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Decolorization of Dyehouse Effluent and Biodegradation of Congo Red by Bacillus thuringiensis RUN1
( Olukanni,O. D ),( A A Osuntoki ),( A O Awotula ),( D C Kalyani ),( G O Gbenle ),( S P Govindwar ) 한국미생물 · 생명공학회 2013 Journal of microbiology and biotechnology Vol.23 No.6
A dye-decolorizing bacterium was isolated from a soil sample and identified as Bacillus thuringiensis using 16S rRNA sequencing. The bacterium was able to decolorize three different textile dyes, namely, Reactive blue 13, Reactive red 58, and Reactive yellow 42, and a real dyehouse effluent up to 80-95% within 6 h. Some non-textile industrially important dyes were also decolorized to different extents. Fourier transform infrared spectroscopy and gas chromatography-mass spectrometer analysis of the ethyl acetate extract of Congo red dye and its metabolites showed that the bacterium could degrade it by the asymmetric cleavage of the azo bonds to yield sodium (4- amino-3-diazenylnaphthalene-1-sulfonate) and phenylbenzene. Sodium (4-amino-3-diazenylnaphthalene-1-sulfonate) was further oxidized by the ortho-cleavage pathway to yield 2- (1-amino-2-diazenyl-2-formylvinyl) benzoic acid. There was induction of the activities of laccase and azoreductase during the decolorization of Congo red, which suggests their probable role in the biodegradation. B. thuringiensis was found to be versatile and could be used for industrial effluent biodegradation.
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Oluwakemi Odukoya,Solomon Nwaneri,Ifedayo Odeniyi,Babatunde Akodu,Esther Oluwole,Gbenga Olorunfemi,Oluwatoyin Popoola,Akinniyi Osuntoki 대한의료정보학회 2022 Healthcare Informatics Research Vol.28 No.1
Objectives: This study developed and compared the performance of three widely used predictive models—logistic regression(LR), artificial neural network (ANN), and decision tree (DT)—to predict diabetes mellitus using the socio-demographic,lifestyle, and physical attributes of a population of Nigerians. Methods: We developed three predictive models using 10 inputvariables. Data preprocessing steps included the removal of missing values and outliers, min-max normalization, and featureextraction using principal component analysis. Data training and validation were accomplished using 10-fold cross-validation. Accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under thereceiver operating characteristic curve (AUROC) were used as performance evaluation metrics. Analysis and model developmentwere performed in R version 3.6.1. Results: The mean age of the participants was 50.52 ± 16.14 years. The classificationaccuracy, sensitivity, specificity, PPV, and NPV for LR were, respectively, 81.31%, 84.32%, 77.24%, 72.75%, and 82.49%. Those for ANN were 98.64%, 98.37%, 99.00%, 98.61%, and 98.83%, and those for DT were 99.05%, 99.76%, 98.08%, 98.77%,and 99.82%, respectively. The best-performing and poorest-performing classifiers were DT and LR, with 99.05% and 81.31%accuracy, respectively. Similarly, the DT algorithm achieved the best AUC value (0.992) compared to ANN (0.976) and LR(0.892). Conclusions: Our study demonstrated that DT, LR, and ANN models can be used effectively for the prediction ofdiabetes mellitus in the Nigerian population based on certain risk factors. An overall comparative analysis of the modelsshowed that the DT model performed better than LR and ANN.
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