Analysis of differential gene expression based on Bayesian estimation of variance

Gene expression is arguably the most important indicator of biological function. Thus identifying differentially expressed genes is one of the main aims of high throughout studies that use microarray and RNAseq platforms to study deregulated cellular pathways. There are many tools for analysing differentia gene expression from transciptomic datasets. The major challenge of this topic is to estimate gene expression variance due to the high amount of ‘background noise’ that is generated from biological equipment and the lack of biological replicates. Bayesian inference has been widely used in the bioinformatics field. In this work, we reveal that the prior knowledge employed in the Bayesian framework also helps to improve the accuracy of differential gene expression analysis when using a small number of replicates. We have developed a differential analysis tool that uses Bayesian estimation of the variance of gene expression for use with small numbers of biological replicates. Our method is more consistent when compared to the widely used cyber-t tool that successfully introduced the Bayesian framework to differential analysis. We also provide a user-friendly web based Graphic User Interface for biologists to use with microarray and RNAseq data. Bayesian inference can compensate for the instability of variance caused when using a small number of biological replicates by using pseudo replicates as prior knowledge. We also show that our new strategy to select pseudo replicates will improve the performance of the analysis. - See more at: http://www.eurekaselect.com/node/138761/article#sthash.VeK9xl5k.dpuf

A Bayesian Framework for the Assessment of Vision-based Weed and Fruit Detection and Classification Algorithms

This paper proposes new metrics and a performance-assessment framework for vision-based weed and fruit detection and classification algorithms. In order to compare algorithms, and make a decision on which one to use fora particular application, it is necessary to take into account that the performance obtained in a series of tests is subject to uncertainty. Such characterisation of uncertainty seems not to be captured by the performance metrics currently reported in the literature. Therefore, we pose the problem as a general problem of scientific inference, which arises out of incomplete information, and propose as a metric of performance the(posterior) predictive probabilities that the algorithms will provide a correct outcome for target and background detection. We detail the framework through which these predicted probabilities can be obtained, which is Bayesian in nature. As an illustration example, we apply the framework to the assessment of performance of four algorithms that could potentially be used in the detection of capsicums (peppers).

Efficient estimation for rank-based regression with clustered data

Rank-based inference is widely used because of its robustness. This article provides optimal rank-based estimating functions in analysis of clustered data with random cluster effects. The extensive simulation studies carried out to evaluate the performance of the proposed method demonstrate that it is robust to outliers and is highly efficient given the existence of strong cluster correlations. The performance of the proposed method is satisfactory even when the correlation structure is misspecified, or when heteroscedasticity in variance is present. Finally, a real dataset is analyzed for illustration.

Nonparametric rank regression for analyzing water quality concentration data with multiple detection limits

Environmental data usually include measurements, such as water quality data, which fall below detection limits, because of limitations of the instruments or of certain analytical methods used. The fact that some responses are not detected needs to be properly taken into account in statistical analysis of such data. However, it is well-known that it is challenging to analyze a data set with detection limits, and we often have to rely on the traditional parametric methods or simple imputation methods. Distributional assumptions can lead to biased inference and justification of distributions is often not possible when the data are correlated and there is a large proportion of data below detection limits. The extent of bias is usually unknown. To draw valid conclusions and hence provide useful advice for environmental management authorities, it is essential to develop and apply an appropriate statistical methodology. This paper proposes rank-based procedures for analyzing non-normally distributed data collected at different sites over a period of time in the presence of multiple detection limits. To take account of temporal correlations within each site, we propose an optimal linear combination of estimating functions and apply the induced smoothing method to reduce the computational burden. Finally, we apply the proposed method to the water quality data collected at Susquehanna River Basin in United States of America, which dearly demonstrates the advantages of the rank regression models.

Smooth bootstrap methods for analysis of longitudinal data

In analysis of longitudinal data, the variance matrix of the parameter estimates is usually estimated by the 'sandwich' method, in which the variance for each subject is estimated by its residual products. We propose smooth bootstrap methods by perturbing the estimating functions to obtain 'bootstrapped' realizations of the parameter estimates for statistical inference. Our extensive simulation studies indicate that the variance estimators by our proposed methods can not only correct the bias of the sandwich estimator but also improve the confidence interval coverage. We applied the proposed method to a data set from a clinical trial of antibiotics for leprosy.

Comparative analysis of traffic state estimation ─ Cumulative counts-based and trajectory-based methods

The Macroscopic Fundamental Diagram (MFD) relates space-mean density and flow. Since the MFD represents the area-wide network traffic performance, studies on perimeter control strategies and network-wide traffic state estimation utilising the MFD concept have been reported. Most previous works have utilised data from fixed sensors, such as inductive loops, to estimate the MFD, which can cause biased estimation in urban networks due to queue spillovers at intersections. To overcome the limitation, recent literature reports the use of trajectory data obtained from probe vehicles. However, these studies have been conducted using simulated datasets; limited works have discussed the limitations of real datasets and their impact on the variable estimation. This study compares two methods for estimating traffic state variables of signalised arterial sections: a method based on cumulative vehicle counts (CUPRITE), and one based on vehicles’ trajectory from taxi Global Positioning System (GPS) log. The comparisons reveal some characteristics of taxi trajectory data available in Brisbane, Australia. The current trajectory data have limitations in quantity (i.e., the penetration rate), due to which the traffic state variables tend to be underestimated. Nevertheless, the trajectory-based method successfully captures the features of traffic states, which suggests that the trajectories from taxis can be a good estimator for the network-wide traffic states.

Promoting articulated action from diverse stakeholders in response to public policy scenarios: A case analysis of the use of ‘scenario improvisation’ method

In this paper we present a novel application of scenario methods to engage a diverse constituency of senior stakeholders, with limited time availability, in debate to inform planning and policy development. Our case study project explores post-carbon futures for the Latrobe Valley region of the Australian state of Victoria. Our approach involved initial deductive development of two ‘extreme scenarios’ by a multi-disciplinary research team, based upon an extensive research programme. Over four workshops with the stakeholder constituency, these initial scenarios were discussed, challenged, refined and expanded through an inductive process, whereby participants took ‘ownership’ of a final set of three scenarios. These were both comfortable and challenging to them. The outcomes of this process subsequently informed public policy development for the region. Whilst this process did not follow a single extant structured, multi-stage scenario approach, neither was it devoid of form. Here, we seek to theorise and codify elements of our process – which we term ‘scenario improvisation’ – such that others may adopt it.

A subculture of mental toughness in an Australian Football League club

Objective The current study aimed to provide a subcultural analysis of mental toughness in a high-performance context in sport. Design Using Schein's (1990) framework of organisational culture, an exploratory qualitative analysis, employing focus group and individual interviews, was used to investigate mental toughness in an Australian Football League club. Method Nine senior coaches and players participated in focus group and individual interviews. Photo elicitation was used as a method to capture mental toughness through the identification of prominent club artefacts. Participants were considered to have significant subcultural knowledge of their football club and were willing to describe personal experiences and perceptions of mental toughness through this cultural lens. Deductive and inductive analyses were conducted to capture the core themes of mental toughness across the disparate levels of Schein's organisational framework. Results Mental toughness was found to be a socially derived term marked by unrelenting standards and sacrificial displays. These acts were underpinned by subcultural values emphasising a desire for constant improvement, a team first ethos, relentless effort, and the maintenance of an infallible image. At its core, mental toughness was assumed to be an internal concept, epitomised an idealised form of masculinity, elitist values, and was rhetorically depicted through metaphors of war. Conclusions It may be difficult to understand mental toughness without giving attention to the contextual norms related to the term. Appreciating how people promote, instil, and internalise prized ideals coveted as mental toughness could be intriguing for future research in sport psychology.

“We have to work within the system!”: Staff perceptions of organizational barriers to decision making for older adults with dementia in Australian aged care facilities

The current study explored the perceptions of direct care staff working in Australian residential aged care facilities (RACFs) regarding the organizational barriers that they believe prevent them from facilitating decision making for individuals with dementia. Normalization process theory (NPT) was used to interpret the findings to understand these barriers in a broader context. The qualitative study involved semi-structured interviews (N = 41) and focus groups (N = 8) with 80 direct care staff members of all levels working in Australian RACFs. Data collection and analysis were conducted in parallel and followed a systematic, inductive approach in line with grounded theory. The perceptions of participants regarding the organizational barriers to facilitating decision making for individuals with dementia can be described by the core category, Working Within the System, and three sub-themes: (a) finding time, (b) competing rights, and (c)not knowing. Examining the views of direct care staff through the lens of NPT allows possible areas for improvement to be identified at an organizational level and the perceived barriers to be understood in the context of promoting normalization of decision making for individuals with dementia.

Accelerating Pseudo-Marginal MCMC using Gaussian Processes

Pseudo-marginal methods such as the grouped independence Metropolis-Hastings (GIMH) and Markov chain within Metropolis (MCWM) algorithms have been introduced in the literature as an approach to perform Bayesian inference in latent variable models. These methods replace intractable likelihood calculations with unbiased estimates within Markov chain Monte Carlo algorithms. The GIMH method has the posterior of interest as its limiting distribution, but suffers from poor mixing if it is too computationally intensive to obtain high-precision likelihood estimates. The MCWM algorithm has better mixing properties, but less theoretical support. In this paper we propose to use Gaussian processes (GP) to accelerate the GIMH method, whilst using a short pilot run of MCWM to train the GP. Our new method, GP-GIMH, is illustrated on simulated data from a stochastic volatility and a gene network model.

Sampling Methods for Exploring Between Subject Variability in Cardiac Electrophysiology Experiments

Between-subject and within-subject variability is ubiquitous in biology and physiology and understanding and dealing with this is one of the biggest challenges in medicine. At the same time it is difficult to investigate this variability by experiments alone. A recent modelling and simulation approach, known as population of models (POM), allows this exploration to take place by building a mathematical model consisting of multiple parameter sets calibrated against experimental data. However, finding such sets within a high-dimensional parameter space of complex electrophysiological models is computationally challenging. By placing the POM approach within a statistical framework, we develop a novel and efficient algorithm based on sequential Monte Carlo (SMC). We compare the SMC approach with Latin hypercube sampling (LHS), a method commonly adopted in the literature for obtaining the POM, in terms of efficiency and output variability in the presence of a drug block through an in-depth investigation via the Beeler-Reuter cardiac electrophysiological model. We show improved efficiency via SMC and that it produces similar responses to LHS when making out-of-sample predictions in the presence of a simulated drug block.

Perceptions on the causes of individual and fraudulent co-offending: Views of forensic accountants

Individual and/or co-offenders fraudulent activities can have a devastating effect on a company’s reputation and credibility. Enron, Xerox, WorldCom, HIH Insurance and One.Tel are examples where stakeholders incurred substantial financial losses as a result of fraud and led to a loss of confidence in corporate dealings by the public in general. There are numerous theoretical approaches that attempt to explain how and why fraudulent acts occur, drawing on the fields of sociology, organisational, management and economic literature, but there is limited empirical evidence published in accounting literature. This qualitative inductive study analyses perceptions and experiences of forensic accountants to gain insights into individual fraud and co-offending in order to determine whether the conceptual framework developed from literature accurately depicts the causes of fraud committed by individuals and groups in the twenty-first century. Findings from the study both support and extend the conceptual framework, demonstrating that strain and anomie can result in fraud, that deviant sub-groups recruit and coerce members by providing relief from strain, and that inadequate corporate governance mechanisms both contribute to fraud occurring, and provide the opportunity for fraudulent activities to be executed and often remain undetected. Additional factors emerging from this study (the ‘technoconomy’, addiction and IT measures) were also identified as contributors to fraud, particularly relevant to the twenty-first century, and consequently, a refined conceptual framework is presented in the discussion and conclusion to the paper.

Preservice early childhood teachers and designers partnering for change at The Lone Pine Koala Sanctuary: An interdisciplinary education and design for sustainability project

This presentation discusses and critiques a current case study of a project in which Early Childhood preservice teachers are working in partnership with Design students to develop principles and concepts for the design and construction of an early childhood centre. This centre, to be built on the grounds of the iconic Lone Pine Koala Sanctuary in Brisbane , focuses on Education for Sustainability (EfS), sustainable design and sustainable business. Interdisciplinary initiatives between QUT staff and students from two Faculties (Education and Creative Industries) have been situated in the real –world context of this project. This practical, authentic project has seen stakeholders take an interdisciplinary approach to sustainability, opening up new ways of thinking about early childhood centre design, particularly with respect to operation and function. Interdisciplinarity and a commitment to genuine partnerships have created intellectual spaces to re-think the potential of the disciplines to be interwoven so that future professionals from different fields might come together to learn from each other and to address the sustainability imperative. The case study documents and explores the possibilities that the Lone Pine project offers for academics and students from Early Childhood and Design to collaboratively inform the Sanctuary’s vision for the Centre. The research examines how students benefit from practical, real world, community-integrated learning; how academic staff across two disciplines are able to work collaboratively within a real-world context; and how external stakeholders experience and benefit from the partnership with university staff and students. Data were collected via a series of focus group and individual interviews designed to explore how the various stakeholders (staff, students, business partners) experienced their involvement in the interdisciplinary project. Inductive and deductive thematic analysis of these data suggest many benefits for participants as well as a number of challenges. Findings suggest that the project has provided students with ‘real world’ partnerships that reposition early childhood students’ identities from ‘novice’ to ‘professional’, where their knowledge, expertise and perspectives are simultaneously validated and challenged in their work with designers. These partnerships are enabling preservice teachers to practice a new model of early childhood leadership in sustainability, one that is vital for leading for change in an increasingly complex world. This presentation celebrates, critiques and problematises this project, exploring wider implications for other contexts in which university staff and students may seek to work across traditional boundaries, thus building partnerships for change.

Dynamic Bayesian network inferencing for non-homogeneous complex systems

Dynamic Bayesian Networks (DBNs) provide a versatile platform for predicting and analysing the behaviour of complex systems. As such, they are well suited to the prediction of complex ecosystem population trajectories under anthropogenic disturbances such as the dredging of marine seagrass ecosystems. However, DBNs assume a homogeneous Markov chain whereas a key characteristics of complex ecosystems is the presence of feedback loops, path dependencies and regime changes whereby the behaviour of the system can vary based on past states. This paper develops a method based on the small world structure of complex systems networks to modularise a non-homogeneous DBN and enable the computation of posterior marginal probabilities given evidence in forwards inference. It also provides an approach for an approximate solution for backwards inference as convergence is not guaranteed for a path dependent system. When applied to the seagrass dredging problem, the incorporation of path dependency can implement conditional absorption and allows release from the zero state in line with environmental and ecological observations. As dredging has a marked global impact on seagrass and other marine ecosystems of high environmental and economic value, using such a complex systems model to develop practical ways to meet the needs of conservation and industry through enhancing resistance and/or recovery is of paramount importance.

Pre-service early childhood teachers and designers partnering for change at The Lone Pine Koala Sanctuary: An interdisciplinary education and design for sustainability project

This presentation discusses and critiques a current case study of a project in which Early Childhood preservice teachers are working in partnership with Design students to develop principles and concepts for the design and construction of an early childhood centre. This centre, to be built on the grounds of the iconic Lone Pine Koala Sanctuary in Brisbane , focuses on Education for Sustainability (EfS), sustainable design and sustainable business. Interdisciplinary initiatives between QUT staff and students from two Faculties (Education and Creative Industries) have been situated in the real –world context of this project. This practical, authentic project has seen stakeholders take an interdisciplinary approach to sustainability, opening up new ways of thinking about early childhood centre design, particularly with respect to operation and function. Interdisciplinarity and a commitment to genuine partnerships have created intellectual spaces to re-think the potential of the disciplines to be interwoven so that future professionals from different fields might come together to learn from each other and to address the sustainability imperative. The case study documents and explores the possibilities that the Lone Pine project offers for academics and students from Early Childhood and Design to collaboratively inform the Sanctuary’s vision for the Centre. The research examines how students benefit from practical, real world, community-integrated learning; how academic staff across two disciplines are able to work collaboratively within a real-world context; and how external stakeholders experience and benefit from the partnership with university staff and students. Data were collected via a series of focus group and individual interviews designed to explore how the various stakeholders (staff, students, business partners) experienced their involvement in the interdisciplinary project. Inductive and deductive thematic analysis of these data suggest many benefits for participants as well as a number of challenges. Findings suggest that the project has provided students with ‘real world’ partnerships that reposition early childhood students’ identities from ‘novice’ to ‘professional’, where their knowledge, expertise and perspectives are simultaneously validated and challenged in their work with designers. These partnerships are enabling preservice teachers to practice a new model of early childhood leadership in sustainability, one that is vital for leading for change in an increasingly complex world. This presentation celebrates, critiques and problematises this project, exploring wider implications for other contexts in which university staff and students may seek to work across traditional boundaries, thus building partnerships for change.