Using a Bayesian network to model colonisation with Vancomycin Resistant Enterococcus (VRE)

Objective: Effective management of multi-resistant organisms is an important issue for hospitals both in Australia and overseas. This study investigates the utility of using Bayesian Network (BN) analysis to examine relationships between risk factors and colonization with Vancomycin Resistant Enterococcus (VRE). Design: Bayesian Network Analysis was performed using infection control data collected over a period of 36 months (2008-2010). Setting: Princess Alexandra Hospital (PAH), Brisbane. Outcome of interest: Number of new VRE Isolates Methods: A BN is a probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph (DAG). BN enables multiple interacting agents to be studied simultaneously. The initial BN model was constructed based on the infectious disease physician‟s expert knowledge and current literature. Continuous variables were dichotomised by using third quartile values of year 2008 data. BN was used to examine the probabilistic relationships between VRE isolates and risk factors; and to establish which factors were associated with an increased probability of a high number of VRE isolates. Software: Netica (version 4.16). Results: Preliminary analysis revealed that VRE transmission and VRE prevalence were the most influential factors in predicting a high number of VRE isolates. Interestingly, several factors (hand hygiene and cleaning) known through literature to be associated with VRE prevalence, did not appear to be as influential as expected in this BN model. Conclusions: This preliminary work has shown that Bayesian Network Analysis is a useful tool in examining clinical infection prevention issues, where there is often a web of factors that influence outcomes. This BN model can be restructured easily enabling various combinations of agents to be studied.

Automatic object segmentation of unstructured scenes using colour and depth maps

This study presents a segmentation pipeline that fuses colour and depth information to automatically separate objects of interest in video sequences captured from a quadcopter. Many approaches assume that cameras are static with known position, a condition which cannot be preserved in most outdoor robotic applications. In this study, the authors compute depth information and camera positions from a monocular video sequence using structure from motion and use this information as an additional cue to colour for accurate segmentation. The authors model the problem similarly to standard segmentation routines as a Markov random field and perform the segmentation using graph cuts optimisation. Manual intervention is minimised and is only required to determine pixel seeds in the first frame which are then automatically reprojected into the remaining frames of the sequence. The authors also describe an automated method to adjust the relative weights for colour and depth according to their discriminative properties in each frame. Experimental results are presented for two video sequences captured using a quadcopter. The quality of the segmentation is compared to a ground truth and other state-of-the-art methods with consistently accurate results.

GrabCutSFM : how 3D information improves unsupervised object segmentation

In this paper, we present an unsupervised graph cut based object segmentation method using 3D information provided by Structure from Motion (SFM), called Grab- CutSFM. Rather than focusing on the segmentation problem using a trained model or human intervention, our approach aims to achieve meaningful segmentation autonomously with direct application to vision based robotics. Generally, object (foreground) and background have certain discriminative geometric information in 3D space. By exploring the 3D information from multiple views, our proposed method can segment potential objects correctly and automatically compared to conventional unsupervised segmentation using only 2D visual cues. Experiments with real video data collected from indoor and outdoor environments verify the proposed approach.

Vision-only autonomous navigation using topometric maps

This paper presents a mapping and navigation system for a mobile robot, which uses vision as its sole sensor modality. The system enables the robot to navigate autonomously, plan paths and avoid obstacles using a vision based topometric map of its environment. The map consists of a globally-consistent pose-graph with a local 3D point cloud attached to each of its nodes. These point clouds are used for direction independent loop closure and to dynamically generate 2D metric maps for locally optimal path planning. Using this locally semi-continuous metric space, the robot performs shortest path planning instead of following the nodes of the graph --- as is done with most other vision-only navigation approaches. The system exploits the local accuracy of visual odometry in creating local metric maps, and uses pose graph SLAM, visual appearance-based place recognition and point clouds registration to create the topometric map. The ability of the framework to sustain vision-only navigation is validated experimentally, and the system is provided as open-source software.

Determining operations affected by delay in predictive train timetables

Constructing train schedules is vital in railways. This complex and time consuming task is however made more difficult by additional requirements to make train schedules robust to delays and other disruptions. For a timetable to be regarded as robust, it should be insensitive to delays of a specified level and its performance with respect to a given metric, should be within given tolerances. In other words the effect of delays should be identifiable and should be shown to be minimal. To this end, a sensitivity analysis is proposed that identifies affected operations. More specifically a sensitivity analysis for determining what operation delays cause each operation to be affected is proposed. The information provided by this analysis gives another measure of timetable robustness and also provides control information that can be used when delays occur in practice. Several algorithms are proposed to identify this information and they utilise a disjunctive graph model of train operations. Upon completion the sets of affected operations can also be used to define the impact of all delays without further disjunctive graph evaluations.

A hybrid genetic algorithm for the minimum interconnection cut problem

In the real world there are many problems in network of networks (NoNs) that can be abstracted to a so-called minimum interconnection cut problem, which is fundamentally different from those classical minimum cut problems in graph theory. Thus, it is desirable to propose an efficient and effective algorithm for the minimum interconnection cut problem. In this paper we formulate the problem in graph theory, transform it into a multi-objective and multi-constraint combinatorial optimization problem, and propose a hybrid genetic algorithm (HGA) for the problem. The HGA is a penalty-based genetic algorithm (GA) that incorporates an effective heuristic procedure to locally optimize the individuals in the population of the GA. The HGA has been implemented and evaluated by experiments. Experimental results have shown that the HGA is effective and efficient.

Molecular cocrystals of 5-(4-bromophenyl)-1,3,4-thiadiazol-2-amine: hydrogen bonding in the structures of the 1:1 adduct with 2-(naphthalen-2-yloxy)acetic acid and the salt with 3,5-dinitrobenzoic acid

The structures of the anhydrous products from the interaction of 2-amino-5-(4-bromophenyl)-1,3,4-thiadiazole with (2-naphthoxy)acetic acid, the 1:1 adduct C8H6BrN3S . C12H10O3 (I) and 3,5-dinitrobenzoic acid, the salt C8H7BrN3S+ C7H3N2O6- (II) have been determined. In the adduct (I), a heterodimer is formed through a cyclic hydrogen-bonding motif [graph set R2/2(8)], involving carboxylic acid O-H...N(hetero)and amine N-H...O(carboxyl) interactions. The heterodimers are essentially planar with a thiadiazole to naphthyl ring dihedral angle of 15.9(2)deg. and the intramolecular thiadiazole to phenyl ring angle of 4.7(2)deg. An amine N-H...N(hetero) hydrogen bond between the heterodimers generates a one-dimensional chain structure extending down [001]. Also present are weak benzene-benzene and naphthalene-naphthalene pi-pi stacking interactions down the b axis [minimum ring centroid separation, 3.936(3) Ang.]. With the salt (II), the cation-anion association is also through a cyclic R2/2(8) motif but involving duplex N-H...O(carboxyl) hydrogen bonds, giving a heterodimer which is close to planar [dihedral angles between the thiadiazole ring and the two benzene rings, 5.00(16)deg. (intra) and 7.23(15)deg. (inter)]. A secondary centrosymmetric cyclic N-H...O(carboxyl) hydrogen-bonding association involving the second amino H-atom generates a heterotetramer. Also present in the crystal are weak pi-pi i-\p interactions between thiadiazolium rings [minimum ring centroid separation, 3.936(3)Ang.], as well as a short Br...O(nitro) interaction [3.314(4)Ang.]. The two structures reported here now provide a total of three crystallographically characterized examples of co-crystalline products from the interaction of 2-amino-5-(4-bromophenyl)-1,3,4-thiadiazole with carboxylic acids, of which only one involves proton-transfer.

An efficient algorithm for the detection of Eden

In this paper, a polynomial time algorithm is presented for solving the Eden problem for graph cellular automata. The algorithm is based on our neighborhood elimination operation which removes local neighborhood configurations which cannot be used in a pre-image of a given configuration. This paper presents a detailed derivation of our algorithm from first principles, and a detailed complexity and accuracy analysis is also given. In the case of time complexity, it is shown that the average case time complexity of the algorithm is \Theta(n^2), and the best and worst cases are \Omega(n) and O(n^3) respectively. This represents a vast improvement in the upper bound over current methods, without compromising average case performance.

Vision-based estimation of airborne target pseudobearing rate using hidden Markov model filters

The problem of estimating pseudobearing rate information of an airborne target based on measurements from a vision sensor is considered. Novel image speed and heading angle estimators are presented that exploit image morphology, hidden Markov model (HMM) filtering, and relative entropy rate (RER) concepts to allow pseudobearing rate information to be determined before (or whilst) the target track is being estimated from vision information.

A dynamic approach for evaluating earthwork hauls in construction

A graph theoretic approach is developed for accurately computing haulage costs in earthwork projects. This is vital as haulage is a predominant factor in the real cost of earthworks. A variety of metrics can be used in our approach, but a fuel consumption proxy is recommended. This approach is novel as it considers the constantly changing terrain that results from cutting and filling activities and replaces inaccurate “static” calculations that have been used previously. The approach is also capable of efficiently correcting the violation of top down cutting and bottom up filling conditions that can be found in existing earthwork assignments and sequences. This approach assumes that the project site is partitioned into uniform blocks. A directed graph is then utilised to describe the terrain surface. This digraph is altered after each cut and fill, in order to reflect the true state of the terrain. A shortest path algorithm is successively applied to calculate the cost of each haul and these costs are summed to provide a total cost of haulage

Filter and control performance bounds in the presence of model uncertainties with aerospace applications

This thesis establishes performance properties for approximate filters and controllers that are designed on the basis of approximate dynamic system representations. These performance properties provide a theoretical justification for the widespread application of approximate filters and controllers in the common situation where system models are not known with complete certainty. This research also provides useful tools for approximate filter designs, which are applied to hybrid filtering of uncertain nonlinear systems. As a contribution towards applications, this thesis also investigates air traffic separation control in the presence of measurement uncertainties.

Fully Bayesian experimental design for pharmacokinetic studies

Utility functions in Bayesian experimental design are usually based on the posterior distribution. When the posterior is found by simulation, it must be sampled from for each future data set drawn from the prior predictive distribution. Many thousands of posterior distributions are often required. A popular technique in the Bayesian experimental design literature to rapidly obtain samples from the posterior is importance sampling, using the prior as the importance distribution. However, importance sampling will tend to break down if there is a reasonable number of experimental observations and/or the model parameter is high dimensional. In this paper we explore the use of Laplace approximations in the design setting to overcome this drawback. Furthermore, we consider using the Laplace approximation to form the importance distribution to obtain a more efficient importance distribution than the prior. The methodology is motivated by a pharmacokinetic study which investigates the effect of extracorporeal membrane oxygenation on the pharmacokinetics of antibiotics in sheep. The design problem is to find 10 near optimal plasma sampling times which produce precise estimates of pharmacokinetic model parameters/measures of interest. We consider several different utility functions of interest in these studies, which involve the posterior distribution of parameter functions.

Automatic segmentation of choroidal thickness in optical coherence tomography

The assessment of choroidal thickness from optical coherence tomography (OCT) images of the human choroid is an important clinical and research task, since it provides valuable information regarding the eye’s normal anatomy and physiology, and changes associated with various eye diseases and the development of refractive error. Due to the time consuming and subjective nature of manual image analysis, there is a need for the development of reliable objective automated methods of image segmentation to derive choroidal thickness measures. However, the detection of the two boundaries which delineate the choroid is a complicated and challenging task, in particular the detection of the outer choroidal boundary, due to a number of issues including: (i) the vascular ocular tissue is non-uniform and rich in non-homogeneous features, and (ii) the boundary can have a low contrast. In this paper, an automatic segmentation technique based on graph-search theory is presented to segment the inner choroidal boundary (ICB) and the outer choroidal boundary (OCB) to obtain the choroid thickness profile from OCT images. Before the segmentation, the B-scan is pre-processed to enhance the two boundaries of interest and to minimize the artifacts produced by surrounding features. The algorithm to detect the ICB is based on a simple edge filter and a directional weighted map penalty, while the algorithm to detect the OCB is based on OCT image enhancement and a dual brightness probability gradient. The method was tested on a large data set of images from a pediatric (1083 B-scans) and an adult (90 B-scans) population, which were previously manually segmented by an experienced observer. The results demonstrate the proposed method provides robust detection of the boundaries of interest and is a useful tool to extract clinical data.

Continuous appearance-based localisation and mapping

This thesis presents a novel approach to mobile robot navigation using visual information towards the goal of long-term autonomy. A novel concept of a continuous appearance-based trajectory is proposed in order to solve the limitations of previous robot navigation systems, and two new algorithms for mobile robots, CAT-SLAM and CAT-Graph, are presented and evaluated. These algorithms yield performance exceeding state-of-the-art methods on public benchmark datasets and large-scale real-world environments, and will help enable widespread use of mobile robots in everyday applications.

Image segmentation and pose estimation of humans in video

This thesis introduces improved techniques towards automatically estimating the pose of humans from video. It examines a complete workflow to estimating pose, from the segmentation of the raw video stream to extract silhouettes, to using the silhouettes in order to determine the relative orientation of parts of the human body. The proposed segmentation algorithms have improved performance and reduced complexity, while the pose estimation shows superior accuracy during difficult cases of self occlusion.