The use of knowledge discovery databases in the identification of patients with colorectal cancer

Colorectal cancer is one of the most common forms of malignancy with 35,000 new patients diagnosed annually within the UK. Survival figures show that outcomes are less favourable within the UK when compared with the USA and Europe with 1 in 4 patients having incurable disease at presentation as of data from 2000.Epidemiologists have demonstrated that the incidence of colorectal cancer is highest on the industrialised western world with numerous contributory factors. These range from a genetic component to concurrent medical conditions and personal lifestyle. In addition, data also demonstrates that environmental changes play a significant role with immigrants rapidly reaching the incidence rates of the host country.Detection of colorectal cancer remains an important and evolving aspect of healthcare with the aim of improving outcomes by earlier diagnosis. This process was initially revolutionised within the UK in 2002 with the ACPGBI 2 week wait guidelines to facilitate referrals form primary care and has subsequently seen other schemes such as bowel cancer screening introduced to augment earlier detection rates. Whereas the national screening programme is dependent on FOBT the standard referral practice is dependent upon a number of trigger symptoms that qualify for an urgent referral to a specialist for further investigations. This process only identifies 25-30% of those with colorectal cancer and remains a labour intensive process with only 10% of those seen in the 2 week wait clinics having colorectal cancer.This thesis hypothesises whether using a patient symptom questionnaire in conjunction with knowledge discovery techniques such as data mining and artificial neural networks could identify patients at risk of colorectal cancer and therefore warrant urgent further assessment. Artificial neural networks and data mining methods are used widely in industry to detect consumer patterns by an inbuilt ability to learn from previous examples within a dataset and model often complex, non-linear patterns. Within medicine these methods have been utilised in a host of diagnostic techniques from myocardial infarcts to its use in the Papnet cervical smear programme for cervical cancer detection.A linkert based questionnaire of those attending the 2 week wait fast track colorectal clinic was used to produce a ‘symptoms’ database. This was then correlated with individual patient diagnoses upon completion of their clinical assessment. A total of 777 patients were included in the study and their diagnosis categorised into a dichotomous variable to create a selection of datasets for analysis. These data sets were then taken by the author and used to create a total of four primary databases based on all questions, 2 week wait trigger symptoms, Best knowledge questions and symptoms identified in Univariate analysis as significant. Each of these databases were entered into an artificial neural network programme, altering the number of hidden units and layers to obtain a selection of outcome models that could be further tested based on a selection of set dichotomous outcomes. Outcome models were compared for sensitivity, specificity and risk. Further experiments were carried out with data mining techniques and the WEKA package to identify the most accurate model. Both would then be compared with the accuracy of a colorectal specialist and GP.Analysis of the data identified that 24% of those referred on the 2 week wait referral pathway failed to meet referral criteria as set out by the ACPGBI. The incidence of those with colorectal cancer was 9.5% (74) which is in keeping with other studies and the main symptoms were rectal bleeding, change in bowel habit and abdominal pain. The optimal knowledge discovery database model was a back propagation ANN using all variables for outcomes cancer/not cancer with sensitivity of 0.9, specificity of 0.97 and LR 35.8. Artificial neural networks remained the more accurate modelling method for all the dichotomous outcomes.The comparison of GP’s and colorectal specialists at predicting outcome demonstrated that the colorectal specialists were the more accurate predictors of cancer/not cancer with sensitivity 0.27 and specificity 0.97, (95% CI 0.6-0.97, PPV 0.75, NPV 0.83) and LR 10.6. When compared to the KDD models for predicting the same outcome, once again the ANN models were more accurate with the optimal model having sensitivity 0.63, specificity 0.98 (95% CI 0.58-1, PPV 0.71, NPV 0.96) and LR 28.7.The results demonstrate that diagnosis colorectal cancer remains a challenging process, both for clinicians and also for computation models. KDD models have been shown to be consistently more accurate in the prediction of those with colorectal cancer than clinicians alone when used solely in conjunction with a questionnaire. It would be ill conceived to suggest that KDD models could be used as a replacement to clinician- patient interaction but they may aid in the acceleration of some patients for further investigations or ‘straight to test’ if used on those referred as routine patients

Soil erosion on buried archaeological sites in arable areas: a modelling approach.

The Monuments at risk Survey (MARS) of England (Darvill and Fulton, 1998) concluded that the dominant agent of damage to archaeological sites is intensive agriculture. No such equivalent or similar study exists for Scotland. This study aimed to assess the threat of soil erosion posed to archaeological cropmark sites across an 80 x 60 km study area by quantitatively modelling soil erosion rates. Archaeological sites are widely distributed across lowland mid-Scotland and are clustered on arable land. Focus was placed on cropmark features since very little is known about them and damage rate is difficult to ascertain without excavation. 2849 registered (NMRS) archaeological sites are present in the study area, 1707 of which are cropmarks. To meet the aim, the total erosion budget was modelled in its component parts: water and tillage translocation. Firstly, water erosion and deposition were modelled using Desmet and Govers’s (1995) simple model accounting for field boundary structure and multiple flow directions. Secondly, tillage translocation was modelled using ARCTILL. The 137Cs tracer technique was applied at four field sites containing cropmark archaeological features. Transect based sampling was applied using 25 m x 25 m cells to coincide exactly with the GIS grid system. Derived erosion/deposition rates were then used to optimise the water and tillage models at each field site, from which a general optimised net model was defined and applied at the regional scale. The effect of field boundaries on patterns and magnitudes of potential overland flow and subsequent erosion/deposition was found to be significant and worthy of further research. The archaeological features at Loanleven (NO 058 252) and Littlelour (NO 479 444) were found to be under serious threat from erosion caused by ploughing practices up to -1.34 kg m-2 yr-1 (-1.14 mm yr-1) and -2.14 kg m-2 yr-1 (-1.34 mm yr-1) respectively. Tillage erosion on average has contributed 75% and 69% at the Loanleven and Blairhall (NO 116 280) sites respectively clearly demonstrating the significance of the process. The highest erosion rates were located on strongly convex slope sections, yet statistically were related only weakly. These loci were strongly correlated with topsoil depths. For the whole study area, the general optimised net model predicted 65% of all archaeological sites (2849 in total) as being on land experiencing net erosion. Of some 1707 cropmark sites, 63% were predicted as being on land experiencing net erosion. 547 cropmark sites (32% of cropmarks) and 1053 (37% of total) of all archaeological sites present within the study area exceeded the soil loss tolerance threshold (0.13 kg m-2 yr-1). This research underlines intensive agriculture as being the main damaging agent of buried archaeology across the study area

Interconnection of solar home systems as a path to bottom-up electrification

Solar Home Systems (SHSs) have revolutionised electricity access for off grid communities, but have a number of significant limitations. They have limited demand diversity, produce excess energy and lack a clear pathway to scale alongside growing energy demand. Electrical interconnection of existing installed SHSs to create minigrids could offer a way to both scale up energy demand and make use of wasted energy. This bottom-up approach has the potential to be flexible to the changing needs of communities, by using SHSs as a starting point for wider electrification, rather than the end goal. Despite this potential, little analytical work has been undertaken to model SHS interconnection, particularly accounting for demand diversity and long-term system performance. This thesis presents a time sequential stochastic model of interconnected SHSs, to investigate these systems under multi-year operational timescales at high temporal resolution. It is shown for case study systems based on real SHS topologies that there exists significant demand diversity, with small clusters of 20 houses with identical appliances exhibiting an average peak demand of less than 70% of the combined worst-case peak for individual SHSs. Excess generated energy is shown to be an average of 100 Wh a day for the smaller system types and 1000Wh a day for larger systems. Interconnection of these systems demonstrates a significant reductions in LCOE for all system types compared to islanded operation, through more optimal dispatch of battery storage assets and use of excess energy. This resulted in a final LCOE of $0.63/kWh for a network of 12 large SHSs - a reduction of 48.12$0.703/kWh for a network of 12 small SHSs - a reduction of 55.23% compared to islanded operation. This informed an investigation of possible operational business models for a network of SHSs, with three approaches proposed - an Energy System Operator with direct control over all users’ systems, an Aggregator model, where the system operator facilitates an energy market and a Peer-to-Peer model with direct consumer to consumer energy trading. This thesis provides a robust evidence base for SHS interconnection – demonstrating that the approach can lower cost of energy and facilitate demand growth for off grid energy consumers and proposes appropriate business models to deliver this affordable and clean energy.Solar Home Systems (SHSs) have revolutionised electricity access for off grid communities, but have a number of significant limitations. They have limited demand diversity, produce excess energy and lack a clear pathway to scale alongside growing energy demand. Electrical interconnection of existing installed SHSs to create minigrids could offer a way to both scale up energy demand and make use of wasted energy. This bottom-up approach has the potential to be flexible to the changing needs of communities, by using SHSs as a starting point for wider electrification, rather than the end goal. Despite this potential, little analytical work has been undertaken to model SHS interconnection, particularly accounting for demand diversity and long-term system performance. This thesis presents a time sequential stochastic model of interconnected SHSs, to investigate these systems under multi-year operational timescales at high temporal resolution. It is shown for case study systems based on real SHS topologies that there exists significant demand diversity, with small clusters of 20 houses with identical appliances exhibiting an average peak demand of less than 70% of the combined worst-case peak for individual SHSs. Excess generated energy is shown to be an average of 100 Wh a day for the smaller system types and 1000Wh a day for larger systems. Interconnection of these systems demonstrates a significant reductions in LCOE for all system types compared to islanded operation, through more optimal dispatch of battery storage assets and use of excess energy. This resulted in a final LCOE of $0.63/kWh for a network of 12 large SHSs - a reduction of 48.12$0.703/kWh for a network of 12 small SHSs - a reduction of 55.23% compared to islanded operation. This informed an investigation of possible operational business models for a network of SHSs, with three approaches proposed - an Energy System Operator with direct control over all users’ systems, an Aggregator model, where the system operator facilitates an energy market and a Peer-to-Peer model with direct consumer to consumer energy trading. This thesis provides a robust evidence base for SHS interconnection – demonstrating that the approach can lower cost of energy and facilitate demand growth for off grid energy consumers and proposes appropriate business models to deliver this affordable and clean energy

A simulation-based evaluation of the benefits and barriers to interconnected solar home systems in East Africa

This paper outlines the relative advantages and disadvantages of interconnecting Solar Home Systems (SHSs) to form micro-grids. Real world remote monitoring data from a number of SHSs operated by BBOXX in Rwanda is analyzed and it is shown that significant demand diversity and differing patterns of energy use exist in SHSs. Significant variation in daily demand, is demonstrated for identical SHSs from 0-10 Wh/day up to 110 Wh/day. Around 65% of generated energy is currently unused and could be utilised to connect new customers and increase the demand of existing customers if systems were interconnected

Detection of respiratory viruses and bacteria in children using a twenty-two target reverse-transcription real-time PCR (RT-qPCR) panel

Background: Rapid detection of the wide range of viruses and bacteria that cause respiratory infection in children is important for patient care and antibiotic stewardship. We therefore designed and evaluated a ready-to-use 22 target respiratory infection reverse-transcription real-time polymerase chain reaction (RT-qPCR) panel to determine if this would improve detection of these agents at our pediatric hospital. Methods: RT-qPCR assays for twenty-two target organisms were dried-down in individual wells of 96 well plates and saved at room temperature. Targets included 18 respiratory viruses and 4 bacteria. After automated nucleic acid extraction of nasopharyngeal aspirate (NPA) samples, rapid qPCR was performed. RT-qPCR results were compared with those obtained by the testing methods used at our hospital laboratories. Results: One hundred fifty-nine pediatric NPA samples were tested with the RT-qPCR panel. One or more respiratory pathogens were detected in 132/159 (83%) samples. This was significantly higher than the detection rate of standard methods (94/159, 59%) (P\u3c0.001). This difference was mainly due to improved RT-qPCR detection of rhinoviruses, parainfluenza viruses, bocavirus, and coronaviruses. The panel internal control assay performance remained stable at room temperature storage over a two-month testing period. Conclusion: The RT-qPCR panel was able to identify pathogens in a high proportion of respiratory samples. The panel detected more positive specimens than the methods in use at our hospital. The pre-made panel format was easy to use and rapid, with results available in approximately 90 minutes. We now plan to determine if use of this panel improves patient care and antibiotic stewardship

High-frequency water quality monitoring in an urban catchment: hydrochemical dynamics, primary production and implications for the Water Framework Directive

This paper describes the hydrochemistry of a lowland, urbanised river-system, The Cut in England, using in situ sub-daily sampling. The Cut receives effluent discharges from four major sewage treatment works serving around 190,000 people. These discharges consist largely of treated water, originally abstracted from the River Thames and returned via the water supply network, substantially increasing the natural flow. The hourly water quality data were supplemented by weekly manual sampling with laboratory analysis to check the hourly data and measure further determinands. Mean phosphorus and nitrate concentrations were very high, breaching standards set by EU legislation. Though 56% of the catchment area is agricultural, the hydrochemical dynamics were significantly impacted by effluent discharges which accounted for approximately 50% of the annual P catchment input loads and, on average, 59% of river flow at the monitoring point. Diurnal dissolved oxygen data demonstrated high in-stream productivity. From a comparison of high frequency and conventional monitoring data, it is inferred that much of the primary production was dominated by benthic algae, largely diatoms. Despite the high productivity and nutrient concentrations, the river water did not become anoxic and major phytoplankton blooms were not observed. The strong diurnal and annual variation observed showed that assessments of water quality made under the Water Framework Directive (WFD) are sensitive to the time and season of sampling. It is recommended that specific sampling time windows be specified for each determinand, and that WFD targets should be applied in combination to help identify periods of greatest ecological risk. This article is protected by copyright. All rights reserved

tert-Butoxy­triphenyl­silane

The title compound, C22H24OSi or Ph3SiOtBu, shows a distorted tetra­hedral coordination sphere around the Si atom. The C—O—Si angle is 135.97 (12)° and the O—Si distance is 1.6244 (13) Å. The mol­ecules are held together by weak inter­actions only. An H⋯H distance of 2.2924 (7) Å is found between aryl H atoms and is the shortest inter­molecular distance in the structure. With regard to the broad applicability of R 3SiO structural motifs in all fields of chemistry, the mol­ecule demonstrates a common model system for silicon centers surrounded by sterically demanding substituents

The water quality of the River Enborne, UK: observations from high-frequency Monitoring in a rural, lowland river system

This paper reports the results of a 2-year study of water quality in the River Enborne, a rural river in lowland England. Concentrations of nitrogen and phosphorus species and other chemical determinands were monitored both at high-frequency (hourly), using automated in situ instrumentation, and by manual weekly sampling and laboratory analysis. The catchment land use is largely agricultural, with a population density of 123 persons km−2. The river water is largely derived from calcareous groundwater, and there are high nitrogen and phosphorus concentrations. Agricultural fertiliser is the dominant source of annual loads of both nitrogen and phosphorus. However, the data show that sewage effluent discharges have a disproportionate effect on the river nitrogen and phosphorus dynamics. At least 38% of the catchment population use septic tank systems, but the effects are hard to quantify as only 6% are officially registered, and the characteristics of the others are unknown. Only 4% of the phosphorus input and 9% of the nitrogen input is exported from the catchment by the river, highlighting the importance of catchment process understanding in predicting nutrient concentrations. High-frequency monitoring will be a key to developing this vital process understanding