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Thiazole Amides, A Novel Class of Algaecides against Freshwater Harmful Algae.

ABSTRACT: Currently, harmful algal blooms are being one of ever-increasing global environmental problems. Much attention has been paid to the use of natural products as the selective algaecides due to their low toxicity, high selectivity and eco-friendly properties. In the present study, the thiazole alkaloid (1), originally isolated from Thermoactino-myces strain TM-64, was shown to exhibit potent algicidal activity against three typically harmful cyanobacterial algae, S. obliqnus, M. aeruginosa, and C. pyrenoidosa. Based on our previous work, a practical, scalable synthesis of alkaloid (1) was developed and reaction could be readily scaled up to more than 100?g. In addition, twenty-six analogues of alkaloid (1) by replacement of tryptamine moiety with different aromatic and aliphatic amines were also prepared. The bioassay results showed that most of these derivatives displayed potent algicidal activity against three harmful algae S. obliqnus, M. aeruginosa, and C. pyrenoidosa with IC50 values in the range of 1.5-5.0??g/mL. Amongst them, compounds (10) and its hydrochloric salt (10S) were found to reveal powerful growth inhibitory activity against harmful cyanobacterial algae with IC50 values as low as 0.08??g/mL, comparable to those of commercial algicide CuSO4 and herbicide Diuron.

SUBMITTER: Wang Y

PROVIDER: S-EPMC5986738 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Thiazole Amides, A Novel Class of Algaecides against Freshwater Harmful Algae.

Wang Ying Y Liu Qisheng Q Wei Zhigang Z Liu Na N Li Yajuan Y Li Duo D Jin Zhong Z Xu Xiaohua X

Scientific reports 20180604 1

Currently, harmful algal blooms are being one of ever-increasing global environmental problems. Much attention has been paid to the use of natural products as the selective algaecides due to their low toxicity, high selectivity and eco-friendly properties. In the present study, the thiazole alkaloid (1), originally isolated from Thermoactino-myces strain TM-64, was shown to exhibit potent algicidal activity against three typically harmful cyanobacterial algae, S. obliqnus, M. aeruginosa, and C. ...[more]

PMID: 29867206

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Project description:BackgroundFreshwater algae can be used as indicators to monitor freshwater ecosystem condition. Algae react quickly and predictably to a broad range of pollutants. Thus they provide early signals of worsening environment. This study was carried out to develop a computer-based image processing technique to automatically detect, recognize, and identify algae genera from the divisions Bacillariophyta, Chlorophyta and Cyanobacteria in Putrajaya Lake. Literature shows that most automated analyses and identification of algae images were limited to only one type of algae. Automated identification system for tropical freshwater algae is even non-existent and this study is partly to fill this gap.ResultsThe development of the automated freshwater algae detection system involved image preprocessing, segmentation, feature extraction and classification by using Artificial neural networks (ANN). Image preprocessing was used to improve contrast and remove noise. Image segmentation using canny edge detection algorithm was then carried out on binary image to detect the algae and its boundaries. Feature extraction process was applied to extract specific feature parameters from algae image to obtain some shape and texture features of selected algae such as shape, area, perimeter, minor and major axes, and finally Fourier spectrum with principal component analysis (PCA) was applied to extract some of algae feature texture. Artificial neural network (ANN) is used to classify algae images based on the extracted features. Feed-forward multilayer perceptron network was initialized with back propagation error algorithm, and trained with extracted database features of algae image samples. System's accuracy rate was obtained by comparing the results between the manual and automated classifying methods. The developed system was able to identify 93 images of selected freshwater algae genera from a total of 100 tested images which yielded accuracy rate of 93%.ConclusionsThis study demonstrated application of automated algae recognition of five genera of freshwater algae. The result indicated that MLP is sufficient, and can be used for classification of freshwater algae. However for future studies, application of support vector machine (SVM) and radial basis function (RBF) should be considered for better classifying as the number of algae species studied increases.

Dataset Information

Thiazole Amides, A Novel Class of Algaecides against Freshwater Harmful Algae.

Publications

Thiazole Amides, A Novel Class of Algaecides against Freshwater Harmful Algae.

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