Long-term continuous monitoring of methane emissions at an oil and gas facility using a multi-open-path laser dispersion spectrometer.

A method for methane emissions monitoring at industrial facility level was developed based on a high precision multi-open-path laser dispersion spectrometer combined with Bayesian analysis algorithms using Monte Carlo Markov Chain (MCMC) inference. From the methane path-averaged concentrations spatially distributed over the facility under study, together with the wind vector, the analysis allows detection, localization and quantification of fugitive methane emissions. This paper describes the very first long term (3 months), continuous (24 h/7 days) deployment of this monitoring system at an operational gas processing and distribution facility. The continuous monitoring system, made of the combination of the open-path high-precision (<10 ppb) methane concentration analyser and the data analysis method, was evaluated with controlled releases of methane of about 5 kg/h for short periods of time (30-60 min). Quantification was successful, with actual emission rates lying well within the quoted uncertainty ranges. Source localisation was found to lack accuracy, with biases of 30-50 m in the direction of the line of sight of the spectrometer, due to the short duration of the controlled releases, the limited wind vector diversity, and complications from air flows around buildings not accounted for by the transport model. Using longer-term data from the deployment, the MCMC algorithm led to the identification of unexpected low intensity persistent sources (<1 kg/h) at the site. Localisation of persistent sources was mostly successful at equipment level (within ~20 m) as confirmed by a subsequent survey with an optical gas imaging (OGI) camera. Quantification of these individual sources was challenging owing to their low intensity, but a consistent estimate of the total methane emission from the facility could be derived using two different inference approaches. These results represent a stepping stone in the development of continuous monitoring systems for methane emissions, pivotal in driving greenhouse gas reduction from industrial facilities. The demonstrated continuous monitoring system gives promising performance in early detection of unexpected emissions and quantification of potentially time-varying emissions from an entire facility.

Locating and Quantifying Methane Emissions by Inverse Analysis of Path-Integrated Concentration Data Using a Markov-Chain Monte Carlo Approach.

The action to reduce anthropogenic greenhouse gas emissions is severely constrained by the difficulty of locating sources and quantifying their emission rates. Methane emissions by the energy sector are of particular concern. We report results achieved with a new area monitoring approach using laser dispersion spectroscopy to measure path-averaged concentrations along multiple beams. The method is generally applicable to greenhouse gases, but this work is focused on methane. Nineteen calibrated methane releases in four distinct configurations, including three separate blind trials, were made within a flat test area of 175 m by 175 m. Using a Gaussian plume gas dispersion model, driven by wind velocity data, we calculate the data anticipated for hundreds of automatically proposed candidate source configurations. The Markov-chain Monte Carlo analysis finds source locations and emission rates whose calculated path-averaged concentrations are consistent with those measured and associated uncertainties. This approach found the correct number of sources and located them to be within <9 m in more than 75% of the cases. The relative accuracy of the mass emission rate results was highly correlated to the localization accuracy and better than 30% in 70% of the cases. The discrepancies for mass emission rates were <2 kg/h for 95% of the cases.

Unsupervised morphological segmentation of tissue compartments in histopathological images.

Algorithmic segmentation of histologically relevant regions of tissues in digitized histopathological images is a critical step towards computer-assisted diagnosis and analysis. For example, automatic identification of epithelial and stromal tissues in images is important for spatial localisation and guidance in the analysis and characterisation of tumour micro-environment. Current segmentation approaches are based on supervised methods, which require extensive training data from high quality, manually annotated images. This is often difficult and costly to obtain. This paper presents an alternative data-independent framework based on unsupervised segmentation of oropharyngeal cancer tissue micro-arrays (TMAs). An automated segmentation algorithm based on mathematical morphology is first applied to light microscopy images stained with haematoxylin and eosin. This partitions the image into multiple binary 'virtual-cells', each enclosing a potential 'nucleus' (dark basins in the haematoxylin absorbance image). Colour and morphology measurements obtained from these virtual-cells as well as their enclosed nuclei are input into an advanced unsupervised learning model for the identification of epithelium and stromal tissues. Here we exploit two Consensus Clustering (CC) algorithms for the unsupervised recognition of tissue compartments, that consider the consensual opinion of a group of individual clustering algorithms. Unlike most unsupervised segmentation analyses, which depend on a single clustering method, the CC learning models allow for more robust and stable detection of tissue regions. The proposed framework performance has been evaluated on fifty-five hand-annotated tissue images of oropharyngeal tissues. Qualitative and quantitative results of the proposed segmentation algorithm compare favourably with eight popular tissue segmentation strategies. Furthermore, the unsupervised results obtained here outperform those obtained with individual clustering algorithms.

Discrete Mereotopology for Spatial Reasoning in Automated Histological Image Analysis.

Discrete mereotopology (DM) is a first-order spatial logic that fuses together mereology (the theory of parthood relations) and topology to model discrete space. We show how a set of quasitopological functions defined within DM can be mapped to specific operators defined in mathematical morphology (MM) and easily implemented in scientific image processing programs. These functions provide the means to model topological properties of individual regions and spatial relations between them such as contact, overlap, and the relation of part to whole. DM not only extends the expressive power of image processing applications where mathematical morphology is used, but by functioning as a logic it also supplies the formal basis with which to prove the correctness of implemented algorithms as well as providing the computational basis to mechanically reason about segmented digital images using automated reasoning programs. In particular, we show how DM can supply a model-based and algorithmic context to the otherwise blind pixel-based image processing routines still dominating conventional imaging approaches. A number of worked examples drawn from the histological domain are given, including segmentation of cells in culture, identifying basal cell layers from stratified epithelia sections, and cell sorting in blood smears.

Morphologic characterization of cell neighborhoods in neoplastic and preneoplastic epithelium.

OBJECTIVE: To explore tissue organization based on the geometry of cell neighborhoods in histologic preparations. STUDY DESIGN: Local complexity of solid tissues was measured in images of discrete tissue compartments. Exclusive areas associated with cell nuclei (v-cells) were computed using a watershed transform of the nuclear staining intensity. Mathematical morphology was used to define neighborhood membership, distances and identify complete nested neighborhoods. Neighborhood complexity was estimated as the scaling of the number of neighbors relative to reference v-cells. RESULTS: The methodology applied to hematoxylin-eosin-stained sections from normal, dysplastic and neoplastic oral epithelium revealed that the scaling exponent, over a finite range of neighborhood levels, is nonunique and fractional. While scaling values overlapped across classes, the average was marginally higher in neoplastic than in dysplastic and normal epithelia. The best classificatory power of the exponent was 58% correct classification into 3 diagnostic classes (11 levels) and 83% between dysplastic and neoplastic classes (13 levels). CONCLUSION: V-cell architecture retains features of the original tissue classes and demonstrates an increase in tissue disorder in neoplasia. This methodology seems suitable for extracting information from tissues where identification of cell boundaries (and therefore segmentation into individual cells) is unfeasible.

WITHDRAWN: Glyconutrient Supplementation in Patients with Myasthenia Gravis.

Abstract Background: Numerous anecdotal reports claim that patients with myasthenia gravis (MG) may benefit from glyconutrient (GN) supplementation; however, little if any empirical evidence exists. This pilot study examined the benefits of GN supplementation on various objective and subjective physiologic measures related to MG. Methods: Seven (7) male and 12 female volunteer patients (n = 19) with symptomatic MG, ages 16-84 (54.79 +/- 18.36) were randomly assigned to either a GN intervention group (IG) or control-crossover group (CCG) that began the GN dietary intervention at 6 weeks. Patients were assessed at various time intervals over 52 weeks and included physiologic measures using the Quantitative Myasthenia Gravis Score (QMG) along with several self-report measures related to current health status. Results: At baseline, no significant differences (p > 0.05) existed between the CCG and IG on any of the test parameters. At 6 weeks, the IG demonstrated significantly (p < 0.01) improved QMG scores while the CCG remained essentially the same. The CCG, which had begun the dietary intervention protocol 6 weeks into the study, also exhibited significant (p < 0.01) improvement in QMG scores similar to that of the IG. At 52 weeks, the entire sample exhibited significant improvement (p < 0.01) in QMG scores from baseline. Significant (p < 0.05) percentage improvement was also reported from subjective measures of activities of daily living (78.3%), energy (81.0%), endurance (79.6%), productivity (92.8%), and quality of life (88.6%). Conclusions: Dietary support with GN may potentially provide physiologic benefits to patients with MG. Continued efficacy studies employing randomized placebo-controlled trials examining specific GN are warranted to evaluate possible autoimmune benefit.

A model of modes of attention and inattention for artificial perception.

This paper considers several aspects of natural visual attention and its link to wider notions of awareness, natural and artificial, in the context of foveated vision. It builds on a theory of abductive perception; a formal definition for an artificial or robot perceptual system, using objects represented as feature clouds. It proposes a broad, but unifying approach to several aspects of visual attention in the light of this, including autonomic eye gaze movements, aspects of secondary and covert attention, and exogenous (sense driven) and endogenous (task driven) attention. Modes of attentional lapse, commonly referred to as inattentional blindness and change blindness, are also discussed in the context of the model presented.