Pesticides and metabolites in groundwater: examples from two major UK aquifers

Reducing the impact of anthropogenic pollution on groundwater bodies and ameliorating any deterioration of water quality is central to key legislative drivers such as the EU Water Framework Directive and the proposed daughter Directive relating to the protection of groundwater. Pesticide pollution has a direct impact on groundwater quality and an indirect impact on the associated aquatic ecosystems supported by groundwater. There is currently no legislative requirement to monitor pesticide metabolite concentrations in groundwater. Pesticide and metabolite results from two nationally important aquifers are presented, the Trassic Sandstone and the Chalk of Southern England. Aerobic microbial degradation of diuron in the soil can lead to the formation of three compounds; dichlorophenylmethyl urea (DCPMU), dichlorophenyl urea (DCPU) and dichloroanaline (DCA).Median diuron concentrations were significantly higher than each of the metabolites with outliers exceeding the PVC on at least one occasion. At nine sites in Kent, Southern England, (60%) metabolites were more prevalent than diuron. Both aquifers are an important source of water, locally supplying up to 80% of public drinking water. The sandstone site has a predominantly arable landuse with a potential diffuse source of pesticides although soakaways are possible point sources.The chalk site has a mixture of arable and industrial/urban landuse. A significant source has been from excessive applications of diuron (“over-spray”) on a number of public amenities. Data from both aquifers show that pesticide concentrations have a high degree of temporal variability. Elevated pesticide concentrations are associated with recharge events in both aquifer systems regardless of pesticide source terms. Pesticides from amenity use and diffuse agricultural sources both pose a threat to groundwater quality. Pesticide metabolites are present in significant concentrations in groundwaters. Systematic, long-term monitoring (5-10 years) is required to understand trends in groundwater quality

A method of justifying confidence in the safety of digital health interventions

Digital health interventions (DHIs) enable improvements in health strategy and address health system challenges. The World Health Organization provides a formal classification for DHIs. However, safety claims, about such interventions, vary in quality and are often vague as to how they are communicated between technical, clinical experts and stakeholders. By combining the classifications with a method of safety analysis and justification, we postulate confidence in the safety of digital technology. Confidence is resulting from the application of the framework to the DHI, using defined health system challenges. The framework and derived safety justifications can be applied to any DHI. It can serve as guideline for health strategy, regulatory and standards based compliance

Supervised machine learning for audio emotion recognition

The field of Music Emotion Recognition has become and established research sub-domain of Music Information Retrieval. Less attention has been directed towards the counterpart domain of Audio Emotion Recognition, which focuses upon detection of emotional stimuli resulting from non-musical sound. By better understanding how sounds provoke emotional responses in an audience, it may be possible to enhance the work of sound designers. The work in this paper uses the International Affective Digital Sounds set. A total of 76 features are extracted from the sounds, spanning the time and frequency domains. The features are then subjected to an initial analysis to determine what level of similarity exists between pairs of features measured using Pearson’s r correlation coefficient before being used as inputs to a multiple regression model to determine their weighting and relative importance. The features are then used as the input to two machine learning approaches: regression modelling and artificial neural networks in order to determine their ability to predict the emotional dimensions of arousal and valence. It was found that a small number of strong correlations exist between the features and that a greater number of features contribute significantly to the predictive power of emotional valence, rather than arousal. Shallow neural networks perform significantly better than a range of regression models and the best performing networks were able to account for 64.4% of the variance in prediction of arousal and 65.4% in the case of valence. These findings are a major improvement over those encountered in the literature. Several extensions of this research are discussed, including work related to improving data sets as well as the modelling processes

An analysis of pre-clinical efficacy testing of antivenoms for sub-Saharan Africa: Inadequate independent scrutiny and poor-quality reporting are barriers to improving snakebite treatment and management

Background The World Health Organization’s strategy to halve snakebite mortality and morbidity by 2030 includes an emphasis on a risk-benefit process assessing the preclinical efficacy of antivenoms manufactured for sub-Saharan Africa. To assist this process, we systematically collected, standardised and analysed all publicly available data on the preclinical efficacy of antivenoms designed for sub-Saharan Africa. Methodology/Principal findings Using a systematic search of publication databases, we focused on publicly available preclinical reports of the efficacy of 16 antivenom products available in sub Saharan Africa. Publications since 1999 reporting the industry standard intravenous pre-incubation method of murine in vivo neutralisation of venom lethality (median effective dose [ED50]) were included. Eighteen publications met the criteria. To permit comparison of the several different reported ED50 values, it was necessary to standardise these to microlitre of antivenom resulting in 50% survival of mice challenged per milligram of venom (μl/mg). We were unable to identify publicly available preclinical data on four antivenoms, whilst data for six polyspecific antivenoms were restricted to a small number of venoms. Only four antivenoms were tested against a wide range of venoms. Examination of these studies for the reporting of key metrics required for interpreting antivenom ED50s were highly variable, as evidenced by eight different units being used for the described ED50 values. Conclusions/Significance There is a disturbing lack of (i) preclinical efficacy testing of antivenom for sub Saharan Africa, (ii) publicly available reports and (iii) independent scrutiny of this medically important data. Where reports do exist, the methods and metrics used are highly variable. This prevents comprehensive meta-analysis of antivenom preclinical efficacy, and severely reduces the utility of antivenom ED50 results in the decision making of physicians treating patients and of national and international health agencies. Here, we propose the use of a standardised result reporting checklist to resolve this issue. Implementation of these straightforward steps will deliver uniform evaluation of products across laboratories, facilitate meta-analyses, and contribute vital information for designing the clinical trials needed to achieve the WHO target of halving snakebite morbidity and mortality by 2030

Supervised machine learning for audio emotion recognition: Enhancing film sound design using audio features, regression models and artificial neural networks

This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s00779-020-01389-0The field of Music Emotion Recognition has become and established research sub-domain of Music Information Retrieval. Less attention has been directed towards the counterpart domain of Audio Emotion Recognition, which focuses upon detection of emotional stimuli resulting from non-musical sound. By better understanding how sounds provoke emotional responses in an audience, it may be possible to enhance the work of sound designers. The work in this paper uses the International Affective Digital Sounds set. A total of 76 features are extracted from the sounds, spanning the time and frequency domains. The features are then subjected to an initial analysis to determine what level of similarity exists between pairs of features measured using Pearson’s r correlation coefficient before being used as inputs to a multiple regression model to determine their weighting and relative importance. The features are then used as the input to two machine learning approaches: regression modelling and artificial neural networks in order to determine their ability to predict the emotional dimensions of arousal and valence. It was found that a small number of strong correlations exist between the features and that a greater number of features contribute significantly to the predictive power of emotional valence, rather than arousal. Shallow neural networks perform significantly better than a range of regression models and the best performing networks were able to account for 64.4% of the variance in prediction of arousal and 65.4% in the case of valence. These findings are a major improvement over those encountered in the literature. Several extensions of this research are discussed, including work related to improving data sets as well as the modelling processes

Safety justification of healthcare applications using synthetic datasets

Background: Increasing numbers of intelligent healthcare applications are developed by analysing big data, on which they are trained. It is necessary to assure that such applications will be safe for patients; this entails validation against datasets. But datasets cannot be shared easily, due to privacy, and consent issues, resulting in delaying innovation. Realistic Synthetic Datasets (RSDs), equivalent to the real datasets, are seen as a solution to this. Objective: To develop the outline for safety justification of an application, validated with an RSD, and identify the safety evidence the RSD developers will need to generate. Method: Assurance case argument development approaches were used, including high level data related risk identification. Result: An outline of the justification of such applications, focusing on the contribution of the RSD. Conclusions: Use of RSD will require specific arguments and evidence, which will affect the adopted methods. Mutually supporting arguments can result in a compelling justification

Looking the part : entrepreneurial growth through strategic legitimisation

Entrepreneurial legitimacy and strategies employed to generate legitimacy are topical and pertinent to entrepreneurship. This study sought to understand the impact of formative stage strategic legitimisation employment on entrepreneurial success. Through a review of the literature, the study built a strategic legitimacy scale offering that allowed for historical empirical testing. Using non probability sampling, entrepreneurs were surveyed through a face-to-face questionnaire in an attempt to attain closed response data regarding their historical strategic legitimisation activities and growth indicators. The study provided empirical evidence for a positive relationship between formative stage strategic legitimisation employment and venture development. Furthermore the presence of exponential value returns against strategic legitimacy activities was exposed at low and moderate levels of legitimacy engagement. While volatile value returns were shown at high levels strategic legitimacy employment. Furthermore the effectiveness of formative stage strategic legitimacy activities was shown to not be effected by the maturity of the industry in which a venture started. CopyrightDissertation (MBA)--University of Pretoria, 2010.Gordon Institute of Business Science (GIBS)unrestricte