Automating incidence and prevalence analysis in open cohorts.

MOTIVATION: Data is increasingly used for improvement and research in public health, especially administrative data such as that collected in electronic health records. Patients enter and exit these typically open-cohort datasets non-uniformly; this can render simple questions about incidence and prevalence time-consuming and with unnecessary variation between analyses. We therefore developed methods to automate analysis of incidence and prevalence in open cohort datasets, to improve transparency, productivity and reproducibility of analyses. IMPLEMENTATION: We provide both a code-free set of rules for incidence and prevalence that can be applied to any open cohort, and a python Command Line Interface implementation of these rules requiring python 3.9 or later. GENERAL FEATURES: The Command Line Interface is used to calculate incidence and point prevalence time series from open cohort data. The ruleset can be used in developing other implementations or can be rearranged to form other analytical questions such as period prevalence. AVAILABILITY: The command line interface is freely available from https://github.com/THINKINGGroup/analogy_publication .

A novel generative adversarial networks modelling for the class imbalance problem in high dimensional omics data.

Class imbalance remains a large problem in high-throughput omics analyses, causing bias towards the over-represented class when training machine learning-based classifiers. Oversampling is a common method used to balance classes, allowing for better generalization of the training data. More naive approaches can introduce other biases into the data, being especially sensitive to inaccuracies in the training data, a problem considering the characteristically noisy data obtained in healthcare. This is especially a problem with high-dimensional data. A generative adversarial network-based method is proposed for creating synthetic samples from small, high-dimensional data, to improve upon other more naive generative approaches. The method was compared with 'synthetic minority over-sampling technique' (SMOTE) and 'random oversampling' (RO). Generative methods were validated by training classifiers on the balanced data.

Prediction of future microplastic accumulation in agricultural soils.

This study shows the general exponential rise in microplastic accumulation in agricultural soils, with fertilizer application speeding up this increase, and future predictions of microplastic concentrations. Utilizing data from the Broadbalk winter wheat experiment at Rothamsted Research, UK, from 1846 to 2022, Poisson regression models were applied to microplastic counts under different soil treatments, including farmyard manure, inorganic fertilizers, and control conditions. A mass conversion factor was applied to obtain the w/w relationship. Results indicated a significant annual increase in microplastic concentrations across all treatments, with fertilized soils showing a notably higher accumulation rate. Our study forecasts that, in 50 and 100 years from now, soils treated with fertilizers are expected to reach microplastic concentrations of 168.9 mg kg-1 (95% CI: 60.32-473.09) and 1159 mg kg-1 (95% CI: 200.49-6699.8) respectively, levels converging on those used in many experiments. This highlights the urgent need for strategies to mitigate microplastic pollution in agricultural fields. The results also help to choose predicted concentrations in global change experiments, as well as to motivate further research to explore the mechanisms of microplastic accumulation and the integration of these insights into broader agricultural and ecological models to guide sustainable practices and environmental conservation.

Sustainable production of healthy, affordable food in the UK: The pros and cons of plasticulture.

An evolving green agenda as the UK seeks to achieve 'net zero' in greenhouse gas emissions by 2050, coupled with our new trading relationship with the European Union, is resulting in new government policies, which will be disruptive to Britain's traditional food and farming practices. These policies encourage sustainable farming and land-sparing to restore natural habitats and will provide an opportunity to address issues such as high emissions of GHGs and dwindling biodiversity resulting from many intensive agricultural practices. To address these and other food challenges such as global conflicts and health issues, Britain will need a revolution in its food system. The aim of this paper is to make the case for such a food revolution where additional healthy food for the UK population is produced in-country in specialised production units for fruits and vegetables developed on sites previously considered unsuitable for crop production. High crop productivity can be achieved in low-cost controlled environments, making extensive use of novel crop science and modern controlled-environment technology. Such systems must be operated with very limited environmental impact. In recent years, growth in the application of plasticulture in UK horticulture has driven some increases in crop yield, quality and value. However, the environmental cost of plastic production and plastic pollution is regarded as a generational challenge that faces the earth system complex. The distribution of plastic waste is ubiquitous, with a significant pollution load arising from a range of agricultural practices. The primary receptor of agriplastic pollution is agricultural soil. Impacts of microplastics on crop productivity and quality and also on human health are only now being investigated. This paper explores the possibility that we can mitigate the adverse environmental effects of agriplastics and thereby exploit the potential of plasticulture to enhance the productivity and positive health impact of UK horticulture.