tryoni as our test animal and as lures the naturally occurring zingerone and chemically related, but synthetic chemical cuelure. Feeding on lures provided direct male benefits in greater mating success and increased multiple mating. For the first time, we recorded direct female effects: increased fecundity and reduced remating receptivity. Egg viability did not differ in females mated with lure-fed or unfed males. The life span of males and females exposed to lures was reduced. These results reveal direct, current-generation fitness benefits for both males and females, although the male benefits appear greater. We discuss that while lure response is indeed likely to be a sexual selection trait, there is no need to invoke runaway selection to explain its evolution.

Migraine genetics and prospects for pharmacotherapy

Migraine is a common complex neurological disorder with a well-known but poorly characterized genetic liability. The search for migraine susceptibility genes has been the focus of intense research. It is now believed that common migraine is not a single gene disorder, but attributable to several potentially interacting genetic variants. These variants may differ in each sufferer and interact with environmental factors to set the individual migraine threshold. This genetic liability may play an important role in the clinical heterogeneity seen in migraine and also in the variability of treatment response. This review will look at genetic loci implicated in migraine to date and consider their current or prospective role in migraine therapy. To elucidate the complex nature of migraine genetic liability, approaches that consider detailed endophenotypic profiles that encompass treatment response may provide much more relevant information than simple end diagnosis.

Liminal moments : designing, thinking and learning

This paper provides a contextual reflection for understanding best practice teaching to first year design students. The outcome (job) focused approach to higher education has lead to some unanticipated collateral damage for students, and in the case we discuss, has altered the students’ expectations of course delivery with specific implications and challenges for design educators. This tendency in educational delivery systems is further compounded by the distinct characteristics of Generation Y students within a classroom context. It is our belief that foundational design education must focus more on process than outcomes, and through this research with first year design students we analyse and raise questions relative to the curriculum for a Design and Creative Thinking course—in which students not only benefit from learning the theories and processes of design thinking, conceptualisation and creativity, but also are encouraged to see it as an essential tool for their education and development as designers. This study considers the challenges within a design environment; specifically, we address the need for process based learning in contrast to the outcome-focused approach taken by most students. The authors base their reflections on teaching design students at a university in Queensland, Australia.

Genetic variation in cytokine-related genes and migraine susceptibility

Migraine is classified by the World Health Organization (WHO) as being one of the top 20 most debilitating diseases. According to the neurovascular hypothesis, neuroinflammation may promote the activation and sensitisation of meningeal nociceptors, inducing the persistent throbbing headache characterized in migraine. The tumor necrosis factor (TNF) gene cluster, made up of TNFα, lymphotoxin α (LTA), and lymphotoxin β (LTB), has been implicated to influence the intensity and duration of local inflammation. It is thought that sterile inflammation mediated by LTA, LTB, and TNFα contributes to threshold brain excitability, propagation of neuronal hyperexcitability and thus initiation and maintenance of a migraine attack. Previous studies have investigated variants within the TNF gene cluster region in relation to migraine susceptibility, with largely conflicting results. The aim of this study was to expand on previous research and utilize a large case-control cohort and range of variants within the TNF gene cluster to investigate the role of the TNF gene cluster in migraine. Nine single nucleotide polymorphisms (SNPs) were selected for investigation as follows: rs1800683, rs2229094, rs2009658, rs2071590, rs2239704, rs909253, rs1800630, rs1800629, and rs3093664. No significant association with migraine susceptibility was found for any of the SNPs tested, with further testing according to migraine subtype and gender also showing no association for disease risk. Haplotype analysis showed that none of the tested haplotypes were significantly associated with migraine.

Cardiac parasympathetic reactivation following exercise : implications for training prescription

The objective of exercise training is to initiate desirable physiological adaptations that ultimately enhance physical work capacity. Optimal training prescription requires an individualized approach, with an appropriate balance of training stimulus and recovery and optimal periodization. Recovery from exercise involves integrated physiological responses. The cardiovascular system plays a fundamental role in facilitating many of these responses, including thermoregulation and delivery/removal of nutrients and waste products. As a marker of cardiovascular recovery, cardiac parasympathetic reactivation following a training session is highly individualized. It appears to parallel the acute/intermediate recovery of the thermoregulatory and vascular systems, as described by the supercompensation theory. The physiological mechanisms underlying cardiac parasympathetic reactivation are not completely understood. However, changes in cardiac autonomic activity may provide a proxy measure of the changes in autonomic input into organs and (by default) the blood flow requirements to restore homeostasis. Metaboreflex stimulation (e.g. muscle and blood acidosis) is likely a key determinant of parasympathetic reactivation in the short term (0–90 min post-exercise), whereas baroreflex stimulation (e.g. exercise-induced changes in plasma volume) probably mediates parasympathetic reactivation in the intermediate term (1–48 h post-exercise). Cardiac parasympathetic reactivation does not appear to coincide with the recovery of all physiological systems (e.g. energy stores or the neuromuscular system). However, this may reflect the limited data currently available on parasympathetic reactivation following strength/resistance-based exercise of variable intensity. In this review, we quantitatively analyse post-exercise cardiac parasympathetic reactivation in athletes and healthy individuals following aerobic exercise, with respect to exercise intensity and duration, and fitness/training status. Our results demonstrate that the time required for complete cardiac autonomic recovery after a single aerobic-based training session is up to 24 h following low-intensity exercise, 24–48 h following threshold-intensity exercise and at least 48 h following high-intensity exercise. Based on limited data, exercise duration is unlikely to be the greatest determinant of cardiac parasympathetic reactivation. Cardiac autonomic recovery occurs more rapidly in individuals with greater aerobic fitness. Our data lend support to the concept that in conjunction with daily training logs, data on cardiac parasympathetic activity are useful for individualizing training programmes. In the final sections of this review, we provide recommendations for structuring training microcycles with reference to cardiac parasympathetic recovery kinetics. Ultimately, coaches should structure training programmes tailored to the unique recovery kinetics of each individual.

Sequential decision fusion of multibiometrics applied to text-dependent speaker verification for controlled errors

Reliability of the performance of biometric identity verification systems remains a significant challenge. Individual biometric samples of the same person (identity class) are not identical at each presentation and performance degradation arises from intra-class variability and inter-class similarity. These limitations lead to false accepts and false rejects that are dependent. It is therefore difficult to reduce the rate of one type of error without increasing the other. The focus of this dissertation is to investigate a method based on classifier fusion techniques to better control the trade-off between the verification errors using text-dependent speaker verification as the test platform. A sequential classifier fusion architecture that integrates multi-instance and multisample fusion schemes is proposed. This fusion method enables a controlled trade-off between false alarms and false rejects. For statistically independent classifier decisions, analytical expressions for each type of verification error are derived using base classifier performances. As this assumption may not be always valid, these expressions are modified to incorporate the correlation between statistically dependent decisions from clients and impostors. The architecture is empirically evaluated by applying the proposed architecture for text dependent speaker verification using the Hidden Markov Model based digit dependent speaker models in each stage with multiple attempts for each digit utterance. The trade-off between the verification errors is controlled using the parameters, number of decision stages (instances) and the number of attempts at each decision stage (samples), fine-tuned on evaluation/tune set. The statistical validation of the derived expressions for error estimates is evaluated on test data. The performance of the sequential method is further demonstrated to depend on the order of the combination of digits (instances) and the nature of repetitive attempts (samples). The false rejection and false acceptance rates for proposed fusion are estimated using the base classifier performances, the variance in correlation between classifier decisions and the sequence of classifiers with favourable dependence selected using the 'Sequential Error Ratio' criteria. The error rates are better estimated by incorporating user-dependent (such as speaker-dependent thresholds and speaker-specific digit combinations) and class-dependent (such as clientimpostor dependent favourable combinations and class-error based threshold estimation) information. The proposed architecture is desirable in most of the speaker verification applications such as remote authentication, telephone and internet shopping applications. The tuning of parameters - the number of instances and samples - serve both the security and user convenience requirements of speaker-specific verification. The architecture investigated here is applicable to verification using other biometric modalities such as handwriting, fingerprints and key strokes.

Selecting rainfall events for effective Water Sensitive Urban Design (WSUD) implementation

The current approach for protecting the receiving water environment from urban stormwater pollution is the adoption of structural measures commonly referred to as Water Sensitive Urban Design (WSUD). The treatment efficiency of WSUD measures closely depends on the design of the specific treatment units. As stormwater quality can be influenced by rainfall characteristics, the selection of appropriate rainfall events for treatment design is essential to ensure the effectiveness of WSUD systems. Based on extensive field investigation of four urban residential catchments and computer modelling, this paper details a technically robust approach for the selection of rainfall events for stormwater treatment design using a three-component model. The modelling outcomes indicate that selecting smaller average recurrence interval (ARI) events with high intensity-short duration as the threshold for the treatment system design is the most feasible since these events cumulatively generate a major portion of the annual pollutant load compared to the other types of rainfall events, despite producing a relatively smaller runoff volume. This implies that designs based on small and more frequent rainfall events rather than larger rainfall events would be appropriate in the context of efficiency in treatment performance, cost-effectiveness and possible savings in land area needed.

Monotonous trend estimation of deck displacement for automatic landing of rotorcraft UAVs

This paper presents a novel and practical procedure for estimating the mean deck height to assist in automatic landing operations of a Rotorcraft Unmanned Aerial Vehicle (RUAV) in harsh sea environments. A modified Prony Analysis (PA) procedure is outlined to deal with real-time observations of deck displacement, which involves developing an appropriate dynamic model to approach real deck motion with parameters identified through implementing the Forgetting Factor Recursive Least Square (FFRLS) method. The model order is specified using a proper order-selection criterion based on minimizing the summation of accumulated estimation errors. In addition, a feasible threshold criterion is proposed to separate the dominant components of deck displacement, which results in an accurate instantaneous estimation of the mean deck position. Simulation results demonstrate that the proposed recursive procedure exhibits satisfactory estimation performance when applied to real-time deck displacement measurements, making it well suited for integration into ship-RUAV approach and landing guidance systems.

Postnatal depression in Central Vietnam

Postnatal depression (PND) is a significant global health issue, which not only impacts maternal wellbeing, but also infant development and family structures. Mental health disorders represent approximately 14% of global burden of disease and disability, including low and middle-income countries (LMIC), and PND has direct relevance to the Millennium Development Goals of reducing child mortality, improving maternal health, and creating global partnerships (United Nations, 2012; Guiseppe, Becker & Farmer, 2011). Emerging evidence suggests that PND in LMIC is similar to, or higher than in high-income countries (HIC), however, less than 10% of LMIC have prevalence data available (Fisher, Cabral de Mello, & Izutsu 2009; Lund et al., 2011). Whilst a small number of studies on maternal mental disorders have been published in Vietnam, only one specifically focuses on PND in a hospital-based sample. Also, community based mental health studies and information on mental health in rural areas of Vietnam is still scarce. The purpose of this study was to determine the prevalence of PND, and its associated social determinants in postnatal women in Thua Thien Hue Province, Central Vietnam. In order to identify social determinants relevant to the Central Vietnamese context, two qualitative studies and one community survey were undertaken. Associations between maternal mental health and infant health outcomes were also explored. The study was comprised of three phases. Firstly, iterative, qualitative interviews with Vietnamese health professionals (n = 17) and postpartum women (n = 15) were conducted and analysed using Kleinman's theory of explanatory models to identify narratives surrounding PND in the Vietnamese context (Kleinman, 1978). Secondly, a participatory concept mapping exercise was undertaken with two groups of health professionals (n = 12) to explore perceived risk and protective factors for postnatal mental health. Qualitative phases of the research elucidated narratives surrounding maternal mental health in the Vietnamese context such as son preference, use of traditional medicines, and the popularity of confinement practices such as having one to three months of complete rest. The qualitative research also revealed the construct of depression was not widely recognised. Rather, postpartum changes in mood were conceptualised as a loss of 'vital strength' following childbirth or 'disappointment'. Most women managed postpartum changes in mood within the family although some sought help from traditional medicine practitioners or biomedical doctors. Thirdly, a cross-sectional study of twelve randomly selected communes (six urban, six rural) in Thua Thien Hue Province was then conducted. Overall, 465 women with infants between 4 weeks and six months old participated, and 431 questionnaires were analysed. Women from urban (n = 216) and rural (n = 215) areas participated. All eligible women completed a structured interview about their health, basic demographics, and social circumstances. Maternal depression was measured using the Edinburgh Postnatal Depression Scale (EPDS) as a continuous variable. Multivariate generalised linear regression was conducted using PASW Statistics version 18.0 (2009). When using the conventional EPDS threshold for probable depression (EPDS score ~ 13) 18.1% (n = 78) of women were depressed (Gibson, McKenzie-McHarg, Shakespeare, Price & Gray, 2009). Interestingly, 20.4% of urban women (n = 44) had EPDS scores~ 13, which was a higher proportion than rural women, where 15.8% (n = 34) had EPDS scores ~ 13, although this difference was not statistically significant: t(429) = -0.689, p = 0.491. Whilst qualitative narratives identified infant gender and family composition, and traditional confinement practices as relevant to postnatal mood, these were not statistically significant in multivariate analysis. Rather, poverty, food security, being frightened of your husband or family members, experiences of intimate partner violence and breastfeeding difficulties had strong statistical associations. PND was also associated with having an infant with diarrhoea in the past two weeks, but not infant malnutrition or acute respiratory infections. This study is the first to explore maternal mental health in Central Vietnam, and provides further evidence that PND is a universally experienced phenomenon. The independent social risk factors of depressive symptoms identified such as poverty, food insecurity, experiences of violence and powerlessness, and relationship adversity points to women in a context of social suffering which is relevant throughout the world (Kleinman, Das & Lock, 1997). The culturally specific risk factors explored such as infant gender were not statistically significant when included in a multivariable model. However, they feature prominently in qualitative narratives surrounding PND in Vietnam, both in this study and previous literature. It appears that whilst infant gender may not be associated with PND per se, the reactions of close relatives to the gender of the baby can adversely affect maternal wellbeing. This study used a community based participatory research approach (CBPR) (Israel.2005). This approach encourages the knowledge produced to be used for public health interventions and workforce training in the community in which the research was conducted, and such work has commenced. These results suggest that packages of interventions for LMIC devised to address maternal mental health and infant wellbeing could be applied in Central Vietnam. Such interventions could include training lay workers to follow up postpartum women, and incorporating mental health screening and referral into primary maternal and child health care (Pate! et al., 2011; Rahman, Malik, Sikander & Roberts, 2008). Addressing the underlying social determinants of PND through poverty reduction and violence elimination programs is also recommended.

Speaker attribution of Australian broadcast news data

Speaker attribution is the task of annotating a spoken audio archive based on speaker identities. This can be achieved using speaker diarization and speaker linking. In our previous work, we proposed an efficient attribution system, using complete-linkage clustering, for conducting attribution of large sets of two-speaker telephone data. In this paper, we build on our proposed approach to achieve a robust system, applicable to multiple recording domains. To do this, we first extend the diarization module of our system to accommodate multi-speaker (>2) recordings. We achieve this through using a robust cross-likelihood ratio (CLR) threshold stopping criterion for clustering, as opposed to the original stopping criterion of two speakers used for telephone data. We evaluate this baseline diarization module across a dataset of Australian broadcast news recordings, showing a significant lack of diarization accuracy without previous knowledge of the true number of speakers within a recording. We thus propose applying an additional pass of complete-linkage clustering to the diarization module, demonstrating an absolute improvement of 20% in diarization error rate (DER). We then evaluate our proposed multi-domain attribution system across the broadcast news data, demonstrating achievable attribution error rates (AER) as low as 17%.