A Knowledge-based System for Intelligent Support in Pharmacogenomics Evidence Assessment: Ontology-driven Evidence Representation, Retrieval, Classification and Interpretation

Thesis (Ph.D.)--University of Washington, 2016-06

Proteome analysis of extracellular proteins regulated by the las and rhl quorum sensing systems in Pseudomonas aeruginosa PAO1

The las and rhl quorum sensing (QS) systems regulate the expression of several genes in response to cell density changes in Pseudomonas aeruginosa. Many of these genes encode surface-associated or secreted virulence factors. Proteins from stationary phase culture supernatants were collected from wild-type and P. aeruginosa PAO1 mutants deficient in one or more of the lasRI, rhIRI and vfr genes and analysed using two-dimensional gel electrophoresis. All mutants released significantly lower amounts of protein than the wild-type. Protein spot patterns from each strain were compared using image analysis and visible spot differences were identified using mass spectrometry. Several previously unknown OS-regulated proteins were characterized, including an aminopeptidase (PA2939), an endoproteinase (PrpL) and a unique 'hypothetical' protein (PA0572), which could not be detected in the culture supernatants of Delta/as mutants, although they were unaffected in Deltarhl mutants. Chitin-binding protein (CbpD) and a hypothetical protein (PA4944) with similarity to host factor I (HF-1) could not be detected when any of the lasRI or rhIRI genes were disrupted. Fourteen proteins were present at significantly greater levels in the culture supernatants of OS mutants, suggesting that QS may also negatively control the expression of some genes. Increased levels of two-partner secretion exoproteins (PA0041 and PA4625) were observed and may be linked to increased stability of their cognate transporters in a CS-defective background. Known QS-regulated extracellular proteins, including elastase (lasB), LasA protease (lasA) and alkaline metalloproteinase (aprA) were also detected.

Aspects of Markov Chains and Particle Systems

Thesis (Ph.D.)--University of Washington, 2016-06

The 1000 brightest hipass galaxies: The H I mass function and Omega(H I)

We present a new, accurate measurement of the H I mass function of galaxies from the HIPASS Bright Galaxy Catalog, a sample of 1000 galaxies with the highest H I peak flux densities in the southern (delta

Does terrestrial epidemiology apply to marine systems?

Most of epidemiological theory has been developed for terrestrial systems, but the significance of disease in the ocean is now being recognized. However, the extent to which terrestrial epidemiology can be directly transferred to marine systems is uncertain. Many broad types of disease-causing organism occur both on land and in the sea, and it is clear that some emergent disease problems in marine environments are caused by pathogens moving from terrestrial to marine systems. However, marine systems are qualitatively different from terrestrial environments, and these differences affect the application of modelling and management approaches that have been developed for terrestrial systems. Phyla and body plans are more diverse in marine environments and marine organisms have different life histories and probably different disease transmission modes than many of their terrestrial counterparts. Marine populations are typically more open than terrestrial ones, with the potential for long-distance dispersal of larvae. Potentially, this might enable unusually rapid propagation of epidemics in marine systems, and there are several examples of this. Taken together, these differences will require the development of new approaches to modelling and control of infectious disease in the ocean.

Classification and analysis of integrating frameworks in multiscale modelling

Chemical engineers are turning to multiscale modelling to extend traditional modelling approaches into new application areas and to achieve higher levels of detail and accuracy. There is, however, little advice available on the best strategy to use in constructing a multiscale model. This paper presents a starting point for the systematic analysis of multiscale models by defining several integrating frameworks for linking models at different scales. It briefly explores how the nature of the information flow between the models at the different scales is influenced by the choice of framework, and presents some restrictions on model-framework compatibility. The concepts are illustrated with reference to the modelling of a catalytic packed bed reactor. (C) 2004 Elsevier Ltd. All rights reserved.

Determination of external and internal mass transfer limitation in nitrifying microbial aggregates

In this article we present a study of the effects of external and internal mass transfer limitation of oxygen in a nitrifying system. The oxygen uptake rates (OUR) were measured on both a macro-scale with a respirometric reactor using off-gas analysis (Titrimetric and Off-Gas Analysis (TOGA) sensor) and on a micro-scale with microsensors. These two methods provide independent, accurate measurements of the reaction rates and concentration profiles around and in the granules. The TOGA sensor and micro-sensor measurements showed a significant external mass transfer effect at low dissolved oxygen (DO) concentrations in the bulk liquid while it was insignificant at higher DO concentrations. The oxygen distribution with anaerobic or anoxic conditions in the center clearly shows major mass transfer limitation in the aggregate interior. The large drop in DO concentration of 22 - 80% between the bulk liquid and aggregate surface demonstrates that the external mass transfer resistance is also highly important. The maximum OUR even for floccular biomass was only attained at much higher DO concentrations ( approximate to 8 mg/L) than typically used in such systems. For granules, the DO required for maximal activity was estimated to be > 20mg/L, clearly indicating the effects of the major external and internal mass transfer limitations on the overall biomass activity. Smaller aggregates had a larger volumetric OUR indicating that the granules may have a lower activity in the interior part of the aggregate. (C) 2004 Wiley Periodicals, Inc.

Mass uptake study of the diffusion of water and SBF into poly(2-hydroxyethyl methacrylate-co-tetrahydrofurfuryI methacrylate) containing aspirin or vitamin B-12

The ingress of water and Kokubo simulated body fluid (SBF) into poly (2-hydroxyethyl methacrylate) (PHEMA), and its co-polymers with tetrahydrofurduryl methacrylate (THFMA), loaded with either one of two model drugs, vitamin 1312 or aspirin, was studied by mass uptake over the temperature range 298-318 K. The polymers were studied as cylinders and were loaded with either 5 wt% or 10 wt% of the drugs. From DSC studies it was observed that vitamin B-12 behaved as a physical cross-linker restricting chain segmental mobility, and so had a small anti-plasticisation effect on PHEMA and the co-polymers rich in HEMA, but almost no effect on the T-g of co-polymers rich in THFMA. On the other hand, aspirin exhibited a plasticising effect on PHEMA and the copolymers. All of the polymers were found to absorb water and SBF according to a Fickian diffusion mechanism. The polymers were all found to swell to a greater extent in SBF than in water, which was attributed to the presence of Tris buffer in the SBF. The sorptions of the two penetrants were found to follow Fickian kinetics in all cases and the diffusion coefficients at 310 K for SBF were found to be smaller than those for water, except for the polymers containing aspirin where the diffusion coefficients were higher than for the other systems. For example, for sorption into PHEMA the diffusion coefficient for water was 1.41 X 10(-11) m(2)/s and for SBF was 0.79 x 10-11 m(2)/s, but in the presence of 5 wt% aspirin the corresponding values were 1.27 x 10(-1)1 m(2)/s and 1.25 x 10(-11) m(2)/s, respectively. The corresponding values for PHEMA loaded with 5 wt% B-12 were 1.25 x 10(-11) m(2)/s and 0.74 x 10(-11) m(2)/s, respectively.

Application and evaluation of classification trees for screening unwanted plants

Risk assessment systems for introduced species are being developed and applied globally, but methods for rigorously evaluating them are still in their infancy. We explore classification and regression tree models as an alternative to the current Australian Weed Risk Assessment system, and demonstrate how the performance of screening tests for unwanted alien species may be quantitatively compared using receiver operating characteristic (ROC) curve analysis. The optimal classification tree model for predicting weediness included just four out of a possible 44 attributes of introduced plants examined, namely: (i) intentional human dispersal of propagules; (ii) evidence of naturalization beyond native range; (iii) evidence of being a weed elsewhere; and (iv) a high level of domestication. Intentional human dispersal of propagules in combination with evidence of naturalization beyond a plants native range led to the strongest prediction of weediness. A high level of domestication in combination with no evidence of naturalization mitigated the likelihood of an introduced plant becoming a weed resulting from intentional human dispersal of propagules. Unlikely intentional human dispersal of propagules combined with no evidence of being a weed elsewhere led to the lowest predicted probability of weediness. The failure to include intrinsic plant attributes in the model suggests that either these attributes are not useful general predictors of weediness, or data and analysis were inadequate to elucidate the underlying relationship(s). This concurs with the historical pessimism that we will ever be able to accurately predict invasive plants. Given the apparent importance of propagule pressure (the number of individuals of an species released), future attempts at evaluating screening model performance for identifying unwanted plants need to account for propagule pressure when collating and/or analysing datasets. The classification tree had a cross-validated sensitivity of 93.6% and specificity of 36.7%. Based on the area under the ROC curve, the performance of the classification tree in correctly classifying plants as weeds or non-weeds was slightly inferior (Area under ROC curve = 0.83 +/- 0.021 (+/- SE)) to that of the current risk assessment system in use (Area under ROC curve = 0.89 +/- 0.018 (+/- SE)), although requires many fewer questions to be answered.

Patterns in complex systems modeling

The design, development, and use of complex systems models raises a unique class of challenges and potential pitfalls, many of which are commonly recurring problems. Over time, researchers gain experience in this form of modeling, choosing algorithms, techniques, and frameworks that improve the quality, confidence level, and speed of development of their models. This increasing collective experience of complex systems modellers is a resource that should be captured. Fields such as software engineering and architecture have benefited from the development of generic solutions to recurring problems, called patterns. Using pattern development techniques from these fields, insights from communities such as learning and information processing, data mining, bioinformatics, and agent-based modeling can be identified and captured. Collections of such 'pattern languages' would allow knowledge gained through experience to be readily accessible to less-experienced practitioners and to other domains. This paper proposes a methodology for capturing the wisdom of computational modelers by introducing example visualization patterns, and a pattern classification system for analyzing the relationship between micro and macro behaviour in complex systems models. We anticipate that a new field of complex systems patterns will provide an invaluable resource for both practicing and future generations of modelers.

Multisensorial obstacles detection and classification for ADAS and autonomous driving

A reliable perception of the real world is a key-feature for an autonomous vehicle and the Advanced Driver Assistance Systems (ADAS). Obstacles detection (OD) is one of the main components for the correct reconstruction of the dynamic world. Historical approaches based on stereo vision and other 3D perception technologies (e.g. LIDAR) have been adapted to the ADAS first and autonomous ground vehicles, after, providing excellent results. The obstacles detection is a very broad field and this domain counts a lot of works in the last years. In academic research, it has been clearly established the essential role of these systems to realize active safety systems for accident prevention, reflecting also the innovative systems introduced by industry. These systems need to accurately assess situational criticalities and simultaneously assess awareness of these criticalities by the driver; it requires that the obstacles detection algorithms must be reliable and accurate, providing: a real-time output, a stable and robust representation of the environment and an estimation independent from lighting and weather conditions. Initial systems relied on only one exteroceptive sensor (e.g. radar or laser for ACC and camera for LDW) in addition to proprioceptive sensors such as wheel speed and yaw rate sensors. But, current systems, such as ACC operating at the entire speed range or autonomous braking for collision avoidance, require the use of multiple sensors since individually they can not meet these requirements. It has led the community to move towards the use of a combination of them in order to exploit the benefits of each one. Pedestrians and vehicles detection are ones of the major thrusts in situational criticalities assessment, still remaining an active area of research. ADASs are the most prominent use case of pedestrians and vehicles detection. Vehicles should be equipped with sensing capabilities able to detect and act on objects in dangerous situations, where the driver would not be able to avoid a collision. A full ADAS or autonomous vehicle, with regard to pedestrians and vehicles, would not only include detection but also tracking, orientation, intent analysis, and collision prediction. The system detects obstacles using a probabilistic occupancy grid built from a multi-resolution disparity map. Obstacles classification is based on an AdaBoost SoftCascade trained on Aggregate Channel Features. A final stage of tracking and fusion guarantees stability and robustness to the result.

Blood type classification using computer vision and machine learning

In emergency situations, where time for blood transfusion is reduced, the O negative blood type (the universal donor) is administrated. However, sometimes even the universal donor can cause transfusion reactions that can be fatal to the patient. As commercial systems do not allow fast results and are not suitable for emergency situations, this paper presents the steps considered for the development and validation of a prototype, able to determine blood type compatibilities, even in emergency situations. Thus it is possible, using the developed system, to administer a compatible blood type, since the first blood unit transfused. In order to increase the system’s reliability, this prototype uses different approaches to classify blood types, the first of which is based on Decision Trees and the second one based on support vector machines. The features used to evaluate these classifiers are the standard deviation values, histogram, Histogram of Oriented Gradients and fast Fourier transform, computed on different regions of interest. The main characteristics of the presented prototype are small size, lightweight, easy transportation, ease of use, fast results, high reliability and low cost. These features are perfectly suited for emergency scenarios, where the prototype is expected to be used.

Efficient training of RBF networks for classification.

Radial Basis Function networks with linear outputs are often used in regression problems because they can be substantially faster to train than Multi-layer Perceptrons. For classification problems, the use of linear outputs is less appropriate as the outputs are not guaranteed to represent probabilities. We show how RBFs with logistic and softmax outputs can be trained efficiently using the Fisher scoring algorithm. This approach can be used with any model which consists of a generalised linear output function applied to a model which is linear in its parameters. We compare this approach with standard non-linear optimisation algorithms on a number of datasets.