Segmentation of cone-beam CT using a hidden Markov random field with informative priors

Cone-beam computed tomography (CBCT) has enormous potential to improve the accuracy of treatment delivery in image-guided radiotherapy (IGRT). To assist radiotherapists in interpreting these images, we use a Bayesian statistical model to label each voxel according to its tissue type. The rich sources of prior information in IGRT are incorporated into a hidden Markov random field model of the 3D image lattice. Tissue densities in the reference CT scan are estimated using inverse regression and then rescaled to approximate the corresponding CBCT intensity values. The treatment planning contours are combined with published studies of physiological variability to produce a spatial prior distribution for changes in the size, shape and position of the tumour volume and organs at risk. The voxel labels are estimated using iterated conditional modes. The accuracy of the method has been evaluated using 27 CBCT scans of an electron density phantom. The mean voxel-wise misclassification rate was 6.2\%, with Dice similarity coefficient of 0.73 for liver, muscle, breast and adipose tissue. By incorporating prior information, we are able to successfully segment CBCT images. This could be a viable approach for automated, online image analysis in radiotherapy.

Bayesian indirect inference using a parametric auxiliary model

Indirect inference (II) is a methodology for estimating the parameters of an intractable (generative) model on the basis of an alternative parametric (auxiliary) model that is both analytically and computationally easier to deal with. Such an approach has been well explored in the classical literature but has received substantially less attention in the Bayesian paradigm. The purpose of this paper is to compare and contrast a collection of what we call parametric Bayesian indirect inference (pBII) methods. One class of pBII methods uses approximate Bayesian computation (referred to here as ABC II) where the summary statistic is formed on the basis of the auxiliary model, using ideas from II. Another approach proposed in the literature, referred to here as parametric Bayesian indirect likelihood (pBIL), we show to be a fundamentally different approach to ABC II. We devise new theoretical results for pBIL to give extra insights into its behaviour and also its differences with ABC II. Furthermore, we examine in more detail the assumptions required to use each pBII method. The results, insights and comparisons developed in this paper are illustrated on simple examples and two other substantive applications. The first of the substantive examples involves performing inference for complex quantile distributions based on simulated data while the second is for estimating the parameters of a trivariate stochastic process describing the evolution of macroparasites within a host based on real data. We create a novel framework called Bayesian indirect likelihood (BIL) which encompasses pBII as well as general ABC methods so that the connections between the methods can be established.

Rural self-reliance: the impact on health experiences of people living with type II diabetes in rural Queensland, Australia

Objective: The objective of the study was to explore whether and how rural culture influences type II diabetes management and to better understand the social processes that rural people construct in coping with diabetes and its complications. In particular, the study aimed to analyse the interface and interactions between rural people with type II diabetes and the Australian health care system, and to develop a theoretical understanding that reflects constructs that may be more broadly applicable. Methods: The study applied constructivist grounded theory methods within an interpretive interactionist framework. Data from 39 semi-structured interviews with rural and urban type II diabetes patients and a mix of rural health care providers were analysed to develop a theoretical understanding of the social processes that define diabetes management in that context. Results: The analysis suggests that although type II diabetes imposes limitations that require adjustment and adaptation, these processes are actively negotiated by rural people within the environmental context to fit the salient social understandings of autonomy and self-reliance. Thus, people normalized self-reliant diabetes management behaviours because this was congruent with the rural culture. Factors that informed the actions of normalization were relationships between participants and health care professionals, support, and access to individual resources. Conclusions: The findings point to ways in which rural self-reliance is conceived as the primary strategy of diabetes management. People face the paradox of engaging with a health care system that at the same time maximizes individual responsibility for health and minimizes the social support by which individuals manage the condition. The emphasis on self-reliance gives some legitimacy to a lack of prevention and chronic care services. Success of diabetes management behaviours is, however, contingent on relative resources. Where there is good primary care, there develops a number of downstream effects including a sense of empowerment to manage difficult rural environmental circumstances. This has particular bearing on health outcomes for people with fewer resources.

Beginning inference in fourth grade : exploring variation in measurement

This paper addresses one of the foundational components of beginning infernce, namely variation, with 5 classes of Year 4 students undertaking a measurement activity using scaled instruments in two contexts: all students measuring one person's arm span and recording the values obtained, and each student having his/her own arm span measured and recorded. The results included documentation of students' explicit appreciation of the variety of ways in which varitation can occur, including outliers, and their ability to create and describe valid representations of their data.

Effect of text type on near work-induced contrast adaptation in myopic and emmetropic young adults

Purpose Contrast adaptation has been speculated to be an error signal for emmetropization. Myopic children exhibit higher contrast adaptation than emmetropic children. This study aimed to determine whether contrast adaptation varies with the type of text viewed by emmetropic and myopic young adults. Methods Baseline contrast sensitivity was determined in 25 emmetropic and 25 spectacle-corrected myopic young adults for 0.5, 1.2, 2.7, 4.4, and 6.2 cycles per degree (cpd) horizontal sine wave gratings. The adults spent periods looking at a 6.2 cpd high-contrast horizontal grating and reading lines of English and Chinese text (these texts comprised 1.2 cpd row and 6 cpd stroke frequencies). The effects of these near tasks on contrast sensitivity were determined, with decreases in sensitivity indicating contrast adaptation. Results Contrast adaptation was affected by the near task (F2,672 = 43.0; P < 0.001). Adaptation was greater for the grating task (0.13 ± 0.17 log unit, averaged across all frequencies) than reading tasks, but there was no significant difference between the two reading tasks (English 0.05 ± 0.13 log unit versus Chinese 0.04 ± 0.13 log unit). The myopic group showed significantly greater adaptation (by 0.04, 0.04, and 0.05 log units for English, Chinese, and grating tasks, respectively) than the emmetropic group (F1,48 = 5.0; P = 0.03). Conclusions In young adults, reading Chinese text induced similar contrast adaptation as reading English text. Myopes exhibited greater contrast adaptation than emmetropes. Contrast adaptation, independent of text type, might be associated with myopia development.

A metastatic neuroendocrine anaplastic small cell tumor in a patient with multiple endocrine neoplasia type 1 syndrome : assessment of disease status and response to doxorubicin, cyclophosphamide, etoposide chemotherapy through scintigraphic imaging with 111In-pentetreotide

Extrapulmonary small cell and small cell neuroendocrine tumors of unknown primary site are, in general, aggressive neoplasms with a short median survival. Like small cell lung cancer (SCLC), they often are responsive to chemotherapy and radiotherapy. Small cell lung cancer and well differentiated neuroendocrine carcinomas of the gastrointestinal tract and pancreas tend to express somatostatin receptors. These tumors may be localized in patients by scintigraphic imaging using radiolabeled somatostatin analogues. A patient with an anaplastic neuroendocrine small cell tumor arising on a background of multiple endocrine neoplasia type 1 syndrome is reported. The patient had a known large pancreatic gastrinoma and previously treated parathyroid adenopathy. At presentation, there was small cell cancer throughout the liver and skeleton. Imaging with a radiolabeled somatostatin analogue, 111In- pentetreotide (Mallinckrodt Medical B. V., Petten, Holland), revealed all sites of disease detected by routine biochemical and radiologic methods. After six cycles of chemotherapy with doxorubicin, cyclophosphamide, and etoposide, there was almost complete clearance of the metastatic disease. 111In-pentetreotide scintigraphy revealed uptake consistent with small areas of residual disease in the liver, the abdomen (in mesenteric lymph nodes), and posterior thorax (in a rib). The primary gastrinoma present before the onset of the anaplastic small cell cancer showed no evidence of response to the treatment. The patient remained well for 1 year and then relapsed with brain, lung, liver, and skeletal metastases. Despite an initial response to salvage radiotherapy and chemotherapy with carboplatin and dacarbazine, the patient died 6 months later.

Percutaneous coronary intervention with drug-eluting stents or coronary artery bypass surgery in subjects with type 2 diabetes : evaluation of: Farkouth ME, Domanski M, Sleeper LA, et al. Strategies for multivessel revascularization in patients with diabetes. N Engl J Med 2012;367(25):2375-84, and Contini GA, Nicolini F, Fortuna D, et al. Five-year outcomes of surgical or percutaneous myocardial revascularization in diabetic patients. Int J Cardiol 2012. [Epub ahead of print]

There is debate as to whether percutaneous coronary intervention (PCI) with drug-eluting stents or coronary artery bypass surgery (CABG) is the best procedure for subjects with type 2 diabetes and coronary artery disease requiring revascularization. There is some evidence that following these procedures there is less further revascularization with CABG than PCI in subjects with diabetes. Two recent studies; the FREEDOM (Future Revascularization Evaluation in patients with Diabetes mellitus: Optimal Management of Multivessel Disease) trial, and a trial using a real world diabetic population from a Registry, have shown that the benefits of CABG over PCI in subjects with type 2 diabetes extends to lower rates of death and myocardial infarct, in addition to lower rates of revascularization. However, the rates of stroke may be higher with CABG than PCI with drug-eluting stents in this population. Thus, if CABG is going to be preferred to PCI in subjects with type 2 diabetes and multivessel coronary disease, consideration should be given to how to reduce the rates of stroke with CABG.

Soybean dwarf luteovirus contains the third variant genome type in the luteovirus group

Complementary DNAs covering the entire RNA genome of soybean dwarf luteovirus (SDV) were cloned and sequenced. Computer analysis of the 5861 nucleotide sequence revealed five major open reading frames (ORFs) possessing conservation of sequence and organisation with known luteovirus sequences. Comparative analyses of the genome structure show that SDV shares sequence homology and features of gene organisation with barley yellow dwarf virus (PAV isolate) in the 5' half of the genome, yet is more closely related to potato leafroll virus in its 3' coding regions. In addition, SDV differs from other known luteoviruses in possessing an exceptionally long 3' terminal sequence with no apparent coding capacity. We conclude from these data that the SDV genome represents a third variant genome type in the luteovirus group.

Odometry-driven inference to link multiple exemplars of a location

A major challenge for robot localization and mapping systems is maintaining reliable operation in a changing environment. Vision-based systems in particular are susceptible to changes in illumination and weather, and the same location at another time of day may appear radically different to a system using a feature-based visual localization system. One approach for mapping changing environments is to create and maintain maps that contain multiple representations of each physical location in a topological framework or manifold. However, this requires the system to be able to correctly link two or more appearance representations to the same spatial location, even though the representations may appear quite dissimilar. This paper proposes a method of linking visual representations from the same location without requiring a visual match, thereby allowing vision-based localization systems to create multiple appearance representations of physical locations. The most likely position on the robot path is determined using particle filter methods based on dead reckoning data and recent visual loop closures. In order to avoid erroneous loop closures, the odometry-based inferences are only accepted when the inferred path's end point is confirmed as correct by the visual matching system. Algorithm performance is demonstrated using an indoor robot dataset and a large outdoor camera dataset.

Bayesian inference of traffic volumes based on Bluetooth data

The study of the relationship between macroscopic traffic parameters, such as flow, speed and travel time, is essential to the understanding of the behaviour of freeway and arterial roads. However, the temporal dynamics of these parameters are difficult to model, especially for arterial roads, where the process of traffic change is driven by a variety of variables. The introduction of the Bluetooth technology into the transportation area has proven exceptionally useful for monitoring vehicular traffic, as it allows reliable estimation of travel times and traffic demands. In this work, we propose an approach based on Bayesian networks for analyzing and predicting the complex dynamics of flow or volume, based on travel time observations from Bluetooth sensors. The spatio-temporal relationship between volume and travel time is captured through a first-order transition model, and a univariate Gaussian sensor model. The two models are trained and tested on travel time and volume data, from an arterial link, collected over a period of six days. To reduce the computational costs of the inference tasks, volume is converted into a discrete variable. The discretization process is carried out through a Self-Organizing Map. Preliminary results show that a simple Bayesian network can effectively estimate and predict the complex temporal dynamics of arterial volumes from the travel time data. Not only is the model well suited to produce posterior distributions over single past, current and future states; but it also allows computing the estimations of joint distributions, over sequences of states. Furthermore, the Bayesian network can achieve excellent prediction, even when the stream of travel time observation is partially incomplete.

Longitudinal assessment of neuropathy in type 1 diabetes using novel ophthalmic markers (LANDMark) : study design and baseline characteristics

Aims Corneal nerve morphology and corneal sensation threshold have recently been explored as potential surrogate markers for the evaluation of diabetic neuropathy. We present the baseline findings of the ‘Longitudinal Assessment of Neuropathy in type 1 Diabetes using novel ophthalmic Markers’(LANDMark) study. Methods The LANDMark study is a 4-year, two-site, natural history study of three participant groups: type 1 diabetes with neuropathy (T1W), type 1 diabetes without neuropathy (T1WO) and control participants without diabetes or neuropathy. All participants undergo a detailed annual assessment of neuropathy including corneal nerve parameters measured using corneal confocal microscopy and corneal sensitivity measured using non-contact corneal aesthesiometry. Results 76 T1W, 166 T1WO and 154 control participants were enrolled into the study. Corneal sensation threshold (mbars) was significantly higher (i.e. sensitivity was lower) in T1W (1.0 ± 1.1) than T1WO (0.7 ± 0.7) and controls (0.6 ± 0.4) (p < 0.001), with no difference between T1WO and controls. Corneal nerve fibre length was lower in T1W (14.0 ± 6.4 mm/mm2) compared to T1WO (19.1 ± 5.8 mm/mm2) and controls (23.2 ± 6.3 mm/mm2) (p < 0.001). Corneal nerve fibre length was lower in T1WO compared to controls. Conclusions The LANDMark baseline findings confirm a reduction in corneal sensitivity only in Type 1 patients with neuropathy. However, corneal nerve fibre length is reduced even in Type 1 patients without neuropathy with an even greater deficit in Type 1 patients with neuropathy.

Model-predictive control and closed-loop stability considerations for nonlinear plants described by local ARX-type models

In this paper, a model-predictive control (MPC) method is detailed for the control of nonlinear systems with stability considerations. It will be assumed that the plant is described by a local input/output ARX-type model, with the control potentially included in the premise variables, which enables the control of systems that are nonlinear in both the state and control input. Additionally, for the case of set point regulation, a suboptimal controller is derived which has the dual purpose of ensuring stability and enabling finite-iteration termination of the iterative procedure used to solve the nonlinear optimization problem that is used to determine the control signal.

Shrinking the keys of discrete-log-type lossy trapdoor functions

To this day, realizations in the standard-model of (lossy) trapdoor functions from discrete-log-type assumptions require large public key sizes, e.g., about Θ(λ 2) group elements for a reduction from the decisional Diffie-Hellman assumption (where λ is a security parameter). We propose two realizations of lossy trapdoor functions that achieve public key size of only Θ(λ) group elements in bilinear groups, with a reduction from the decisional Bilinear Diffie-Hellman assumption. Our first construction achieves this result at the expense of a long common reference string of Θ(λ 2) elements, albeit reusable in multiple LTDF instantiations. Our second scheme also achieves public keys of size Θ(λ), entirely in the standard model and in particular without any reference string, at the cost of a slightly more involved construction. The main technical novelty, developed for the second scheme, is a compact encoding technique for generating compressed representations of certain sequences of group elements for the public parameters.