Minimal Infrastructure Radio Frequency Home Localisation Systems

The ability to track the location of a subject in their home allows the provision of a number of location based services, such as remote activity monitoring, context sensitive prompts and detection of safety critical situations such as falls. Such pervasive monitoring functionality offers the potential for elders to live at home for longer periods of their lives with minimal human supervision. The focus of this thesis is on the investigation and development of a home roomlevel localisation technique which can be readily deployed in a realistic home environment with minimal hardware requirements. A conveniently deployed Bluetooth ® localisation platform is designed and experimentally validated throughout the thesis. The platform adopts the convenience of a mobile phone and the processing power of a remote location calculation computer. The use of Bluetooth ® also ensures the extensibility of the platform to other home health supervision scenarios such as wireless body sensor monitoring. Central contributions of this work include the comparison of probabilistic and nonprobabilistic classifiers for location prediction accuracy and the extension of probabilistic classifiers to a Hidden Markov Model Bayesian filtering framework. New location prediction performance metrics are developed and signicant performance improvements are demonstrated with the novel extension of Hidden Markov Models to higher-order Markov movement models. With the simple probabilistic classifiers, location is correctly predicted 80% of the time. This increases to 86% with the application of the Hidden Markov Models and 88% when high-order Hidden Markov Models are employed. Further novelty is exhibited in the derivation of a real-time Hidden Markov Model Viterbi decoding algorithm which presents all the advantages of the original algorithm, while producing location estimates in real-time. Significant contributions are also made to the field of human gait-recognition by applying Bayesian filtering to the task of motion detection from accelerometers which are already present in many mobile phones. Bayesian filtering is demonstrated to enable a 35% improvement in motion recognition rate and even enables a floor recognition rate of 68% using only accelerometers. The unique application of time-varying Hidden Markov Models demonstrates the effect of integrating these freely available motion predictions on long-term location predictions

Antimicrobial efficacy of plant essential oils and extracts against Escherichia coli

The efficacies of 11 plant-derived antimicrobials were evaluated against Escherichia coli in vitro in solution at room temperature. These included lemongrass, cinnamon, and oregano essential oils and their active components (citral, cinnamaldehyde, and carvacrol, respectively). Allspice and clove bud oils and olive, green tea, and grape seed extracts were also studied. The efficacies of the antimicrobials were both concentration- and exposure time-dependent. The essential oils and their active components demonstrated statistically significant >5.0-log10 reductions within 1-10 min. The plant extracts were less effective; green tea and grape seed extracts required 24 h before significant reductions were observed (1.93-log10 and 5.05-log10, respectively). Nevertheless, olive extract exhibited a reduction of ∼5-log10 within 30 min. Most of these plant-derived compounds exhibited strong bactericidal activity and can potentially be applied as alternatives to chemicals for foods/food contact surfaces since they are generally recognized as safe (GRAS) for human consumption. They may also be useful in applications in which other antimicrobials have reduced efficacy (e.g., in the presence of organics) or used with sensitive populations that are unable to tolerate exposure to harsher chemicals (e.g., elderly care facilities). These compounds could be used alone, in combination, or with fast-acting antimicrobials to provide a long-lasting residual.United States Department of Agriculture [2010-51300-21760]12 month embargo; published online: 1 March 2019This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Design and construction of modular genetic devices and the enzymatic hydrolysis of lignocellulosic biomass

The enzymatic deconstruction of lignocellulosic plant biomass is performed by specialist microbial species. It is a ubiquitous process within nature and central to the global recycling of carbon and energy. Lignocellulose is a complex heteropolymer, highly recalcitrant and resistant to hydrolysis due to the major polysaccharide cellulose existing as a crystalline lattice, intimately associated with a disordered sheath of hemicellulosic polysaccharides and lignin. In this thesis I aim to transfer the highly efficient cellulolytic mechanism of the bacterium Cellulomonas fimi, to that of a suitably amenable and genetically tractable expression host, in the hopes of better understanding the enzymatic hydrolysis of lignocellulose. Using tools and concepts from molecular biology and synthetic biology, I constructed a library of standardised genetic parts derived from C. fimi, each encoding a known enzymatic activity involved in the hydrolysis of cellulose, mannan or xylan; three of the major polysaccharides present in lignocellulose. Characterization assays were performed on individual parts to confirm enzymatic activity and compare efficiencies against a range of substrates. Results then informed the rational design and construction of parts into modular devices. The resultant genetic devices were introduced into the expression hosts Escherichia coli and Citrobacter freundii, and transformed strains were assayed for the ability to utilize various forms of xylan, mannan and cellulose as a sole carbon source. Results identified devices which when expressed by either host showed growth on the respective carbon sources. Notably, devices with improved activity against amorphous cellulose, crystalline cellulose, mannan and xylan were determined. Recombinant cellulase expressing strains of E. coli and C. freundii were shown capable of both deconstruction and utilization of pure cellulose paper as a sole carbon source. Moreover, this capacity was shown to be entirely unhindered when C. freundii strains were cultured in saline media. These findings show promise in developing C. freundii for bioprocessing of biomass in sea water, so as to reduce the use of fresh water resources and improve sustainability as well as process economics. Work presented in this thesis contributes towards understanding the complementarities and synergies of the enzymes responsible for lignocellulose hydrolysis. Moreover, the research emphasizes the merits of standardizing genetic parts used within metabolic engineering projects and how adopting such design principles can expedite the research process

Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases.

Yuan et al. recently described an independent evaluation of several phenotype-driven gene prioritization methods for Mendelian disease on two separate, clinical datasets. Although they attempted to use default settings for each tool, we describe three key differences from those we currently recommend for our Exomiser and PhenIX tools. These influence how variant frequency, quality and predicted pathogenicity are used for filtering and prioritization. We propose that these differences account for much of the discrepancy in performance between that reported by them (15-26% diagnoses ranked top by Exomiser) and previously published reports by us and others (72-77%). On a set of 161 singleton samples, we show using these settings increases performance from 34% to 72% and suggest a reassessment of Exomiser and PhenIX on their datasets using these would show a similar uplift

The Effectiveness of Using Sentence Makers in Improving Writing Performance among Pupils in Lubok Antu Rural Schools

ESL writing is a critical problem in Lubok Antu. This study investigated the effectiveness of using Sentence Maker in improving ESL writing among the Year 5 and Year 6 pupils in Lubok Antu rural schools. For this study, quantitative data were required. A number of 22 ESL learners were asked to write an essay as the pre-test. All the 22 essays were carefully rated and pre-test data were obtained. The results revealed the low performance in ESL writing. Then, intervention was introduced in the while-process. Learners were introduced to the Sentence Maker tool to visually aid them to understand sentence pattern more clearly. Post-test was conducted to collect data on the grades after intervention was done. Comparison between the pre-test and the post-test data revealed that Sentence Maker has been a useful tool that aids in improving learners’ ESL writing. The findings of this study may benefit the primary ESL learners particularly those from among the rural schools in Lubok Antu. Educators may also find this tool as beneficial as it is easy to use. In the near future study should include the common errors in ESL writing among the rural ESL learners in the district and their perception in using Sentence Maker to address the errors

Assessing the applicability of the Revised Universal Soil Loss Equation (RUSLE) to Irish Catchments

Elevated suspended sediment concentrations in fluvial environments have important implications for system ecology and even small concentrations may have serious consequences for sensitive ecosystems or organisms, such as freshwater pearl mussels (<i>Margaritifera margaritifera</i>). Informed decision making is therefore required for land managers to understand and control soil erosion and sediment delivery to the river network. However, given that monitoring of sediment fluxes requires financial and human resources which are often limited at a national scale, sediment mobilisation and delivery models are commonly used for sediment yield estimation and management. The Revised Universal Soil Loss Equation (RUSLE) is the most widely used model for overland flow erosion and can, when combined with a sediment delivery ratio (SDR), provide reasonable sediment load estimations for a catchment. This paper presents RUSLE factors established from extant GIS and rainfall datasets that are incorporated into a flexible catchment modelling approach. We believe that this is the first time that results from a RUSLE application at a national scale are tested against measured sediment yield values available from Ireland. An initial assessment of RUSLE applied to Irish conditions indicates an overestimation of modelled sediment yield values for most of the selected catchments. Improved methods for model and SDR factors estimation are needed to account for Irish conditions and catchment characteristics. Nonetheless, validation and testing of the model in this study using observed values is an important step towards more effective sediment yield modelling tools for nationwide applications