N-acetyl cysteine for depressive symptoms in bipolar disorder - a double blind, randomized, placebo-controlled trial

Background: Treatment-resistant subthreshold depression is a major problem in bipolar disorder. Both depression and bipolar disorderare complicated by glutathione depletion. We hypothesized that treatment with N-acetyl cysteine (NAC), a safe, orally bioavailable precursor of glutathione, may improve the depressive component of bipolar disorder.

Methods: A randomized, double-blind, multicenter, placebo-controlled study of individuals (n 75) with bipolar disorder in the maintenance phase treated with NAC (1 g twice daily) adjunctive to usual medication over 24 weeks, with a 4-week washout. The two primary outcomes were the Montgomery Asberg Depression Rating Scale (MADRS) and time to a mood episode. Secondary outcomes included the Bipolar Depression Rating Scale and 11 other ratings of clinical status, quality of life, and functioning.

Results: NAC treatment caused a significant improvement on the MADRS (least squares mean difference [95% confidence interval]: 8.05 [13.16, 2.95], p .002) a n d most secondary scales at end point. Benefit was evident by 8 weeks on the Global Assessment of Functioning Scale and Social and Occupational Functioning Assessment Scale and at 20 weeks on the MADRS. Improvements were lost after washout. There was no effect of NAC on time to a mood episode (log-rank test: p .968) and no significant between-group differences inadverse events. Effect sizes at end point were medium to high for improvements in MADRS and 9 of the 12 secondary readouts.

Conclusions: NAC appears a safe and effective augmentation strategy for depressive symptoms in bipolar  disorder.

Gender differences in publication output: towards an unbiased metric of research performance

We examined the publication records of a cohort of 168 life scientists in the field of ecology and evolutionary biology to assess gender differences in research performance. Clear discrepancies in publication rate between men and women appear very early in their careers and this has consequences for the subsequent citation of their work. We show that a recently proposed index designed to rank scientists fairly is in fact strongly biased against female researchers, and advocate a modified index to assess men and women on a more equitable basis.

Towards a southern theory of higher education

Crudely, social inclusion in Australian higher education is a numbers game. While the student recruitment departments of universities focus on ‘bums on seats’, equity advocates draw attention to ‘which bums’, in ‘what proportions’, and, more to the point, ‘which seats’, ‘where’. But if the counting of bums is crude, so is the differentiation of seats. Just distinguishing between courses and universities and scrutinizing the distribution of groups, is a limited view of equity. The most prestigious seats of learning give students access primarily to dominant forms of knowledge and ways of thinking. In terms of access, it is to a diminished higher education, for all. Further, undergraduates – particularly in their first year – are rarely credited with having much to contribute. Higher education is the poorer for it. In this paper I propose an expanded conception for social inclusion and an enlarged regard for what is being accessed by students who gain entry to university. Drawing on Connell’s conception of ‘Southern Theory’, I highlight power/knowledge relations in higher education and particularly ‘southerners’: those under‐represented in universities – often located south of ENTER (Equivalent National Tertiary Entrance Rank) cut‐offs – and whose cultural capital is similarly marginalised and discounted. While increasing regard for the importance of Indigenous knowledges is beginning to challenge the norms of higher education, we are yet to generalise such reconceptions of epistemology to include knowledges particular to people from regional and rural areas, with disabilities, and from low socioeconomic backgrounds. Nor have we really engaged with different ways of thinking about the physical and social worlds that are particular to these groups. To take account of marginalized forms of knowledge and of thinking will mean thinking differently about what higher education is and how it gets done.

Targeting and measuring housing problems in developing countries through urban texture analysis

Latin-american countries passed from predominantely rural to predominantely urban within few decades. The level of urbanisation in Brazil progressed from 36% in 1950, 50% in 1970, and scalating to 85% in 2005. This rapid transformation resulted in many social problems, as cities were not able to provide appropriate housing and infrastructure for the growing population. As a response, the Brazilian Ministry for Cities, in 2005, created the National System for Social Housing, with the goal to establish guidelines in the Federal level, and build capacity and fund social housing projects in the State and Local levels. This paper presents a research developed in Gramado city, Brazil, as part of the Local Social Housing Plan process, with the goal to produce innovative tools to help social housing planning and management. It proposes and test a methodology to locate and characterise/rank housing defficiencies across the city combining GIS and fractal geometry analysis. Fractal measurements, such as fractal dimension and lacunarity, are able to differentiate urban morphology, and integrated to infrastructure and socio-economical spatial indicators, they can be used to estimate housing problems and help to target, classify and schedule actions to improve housing in cities and regions. Gramado city was divided in a grid with 1,000 cells. For each cell, the following indicators were measured: average income of households, % of roads length which are paved (as a proxy for availability of infrastructures as water and sewage), fractal dimension and lacunarity of the dwellings spatial distribution. A statistical model combining those measurements was produced using a sample of 10% of the cells divided in five housing standards (from high income/low density dwellings to slum's dwellings). The estimation of the location and level of social housing deficiencies in the whole region using the model, compared to the real situation, achived high correlations. Simple and based on easily accessible and inexpensive data, the method also helped to overcome limitations of lack of information and fragmented knowledge of the area related to housing conditions by local professionals.

The long-term predictive validity of the consumers' need for uniqueness scale

This research examines the long-term ability of the consumers’ need for uniqueness measure to predict consumers’ valued possessions, shopping behaviors, and experiences. Consumers’ need for uniqueness reflects individual differences in counterconformity motivation (i.e., the pursuit of differentness relative to others) that is manifest in consumer responses. This research reports on 2 longitudinal assessments of the scale's predictive validity using data collected at 4 stages from a consumer mail-survey sample. One year following initial responses to the trait measure, respondents participated in a second survey in which they photographed and rank ordered their valued possessions. In a third survey, the photographs served as cues to obtain responses to ratings of possession benefits and to questions regarding acquisition of the possession. Overall, the results support a number of hypotheses regarding the relation of consumers’ need for uniqueness to salient possession benefits, types of goods comprising consumers’ valued possessions, and shopping behaviors associated with acquiring valued possessions. Two years following initial responses to the trait measure, members of the consumer mail panel completed a fourth survey, indicating their participation in various uniqueness-enhancing consumer activities. Results support the stability and predictive validity of the measure over the 2-year period. Because the design of possessions and endorsed uniqueness-enhancing activities considered in the consumer mail panel studies varied across individuals, an additional study was conducted that demonstrated the scale's ability to predict individuals’ counterconformity responses to the same choice situation.

Generating comprehensible summaries of rushes sequences based on robust feature matching

This paper describes our first attempt at tackling a pilot task in Trecvid: video summarization of rushes data [3]. Our method is based on the tight clustering produced via SIFT matching. In this first attempt, we try to examine how our approach performs without complex implementation in terms of concept detection and excerpt assembly (i.e, no picture-in-picture, split screen and special transitions). Although we do not perform very well in terms of concept inclusion, we rank very well in terms of the summary being easy to understand and relevancy of included segments.

Probabilistic models over ordered partitions with applications in document ranking and collaborative filtering

Ranking is an important task for handling a large amount of content. Ideally, training data for supervised ranking would include a complete rank of documents (or other objects such as images or videos) for a particular query. However, this is only possible for small sets of documents. In practice, one often resorts to document rating, in that a subset of documents is assigned with a small number indicating the degree of relevance. This poses a general problem of modelling and learning rank data with ties. In this paper, we propose a probabilistic generative model, that models the process as permutations over partitions. This results in super-exponential combinatorial state space with unknown numbers of partitions and unknown ordering among them. We approach the problem from the discrete choice theory, where subsets are chosen in a stagewise manner, reducing the state space per each stage significantly. Further, we show that with suitable parameterisation, we can still learn the models in linear time. We evaluate the proposed models on two application areas: (i) document ranking with the data from the recently held Yahoo! challenge, and (ii) collaborative filtering with movie data. The results demonstrate that the models are competitive against well-known rivals.

Predicting landscape-genetic consequences of habitat loss, fragmentation and mobility for multiple species of woodland birds

Inference concerning the impact of habitat fragmentation on dispersal and gene flow is a key theme in landscape genetics. Recently, the ability of established approaches to identify reliably the differential effects of landscape structure (e.g. land-cover composition, remnant vegetation configuration and extent) on the mobility of organisms has been questioned. More explicit methods of predicting and testing for such effects must move beyond post hoc explanations for single landscapes and species. Here, we document a process for making a priori predictions, using existing spatial and ecological data and expert opinion, of the effects of landscape structure on genetic structure of multiple species across replicated landscape blocks. We compare the results of two common methods for estimating the influence of landscape structure on effective distance: least-cost path analysis and isolation-by-resistance. We present a series of alternative models of genetic connectivity in the study area, represented by different landscape resistance surfaces for calculating effective distance, and identify appropriate null models. The process is applied to ten species of sympatric woodland-dependant birds. For each species, we rank a priori the expectation of fit of genetic response to the models according to the expected response of birds to loss of structural connectivity and landscape-scale tree-cover. These rankings (our hypotheses) are presented for testing with empirical genetic data in a subsequent contribution. We propose that this replicated landscape, multi-species approach offers a robust method for identifying the likely effects of landscape fragmentation on dispersal.

A new re-ranking method using enhanced pseudo-relevance feedback for content-based medical image retrieval

We propose a novel re-ranking method for content-based medical image retrieval based on the idea of pseudo-relevance feedback (PRF). Since the highest ranked images in original retrieval results are not always relevant, a naive PRF based re-ranking approach is not capable of producing a satisfactory result. We employ a two-step approach to address this issue. In step 1, a Pearson's correlation coefficient based similarity update method is used to re-rank the high ranked images. In step 2, after estimating a relevance probability for each of the highest ranked images, a fuzzy SVM ensemble based approach is adopted to re-rank the images. The experiments demonstrate that the proposed method outperforms two other re-ranking methods.

Preliminary investigation of the responsiveness of the Melbourne low vision ADL index to low-vision rehabilitation

Purpose. To conduct a preliminary investigation on the ability of the Melbourne Low Vision ADL Index to detect changes in functional ability as a result of low-vision rehabilitation.

Methods. Twenty two subjects with age-related macular degeneration (ARMD) who were newly referred to the Kooyong Low Vision Clinic were recruited. The Melbourne Low Vision ADL Index was administered prerehabilitation and postrehabilitation. Changes in scores and effect size statistics were analyzed.

Results. The median total score for the subjects prerehabilitation was 67, and the median total score postrehabilitation was 76. The difference in prerehabilitation and postrehabilitation scores was statistically significant (Wilcoxon signed rank test = 248.5, p < 0.001). The mean change score for the total Melbourne Low Vision ADL Index was 9.3 (SD, 5.6). Thus the overall effect size statistic (mean change score divided by SD of prerehabilitation score) was 0.78.

Conclusions. This preliminary investigation indicates that the Melbourne Low Vision ADL Index is responsive to a rehabilitation program for patients with ARMD. It has potential to be used as a measure of low-vision rehabilitation outcomes.

Small within-day increases in temperature affects boldness and alters personality in coral reef fish

Consistent individual differences in behaviour, termed personality, are common in animal populations and can constrain their responses to ecological and environmental variation, such as temperature. Here, we show for the first time that normal within-daytime fluctuations in temperature of less than 3°C have large effects on personality for two species of juvenile coral reef fish in both observational and manipulative experiments. On average, individual scores on three personality traits (PTs), activity, boldness and aggressiveness, increased from 2.5- to sixfold as a function of temperature. However, whereas most individuals became more active, aggressive and bold across temperature contexts (were plastic), others did not; this changed the individual rank order across temperatures and thus altered personality. In addition, correlations between PTs were consistent across temperature contexts, e.g. fish that were active at a given temperature also tended to be both bold and aggressive. These results (i) highlight the importance of very carefully controlling for temperature when studying behavioural variation among and within individuals and (ii) suggest that individual differences in energy metabolism may contribute to animal personality, given that temperature has large direct effects on metabolic rates in ectotherms.

A guided rule reduction system for prioritization of failures in fuzzy FMEA

Traditional Failure Mode and Effect Analysis (FMEA) utilizes the Risk Priority Number (RPN) ranking system to evaluate the risk level of failures, to rank failures, and to prioritize actions. Although this method is simple, it suffers from several shortcomings. In this paper, use of fuzzy inference techniques for RPN determination in an attempt to overcome the weaknesses associated with the traditional RPN ranking system is investigated. However, the fuzzy RPN model, suffers from the combinatorial rule explosion problem. As a result, a generic rule reduction approach, i.e. the Guided Rule Reduction System (GRRS), is proposed to reduce the number of rules that need to be provided by users during the fuzzy RPN modeling process. The proposed approach is evaluated using real-world case studies pertaining to semiconductor manufacturing. The results are analyzed, and implications of the proposed approach are discussed.

Enhancing the failure mode and effect analysis methodology with fuzzy inference techniques

Traditional Failure Mode and Effect Analysis (FMEA) adopts the Risk Priority Number (RPN) ranking model to evaluate failure risks, to rank failures, as well as to prioritize actions. Although this approach is simple, it suffers from several shortcomings. In this paper, we investigate a number of fuzzy inference techniques for determining the RPN scores, in an attempt to overcome the weaknesses associated with the traditional RPN model. The main objective is to examine the possibility of using fuzzy rule interpolation and reduction techniques to design new fuzzy RPN models. The performance of the fuzzy RPN models is evaluated using a real-world case study pertaining to the test handler process in a semiconductor manufacturing plant. The FMEA procedure for the test handler is performed, and a fuzzy RPN model is developed. In addition, improvement to the fuzzy RPN model is proposed by refining the weights of the fuzzy production rules, hence a new weighted fuzzy RPN model. The ability of the weighted fuzzy RPN model in failure risk evaluation with a reduced rule base is also demonstrated.

Existence conditions for unknown input functional observers

This article presents necessary and sufficient conditions for the existence and design of an unknown input Functional observer. The existence of the observer can be verified by computing a nullspace of a known matrix and testing some matrix rank conditions. The existence of the observer does not require the satisfaction of the observer matching condition (i.e. Equation (16) in Hou and Muller 1992, ‘Design of Observers for Linear Systems with Unknown Inputs’, IEEE Transactions on Automatic Control, 37, 871–875), is not limited to estimating scalar functionals and allows for arbitrary pole placement. The proposed observer always exists when a state observer exists for the unknown input system, and furthermore, the proposed observer can exist even in some instances when an unknown input state observer does not exist.

The langauge of tourism : perfecting soft skills in a flat world

India is Australia's 11th-biggest inbound tourism market, bringing in 148,200 visitors who spent $867 million last year, but by 2020 officials say that could reach 300,000 visitors spending $2.3 billion.
Delhi and Mumbai have been targeted by Australia because they have an emerging middle class and India's highest concentration of affluent households.
The Minister for Tourism, Martin Ferguson, unveiled an India 2020 strategic plan last month at the annual Australian Tourism Exchange in Perth, the largest travel trade show in the southern hemisphere. "We have put a huge effort into attracting tourists from China recently and the next cab off the rank is India," he said.
The plan means that Tourism Australia's "There's Nothing Like Australia" campaign will be rolled out in Delhi and Mumbai and there will be extensive advertising on TV and digital channels as well as print.