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Research that seeks to compare two predictive models requires a thorough statistical approach to draw valid inferences about comparisons between the performance of the two models. Researchers present estimates of model performance with little evidence on whether they reflect true differences in model performance. In this study, we apply two statistical tests, that is, the 5 × 2-fold cv paired t-test, and the combined 5 × 2-fold cv F-test to provide statistical evidence on differences in predictive performance between the Fine-Gray (FG) and random survival forest (RSF) models for competing risks. These models are trained on different scenarios of low-dimensional simulated survival data to determine whether the differences in their predictive performance that exist are indeed significant. Each simulation was repeated one hundred times on ten different seeds. The results indicate that the RSF model is superior in predictive performance in the presence of complex relationships (quadratic and interactions) between the outcome and its predictors. The two statistical tests show that the differences in performance are significant in quadratic simulation but not significant in interaction simulations. The study has also revealed that the FG model is superior in predictive performance in linear simulations and its differences in predictive performance compared to the RSF model are significant. The combined 5 × 2-fold cv F-test has lower type I error rates compared to the 5 × 2-fold cv paired t-test.

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Although pollutants pose environmental and human health risks, the majority are not routinely monitored and regulated. Organic pollutants emanate from a variety of sources, and can be classified depending on their chemistry and environmental fate. Classification of pollutants is important because it informs fate processes and apposite removal technologies. The occurrence of emerging contaminants (ECs) in water bodies is a source of environmental and human health concern globally. Despite being widely reported, data on the occurrence of ECs in South Africa are scarce. Specifically, ECS in wastewater in the Northern Cape in South Africa are understudied. In this study, various ECs were screened in water samples collected from three wastewater treatment plants (WWTPs) in the province. The ECs were detected using liquid chromatography coupled to high resolution Orbitrap mass spectrometry following Oasis HLB solid-phase extraction. The main findings were: (1) there is a wide variety of ECs in the WWTPs, (2) physico-chemical properties such as pH, total dissolved solids, conductivity, and dissolved organic content showed reduced values in the outlet compared to the inlet which confirms the presence of less contaminants in the treated wastewater, (3) specific ultraviolet absorbance of less than 2 was observed in the WWTPs samples, suggesting the presence of natural organic matter (NOM) that is predominantly non-humic in nature, (4) most of the ECs were recalcitrant to the treatment processes, (5) pesticides, recreational drugs, and analgesics constitute a significant proportion of pollutants in wastewater, and (6) NOM removal ranged between 35 and 90%. Consequently, a comprehensive database of ECs in wastewater in Sol Plaatje Municipality was created. Since the detected ECs pose ecotoxicological risks, there is a need to monitor and quantify ECs in WWTPs. These data are useful in selecting suitable monitoring and control strategies at WWTPs.

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The SARS-CoV-2 virus which originated in Wuhan, China has since spread throughout the world and is affecting millions of people. When there is a novel virus outbreak, it is crucial to quickly determine if the epidemic is a result of the novel virus or a well-known virus. We propose a deep learning algorithm that uses a convolutional neural network (CNN) as well as a bi-directional long short-term memory (Bi-LSTM) neural network, for the classification of the severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) amongst Coronaviruses. Besides, we classify whether a genome sequence contains candidate regulatory motifs or otherwise. Regulatory motifs bind to transcription factors. Transcription factors are responsible for the expression of genes. The experimental results show that at peak performance, the proposed convolutional neural network bi-directional long short-term memory (CNN-Bi-LSTM) model achieves a classification accuracy of 99.95%, area under curve receiver operating characteristic (AUC ROC) of 100.00%, a specificity of 99.97%, the sensitivity of 99.97%, Cohen's Kappa equal to 0.9978, Mathews Correlation Coefficient (MCC) equal to 0.9978 for the classification of SARS CoV-2 amongst Coronaviruses. Also, the CNN-Bi-LSTM correctly detects whether a sequence has candidate regulatory motifs or binding-sites with a classification accuracy of 99.76%, AUC ROC of 100.00%, a specificity of 99.76%, a sensitivity of 99.76%, MCC equal to 0.9980, and Cohen's Kappa of 0.9970 at peak performance. These results are encouraging enough to recognise deep learning algorithms as alternative avenues for detecting SARS CoV-2 as well as detecting regulatory motifs in the SARS CoV-2 genes.