Traversability estimation for a planetary rover via experimental kernel learning in a Gaussian process framework

A critical requirement for safe autonomous navigation of a planetary rover is the ability to accurately estimate the traversability of the terrain. This work considers the problem of predicting the attitude and configuration angles of the platform from terrain representations that are often incomplete due to occlusions and sensor limitations. Using Gaussian Processes (GP) and exteroceptive data as training input, we can provide a continuous and complete representation of terrain traversability, with uncertainty in the output estimates. In this paper, we propose a novel method that focuses on exploiting the explicit correlation in vehicle attitude and configuration during operation by learning a kernel function from vehicle experience to perform GP regression. We provide an extensive experimental validation of the proposed method on a planetary rover. We show significant improvement in the accuracy of our estimation compared with results obtained using standard kernels (Squared Exponential and Neural Network), and compared to traversability estimation made over terrain models built using state-of-the-art GP techniques.

Food reward sensitivity predicts overconsumption of high-fat snack food

Overconsumption of snack foods has been linked to rising rates of obesity, with our ‘obesogenic’ environment and its abundance of palatable, high-calorie foods and associated cues especially implicated. However, it is clear that some individuals are particularly susceptible to overconsumption and weight gain. It was hypothesised that individuals sensitive to the rewarding properties of palatable foods, and associated stimuli, would show elevated consumption. Snack food intake was measured in 50 adults (mean age 34.5 years, BMI 23.9 kg/m2, 56% female) in a repeated measures design, both with and without a ‘food cue’. Trait (BIS/BAS scales), behavioural (computerised CARROT) and food reward were assessed. Sensitivity to food reward, but not generalised reward, was positively associated with snack food intake. This relationship was not affected by the presence of a food cue. Findings are discussed in the context of implications for weight management.

TNF and TNF receptor expression and insulin sensitivity in human omental and subcutaneous adipose tissue--influence of BMI and adipose distribution

Tumour necrosis factor (TNF)alpha is implicated in the relationship between obesity and insulin resistance/ type 2 diabetes. In an effort to understand this association better we (i) profiled gene expression patterns of TNF, TNFR1 and TNFR2 and (ii) investigated the effects of TNF on glucose uptake in isolated adipocytes and adipose tissue explants from omental and subcutaneous depots from lean, overweight and obese individuals. TNF expression correlated with expression of TNFR2, but not TNFR1, and TNF and TNFR2 expression increased in obesity. TNFR1 expression was higher in omental than in subcutaneous adipocytes. Expression levels of TNF or either receptor did not differ between adipocytes from individuals with central and peripheral obesity. TNF only suppressed glucose uptake in insulin-stimulated subcutaneous tissue and this suppression was only observed in tissue from lean subjects. These data support a relationship between the TNF system and body mass index (BMI), but not fat distribution, and suggest depot specificity of the TNF effect on glucose uptake. Furthermore, adipose tissue from obese subjects already appears insulin 'resistant' and this may be a result of the increased TNF levels.

A simple model for the establishment of tick-borne pathogens of Ixodes scapularis : a global sensitivity analysis of R0

The basic reproduction number of a pathogen, R 0, determines whether a pathogen will spread (R0>1R 0>1), when introduced into a fully susceptible population or fade out (R0<1R 0<1), because infected hosts do not, on average, replace themselves. In this paper we develop a simple mechanistic model for the basic reproduction number for a group of tick-borne pathogens that wholly, or almost wholly, depend on horizontal transmission to and from vertebrate hosts. This group includes the causative agent of Lyme disease, Borrelia burgdorferi, and the causative agent of human babesiosis, Babesia microti, for which transmission between co-feeding ticks and vertical transmission from adult female ticks are both negligible. The model has only 19 parameters, all of which have a clear biological interpretation and can be estimated from laboratory or field data. The model takes into account the transmission efficiency from the vertebrate host as a function of the days since infection, in part because of the potential for this dynamic to interact with tick phenology, which is also included in the model. This sets the model apart from previous, similar models for R0 for tick-borne pathogens. We then define parameter ranges for the 19 parameters using estimates from the literature, as well as laboratory and field data, and perform a global sensitivity analysis of the model. This enables us to rank the importance of the parameters in terms of their contribution to the observed variation in R0. We conclude that the transmission efficiency from the vertebrate host to Ixodes scapularis ticks, the survival rate of Ixodes scapularis from fed larva to feeding nymph, and the fraction of nymphs finding a competent host, are the most influential factors for R0. This contrasts with other vector borne pathogens where it is usually the abundance of the vector or host, or the vector-to-host ratio, that determine conditions for emergence. These results are a step towards a better understanding of the geographical expansion of currently emerging horizontally transmitted tick-borne pathogens such as Babesia microti, as well as providing a firmer scientific basis for targeted use of acaricide or the application of wildlife vaccines that are currently in development.

Geometric sensitivity of patient-specific finite element models of the spine to variability in user-selected anatomical landmarks

Software to create individualised finite element (FE) models of the osseoligamentous spine using pre-operative computed tomography (CT) data-sets for spinal surgery patients has recently been developed. This study presents a geometric sensitivity analysis of this software to assess the effect of intra-observer variability in user-selected anatomical landmarks. User-selected landmarks on the osseous anatomy were defined from CT data-sets for three scoliosis patients and these landmarks were used to reconstruct patient-specific anatomy of the spine and ribcage using parametric descriptions. The intra-observer errors in landmark co-ordinates for these anatomical landmarks were calculated. FE models of the spine and ribcage were created using the reconstructed anatomy for each patient and these models were analysed for a loadcase simulating clinical flexibility assessment. The intra-observer error in the anatomical measurements was low in comparison to the initial dimensions, with the exception of the angular measurements for disc wedge and zygapophyseal joint (z-joint) orientation and disc height. This variability suggested that CT resolution may influence such angular measurements, particularly for small anatomical features, such as the z-joints, and may also affect disc height. The results of the FE analysis showed low variation in the model predictions for spinal curvature with the mean intra-observer variability substantially less than the accepted error in clinical measurement. These findings demonstrate that intra-observer variability in landmark point selection has minimal effect on the subsequent FE predictions for a clinical loadcase.

Recursive pathways to marginal likelihood estimation with prior-sensitivity analysis

We investigate the utility to computational Bayesian analyses of a particular family of recursive marginal likelihood estimators characterized by the (equivalent) algorithms known as "biased sampling" or "reverse logistic regression" in the statistics literature and "the density of states" in physics. Through a pair of numerical examples (including mixture modeling of the well-known galaxy dataset) we highlight the remarkable diversity of sampling schemes amenable to such recursive normalization, as well as the notable efficiency of the resulting pseudo-mixture distributions for gauging prior-sensitivity in the Bayesian model selection context. Our key theoretical contributions are to introduce a novel heuristic ("thermodynamic integration via importance sampling") for qualifying the role of the bridging sequence in this procedure, and to reveal various connections between these recursive estimators and the nested sampling technique.

Comparison of high sensitivity troponin T and I assays in the diagnosis of non-ST elevation acute myocardial infarction in emergency patients with chest pain

Objectives: Concentrations of troponin measured with high sensitivity troponin assays are raised in a number of emergency department (ED) patients; however many are not diagnosed with acute myocardial infarction (AMI). Clinical comparisons between the early use (2 h after presentation) of high sensitivity cardiac troponin T (hs-cTnT) and I (hs-cTnI) assays for the diagnosis of AMI have not been reported. Design and methods: Early (0 h and 2 h) hs-cTnT and hs-cTnI assay results in 1571 ED patients with potential acute coronary syndrome (ACS) without ST elevation on electrocardiograph (ECG) were evaluated. The primary outcome was diagnosis of index MI adjudicated by cardiologists using the local cTnI assay results taken ≥6 h after presentation, ECGs and clinical information. Stored samples were later analysed with hs-cTnT and hs-cTnI assays. Results: The ROC analysis for AMI (204 patients; 13.0%) for hs-cTnT and hs-cTnI after 2 h was 0.95 (95% CI: 0.94–0.97) and 0.98 (95% CI: 0.97–0.99) respectively. The sensitivity, specificity, PLR, and NLR of hs-cTnT and hs-cTnI for AMI after 2 h were 94.1% (95% CI: 90.0–96.6) and 95.6% (95% CI: 91.8–97.7), 79.0% (95% CI: 76.8–81.1) and 92.5% (95% CI: 90.9–93.7), 4.48 (95% CI: 4.02–5.00) and 12.86 (95% CI: 10.51–15.31), and 0.07 (95% CI: 0.04–0.13) and 0.05 (95% CI:0.03–0.09) respectively. Conclusions: Exclusion of AMI 2 h after presentation in emergency patients with possible ACS can be achieved using hs-cTnT or hs-cTnI assays. Significant differences in specificity of these assays are relevant and if using the hs-cTnT assay, further clinical assessment in a larger proportion of patients would be required.

Investigating the sensitivity of OSLD responses for varying field sizes across matched linear accelerators

When radiation therapy centres are equipped with two or more linear accelerators from the same vendor, they are usually beam-matched. This work tested the sensitivity of optically stimulated luminescence dosimeters (OSLDs) across matched linear accelerators. The responses were compared with an unshielded diode detector for varying field sizes. Clinical studies are currently done with thermoluminescent dosimeters (TLD), which absorb radiation then emit some levels of light determined by the radiation absorption when heated.

Automatic classification of human epithelial type 2 cell indirect immunofluorescence images using cell pyramid matching

This paper describes a novel system for automatic classification of images obtained from Anti-Nuclear Antibody (ANA) pathology tests on Human Epithelial type 2 (HEp-2) cells using the Indirect Immunofluorescence (IIF) protocol. The IIF protocol on HEp-2 cells has been the hallmark method to identify the presence of ANAs, due to its high sensitivity and the large range of antigens that can be detected. However, it suffers from numerous shortcomings, such as being subjective as well as time and labour intensive. Computer Aided Diagnostic (CAD) systems have been developed to address these problems, which automatically classify a HEp-2 cell image into one of its known patterns (eg. speckled, homogeneous). Most of the existing CAD systems use handpicked features to represent a HEp-2 cell image, which may only work in limited scenarios. We propose a novel automatic cell image classification method termed Cell Pyramid Matching (CPM), which is comprised of regional histograms of visual words coupled with the Multiple Kernel Learning framework. We present a study of several variations of generating histograms and show the efficacy of the system on two publicly available datasets: the ICPR HEp-2 cell classification contest dataset and the SNPHEp-2 dataset.

MCF7/LCC2: A 4-hydroxytamoxifen resistant human breast cancer variant that retains sensitivity to the steroidal antiestrogen ICI 182,780

The development of resistance to the antiestrogen tamoxifen occurs in a high percentage of initially responsive patients. We have developed a new model in which to investigate acquired resistance to triphenylethylenes. A stepwise in vitro selection of the hormone-independent human breast cancer variant MCF-7/LCC1 against 4-hydroxytamoxifen produced a stable resistant population designated MCF7/LCC2. MCF7/LCC2 cells retain levels of estrogen receptor expression comparable to the parental MCF7/LCC1 and MCF-7 cells. Progesterone receptor expression remains estrogen inducible in MCF7/LCC2 cells, although to levels significantly lower than observed in MCF-7 and MCF7/LCC1 cells. MCF7/ LCC2 cells form tumors in ovariectomized nude mice without estrogen supplementation, and these tumors are tamoxifen resistant but can be tstrogen stimulated. Significantly, MCF7/LCC2 cells have retained sensitivity to the steroidal antiestrogen ICI 182,780. These data suggest that some breast cancer patients who acquire resistance to tamoxifen may not develop cross-resistance to treatment with steroidal antiestrogens.

A coupled SPH-DEM model for micro-scale structural deformations of plant cells during drying

A single plant cell was modeled with smoothed particle hydrodynamics (SPH) and a discrete element method (DEM) to study the basic micromechanics that govern the cellular structural deformations during drying. This two-dimensional particle-based model consists of two components: a cell fluid model and a cell wall model. The cell fluid was approximated to a highly viscous Newtonian fluid and modeled with SPH. The cell wall was treated as a stiff semi-permeable solid membrane with visco-elastic properties and modeled as a neo-Hookean solid material using a DEM. Compared to existing meshfree particle-based plant cell models, we have specifically introduced cell wall–fluid attraction forces and cell wall bending stiffness effects to address the critical shrinkage characteristics of the plant cells during drying. Also, a moisture domain-based novel approach was used to simulate drying mechanisms within the particle scheme. The model performance was found to be mainly influenced by the particle resolution, initial gap between the outermost fluid particles and wall particles and number of particles in the SPH influence domain. A higher order smoothing kernel was used with adaptive smoothing length to improve the stability and accuracy of the model. Cell deformations at different states of cell dryness were qualitatively and quantitatively compared with microscopic experimental findings on apple cells and a fairly good agreement was observed with some exceptions. The wall–fluid attraction forces and cell wall bending stiffness were found to be significantly improving the model predictions. A detailed sensitivity analysis was also done to further investigate the influence of wall–fluid attraction forces, cell wall bending stiffness, cell wall stiffness and the particle resolution. This novel meshfree based modeling approach is highly applicable for cellular level deformation studies of plant food materials during drying, which characterize large deformations.

Sensitivity of an Elekta iView GT a-Si EPID model to delivery errors for pre-treatment verification of IMRT fields

A Monte Carlo model of an Elekta iViewGT amorphous silicon electronic portal imaging device (a-Si EPID) has been validated for pre-treatment verification of clinical IMRT treatment plans. The simulations involved the use of the BEAMnrc and DOSXYZnrc Monte Carlo codes to predict the response of the iViewGT a-Si EPID model. The predicted EPID images were compared to the measured images obtained from the experiment. The measured EPID images were obtained by delivering a photon beam from an Elekta Synergy linac to the Elekta iViewGT a-Si EPID. The a-Si EPID was used with no additional build-up material. Frame averaged EPID images were acquired and processed using in-house software. The agreement between the predicted and measured images was analyzed using the gamma analysis technique with acceptance criteria of 3% / 3 mm. The results show that the predicted EPID images for four clinical IMRT treatment plans have a good agreement with the measured EPID signal. Three prostate IMRT plans were found to have an average gamma pass rate of more than 95.0 % and a spinal IMRT plan has the average gamma pass rate of 94.3 %. During the period of performing this work a routine MLC calibration was performed and one of the IMRT treatments re-measured with the EPID. A change in the gamma pass rate for one field was observed. This was the motivation for a series of experiments to investigate the sensitivity of the method by introducing delivery errors, MLC position and dosimetric overshoot, into the simulated EPID images. The method was found to be sensitive to 1 mm leaf position errors and 10% overshoot errors.

Engineering thin film semiconductor gas sensors to increase sensitivity and decrease operation temperature

Thin film nanostructured gas sensors typically operate at temperatures above 400°C, but lower temperature operation is highly desirable, especially for remote area field sensing as this reduces significantly power consumption. We have investigated a range of sensor materials based on both pure and doped tungsten oxide (mainly focusing on Fe-doping), deposited using both thermal evaporation and electron-beam evaporation, and using a variety of post-deposition annealing. The films show excellent sensitivity at operating temperatures as low as 150°C for detection of NO2. There is a definite relationship between the sensitivity and the crystallinity and nanostructure obtained through the deposition and heat treatment processes, as well as variations in the conductivity caused both by doping and heat treatmetn. The ultimate goal of this work is to control the sensing properties, including selectivity to specific gases through the engineering of the electronic properties and the nanostructure of the films.