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Abid, R.

Publications and source records attributed to Abid, R..

2 recordsLinked to original sources

Development of Nature-Based Insect-Repellent Surface Coating Using ZnO Nanoparticles and Plant Extracts for Mosquito Control

Mosquitos have been a malice and source of many diseases in humans. Later on, humans understood they learned that plants also possess mosquito-repellent properties. Different insectrepellent coatings are present in the market which are chemically prepared and can be harmful to humans and the environment. Different plants have insect-repellent properties which have been utilized in this research to make a nature-based insect-repellent surface coating. Moringa oleifera L. and Mentha piperita L. are naturally insect-repellent plants. Nanoparticles increase the surface area and efficiency of extracts of plants. Thus, ZnONP of Moringa oliefera L. and Mentha piperita L. plants were made characterization was done through UV-vis spectroscopy, FTIR, and PSA. The UV-visible spectrum showed absorption peaks for ZnO nanoparticles at 350nm for Mentha piperita L. and 356nm for Moringa oleifera L. The particle size analysis indicated the variable sizes of ZnONPs for both plants. FTIR showed vibration peaks from 3341 to 650cm-1 for Moringa oleifera L. and 3393 to 700 cm-1 for Mentha piperita L. ZnONPs were used in paint along with water extracts of plants to make the paint insect-repellent in nature. Mosquito repellent activity of paint formulations was also tested against Aedes aegypti.

animal behavior and cognition↗

Accuracy Responses in Species Identification varying DNA Barcode lengths with a Naive Bayes Classifier: Efficacy of Mini-Barcode under A Supervised Machine Learning approach

Specific gene regions in DNA, such as cytochrome c oxidase I (COI) in animals, are defined as DNA barcodes and can be used as identifiers to distinguish species. The standard length of a DNA barcode is approximately 650 base pairs (bp). However, because of the challenges associated with sequencing technologies and the unavailability of high-quality genomic DNA, it is not always possible to obtain the full-length barcode sequence of an organism. Recent studies suggest that mini-barcodes, which are shorter (100-300 bp) barcode sequences, can contribute significantly to species identification. Among various methods proposed for the identification task, supervised machine learning methods are effective. However, any prior work indicating the efficacy of mini-barcodes in species identification under a machine learning approach is elusive to find. In this study, we analyzed the effect of different barcode lengths on species identification using supervised machine learning and proposed a general approximation of the required length of the minibarcode. Since Naive Bayes is seen to generally outperform other supervised methods in species identification in other studies, we implemented this classifier and showed the effectiveness of the mini-barcode by demonstrating the accuracy responses obtained after varying the length of the DNA barcode sequences.

bioinformatics↗