Monitoring mesenchymal stem cell cultures using image processing and pattern recognition techniques

Stem cells have attracted tremendous interest in recent times due to their promise in providing innovative new treatments for a great range of currently debilitating diseases. This is due to their potential ability to regenerate and repair damaged tissue, and hence restore lost body function, in a manner beyond the body's usual healing process. Bone marrow-derived mesenchymal stem cells or bone marrow stromal cells are one type of adult stem cells that are of particular interest. Since they are derived from a living human adult donor, they do not have the ethical issues associated with the use of human embryonic stem cells. They are also able to be taken from a patient or other donors with relative ease and then grown readily in the laboratory for clinical application. Despite the attractive properties of bone marrow stromal cells, there is presently no quick and easy way to determine the quality of a sample of such cells. Presently, a sample must be grown for weeks and subject to various time-consuming assays, under the direction of an expert cell biologist, to determine whether it will be useful. Hence there is a great need for innovative new ways to assess the quality of cell cultures for research and potential clinical application. The research presented in this thesis investigates the use of computerised image processing and pattern recognition techniques to provide a quicker and simpler method for the quality assessment of bone marrow stromal cell cultures. In particular, aim of this work is to find out whether it is possible, through the use of image processing and pattern recognition techniques, to predict the growth potential of a culture of human bone marrow stromal cells at early stages, before it is readily apparent to a human observer. With the above aim in mind, a computerised system was developed to classify the quality of bone marrow stromal cell cultures based on phase contrast microscopy images. Our system was trained and tested on mixed images of both healthy and unhealthy bone marrow stromal cell samples taken from three different patients. This system, when presented with 44 previously unseen bone marrow stromal cell culture images, outperformed human experts in the ability to correctly classify healthy and unhealthy cultures. The system correctly classified the health status of an image 88% of the time compared to an average of 72% of the time for human experts. Extensive training and testing of the system on a set of 139 normal sized images and 567 smaller image tiles showed an average performance of 86% and 85% correct classifications, respectively. The contributions of this thesis include demonstrating the applicability and potential of computerised image processing and pattern recognition techniques to the task of quality assessment of bone marrow stromal cell cultures. As part of this system, an image normalisation method has been suggested and a new segmentation algorithm has been developed for locating cell regions of irregularly shaped cells in phase contrast images. Importantly, we have validated the efficacy of both the normalisation and segmentation method, by demonstrating that both methods quantitatively improve the classification performance of subsequent pattern recognition algorithms, in discriminating between cell cultures of differing health status. We have shown that the quality of a cell culture of bone marrow stromal cells may be assessed without the need to either segment individual cells or to use time-lapse imaging. Finally, we have proposed a set of features, that when extracted from the cell regions of segmented input images, can be used to train current state of the art pattern recognition systems to predict the quality of bone marrow stromal cell cultures earlier and more consistently than human experts.

Removal of toxic arsenic and antimony from groundwater Spiro Tunnel Bulkhead in Park City Utah using colloidal iron hydroxide : comparison with reverse ssmosis

Verification testing of two model technologies in pilot scale to remove arsenic and antimony based on reverse osmosis and chemical coagulation/filtration systems was conducted in Spiro Tunnel Water Filtration Plant located in Park City, Utah, US. The source water was groundwater in abandoned silver mine, naturally contaminated by 60-80 ppb of arsenic and antimony below 10 ppb. This water represents one of the sources of drinking water for Park City and constitutes about 44% of the water supply. The failure to remove antimony efficiently by coagulation/filtration (only 4.4% removal rate) under design conditions is discussed in terms of the chemistry differences between Sb (III, V) and As (III, V). Removal of Sb(V) at pH > 7, using coagulation/filtration technology, requires much higher (50 to 80 times) concentration of iron (III) than As. The stronger adsorption of arsenate over a wider pH range can be explained by the fact that arsenic acid is tri-protic, whereas antimonic acid is monoprotic. This difference in properties of As(V) and Sb(V) makes antimony (V) more difficult to be efficiently removed in low concentrations of iron hydroxide and alkaline pH waters, especially in concentration of Sb < 10 ppb.

Heat strain, hydration status, and symptoms of heat illness in surface mine workers

The aim of the research program was to evaluate the heat strain, hydration status, and heat illness symptoms experienced by surface mine workers. An initial investigation involved 91 surface miners completing a heat stress questionnaire; assessing the work environment, hydration practices, and heat illness symptom experience. The key findings included 1) more than 80 % of workers experienced at least one symptom of heat illness over a 12 month period; and 2) the risk of moderate symptoms of heat illness increased with the severity of dehydration. These findings highlight a health and safety concern for surface miners, as experiencing symptoms of heat illness is an indication that the physiological systems of the body may be struggling to meet the demands of thermoregulation. To illuminate these findings a field investigation to monitor the heat strain and hydration status of surface miners was proposed. Two preliminary studies were conducted to ensure accurate and reliable data collection techniques. Firstly, a study was undertaken to determine a calibration procedure to ensure the accuracy of core body temperature measurement via an ingestible sensor. A water bath was heated to several temperatures between 23 . 51 ¢ªC, allowing for comparison of the temperature recorded by the sensors and a traceable thermometer. A positive systematic bias was observed and indicated a need for calibration. It was concluded that a linear regression should be developed for each sensor prior to ingestion, allowing for a correction to be applied to the raw data. Secondly, hydration status was to be assessed through urine specific gravity measurement. It was foreseeable that practical limitations on mine sites would delay the time between urine collection and analysis. A study was undertaken to assess the reliability of urine analysis over time. Measurement of urine specific gravity was found to be reliable up to 24 hours post urine collection and was suitable to be used in the field study. Twenty-nine surface miners (14 drillers [winter] and 15 blast crew [summer]) were monitored during a normal work shift. Core body temperature was recorded continuously. Average mean core body temperature was 37.5 and 37.4 ¢ªC for blast crew and drillers, with average maximum body temperatures of 38.0 and 37.9 ¢ªC respectively. The highest body temperature recorded was 38.4 ¢ªC. Urine samples were collected at each void for specific gravity measurement. The average mean urine specific gravity was 1.024 and 1.021 for blast crew and drillers respectively. The Heat Illness Symptoms Index was used to evaluate the experience of heat illness symptoms on shift. Over 70 % of drillers and over 80 % of blast crew reported at least one symptom. It was concluded that 1) heat strain remained within the recommended limits for acclimatised workers; and 2) the majority of workers were dehydrated before commencing their shift, and tend to remain dehydrated for the duration. Dehydration was identified as the primary issue for surface miners working in the heat. Therefore continued study focused on investigating a novel approach to monitoring hydration status. The final aim of this research program was to investigate the influence dehydration has on intraocular pressure (IOP); and subsequently, whether IOP could provide a novel indicator of hydration status. Seven males completed 90 minutes of walking in both a cool and hot climate with fluid restriction. Hydration variables and intraocular pressure were measured at baseline and at 30 minute intervals. Participants became dehydrated during the trial in the heat but maintained hydration status in the cool. Intraocular pressure progressively declined in the trial in the heat but remained relatively stable when hydration was maintained. A significant relationship was observed between intraocular pressure and both body mass loss and plasma osmolality. This evidence suggests that intraocular pressure is influenced by changes in hydration status. Further research is required to determine if intraocular pressure could be utilised as an indirect indicator of hydration status.

Turbulent velocity and suspended sediment concentration measurements in an urban environment of the Brisbane River Flood Plain at Gardens Point on 12-13 January 2011

The flood flow in urbanised areas constitutes a major hazard to the population and infrastructure as seen during the summer 2010-2011 floods in Queensland (Australia). Flood flows in urban environments have been studied relatively recently, although no study considered the impact of turbulence in the flow. During the 12-13 January 2011 flood of the Brisbane River, some turbulence measurements were conducted in an inundated urban environment in Gardens Point Road next to Brisbane's central business district (CBD) at relatively high frequency (50 Hz). The properties of the sediment flood deposits were characterised and the acoustic Doppler velocimeter unit was calibrated to obtain both instantaneous velocity components and suspended sediment concentration in the same sampling volume with the same temporal resolution. While the flow motion in Gardens Point Road was subcritical, the water elevations and velocities fluctuated with a distinctive period between 50 and 80 s. The low frequency fluctuations were linked with some local topographic effects: i.e, some local choke induced by an upstream constriction between stairwells caused some slow oscillations with a period close to the natural sloshing period of the car park. The instantaneous velocity data were analysed using a triple decomposition, and the same triple decomposition was applied to the water depth, velocity flux, suspended sediment concentration and suspended sediment flux data. The velocity fluctuation data showed a large energy component in the slow fluctuation range. For the first two tests at z = 0.35 m, the turbulence data suggested some isotropy. At z = 0.083 m, on the other hand, the findings indicated some flow anisotropy. The suspended sediment concentration (SSC) data presented a general trend with increasing SSC for decreasing water depth. During a test (T4), some long -period oscillations were observed with a period about 18 minutes. The cause of these oscillations remains unknown to the authors. The last test (T5) took place in very shallow waters and high suspended sediment concentrations. It is suggested that the flow in the car park was disconnected from the main channel. Overall the flow conditions at the sampling sites corresponded to a specific momentum between 0.2 to 0.4 m2 which would be near the upper end of the scale for safe evacuation of individuals in flooded areas. But the authors do not believe the evacuation of individuals in Gardens Point Road would have been safe because of the intense water surges and flow turbulence. More generally any criterion for safe evacuation solely based upon the flow velocity, water depth or specific momentum cannot account for the hazards caused by the flow turbulence, water depth fluctuations and water surges.

Experimental and finite element analysis of a double strap joint between steel plates and normal modulus CFRP

Strengthening of steel structures using externally-bonded carbon fibre reinforced polymers ‘CFRP’ is a rapidly developing technique. This paper describes the behaviour of axially loaded flat steel plates strengthened using carbon fibre reinforced polymer sheets. Two steel plates were joined together with adhesive and followed by the application of carbon fibre sheet double strap joint with different bond lengths. The behaviour of the specimens was further investigated by using nonlinear finite element analysis to predict the failure modes and load capacity. In this study, bond failure is the dominant failure mode for normal modulus (240 GPa) CFRP bonding which closely matched the results of finite elements. The predicted ultimate loads from the FE analysis are found to be in good agreement with experimental values.

Application of higher order spectra to identify epileptic EEG

Epilepsy is characterized by the spontaneous and seemingly unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed. An automatic system that detects seizure onsets would allow patients or the people near them to take appropriate precautions, and could provide more insight into this phenomenon. Various methods have been proposed to predict the onset of seizures based on EEG recordings. The use of nonlinear features motivated by the higher order spectra (HOS) has been reported to be a promising approach to differentiate between normal, background (pre-ictal) and epileptic EEG signals. In this work, we made a comparative study of the performance of Gaussian mixture model (GMM) and Support Vector Machine (SVM) classifiers using the features derived from HOS and from the power spectrum. Results show that the selected HOS based features achieve 93.11% classification accuracy compared to 88.78% with features derived from the power spectrum for a GMM classifier. The SVM classifier achieves an improvement from 86.89% with features based on the power spectrum to 92.56% with features based on the bispectrum.

Dynamic texture reconstruction from sparse codes for unusual event detection in crowded scenes

Unusual event detection in crowded scenes remains challenging because of the diversity of events and noise. In this paper, we present a novel approach for unusual event detection via sparse reconstruction of dynamic textures over an overcomplete basis set, with the dynamic texture described by local binary patterns from three orthogonal planes (LBPTOP). The overcomplete basis set is learnt from the training data where only the normal items observed. In the detection process, given a new observation, we compute the sparse coefficients using the Dantzig Selector algorithm which was proposed in the literature of compressed sensing. Then the reconstruction errors are computed, based on which we detect the abnormal items. Our application can be used to detect both local and global abnormal events. We evaluate our algorithm on UCSD Abnormality Datasets for local anomaly detection, which is shown to outperform current state-of-the-art approaches, and we also get promising results for rapid escape detection using the PETS2009 dataset.

A proposed role for models and simulations in management research

Overall, computer models and simulations have a rather disappointing record within the management sciences as a tool for predicting the future. Social and market environments can be influenced by an overwhelming number of variables, and it is therefore difficult to use computer models to make forecasts or to test hypotheses concerning the relationship between individual behaviours and macroscopic outcomes. At the same time, however, advocates of computer models argue that they can be used to overcome the human mind's inability to cope with several complex variables simultaneously or to understand concepts that are highly counterintuitive. This paper seeks to bridge the gap between these two perspectives by suggesting that management research can indeed benefit from computer models by using them to formulate fruitful hypotheses.