ORCID Profile
0000-0002-5654-394X
Current Organisation
CSIRO
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Publisher: Informa UK Limited
Date: 11-11-2019
Publisher: American Chemical Society (ACS)
Date: 16-04-2019
Publisher: MDPI AG
Date: 13-02-2021
Abstract: The increasing metal release into the environment warrants investigating their impact on plants, which are cornerstones of ecosystems. Here, Lactuca sativa L. (lettuce) seedlings were exposed hydroponically to different concentrations of silver ions and nanoparticles (Ag NPs) for 25 days to evaluate their impact on plant growth. Seedlings taking Ag+ ions showed an increment of 18% in total phenolic content and 12% in total flavonoid content, whereas under Ag NPs, 7% free radical scavenging activity, 12% total phenolic contents (TPC), and 10% total reducing power are increased. An increase in 31% shoot length, 25% chlorophyll, 11% carbohydrate, and 16% protein content of the lettuce plant is observed in response to Ag NPs, while silver nitrate (AgNO3) has a reduced 40% growth. The lettuce plant was most susceptible to toxic effects of Ag+ ions at a lower concentration, i.e., 0.01 mg/L, while Ag NPs showed less toxicity, only when higher concentrations mg/L were applied. Further, biomolecules other than antioxidant enzymes showed higher phytotoxicity for Ag+ ions, followed by Ag NPs with the concentration of 25, 50, and 100 mg/L compared to the control. Thus, moderate concentrations of Ag NPs have a stimulatory effect on seedling growth, while higher concentrations induced inhibitory effects due to the release of Ag+ ions. These results suggest that optimum metallic contents are desirable for the healthier growth of plants in a controlled way.
Publisher: Informa UK Limited
Date: 10-06-2019
Publisher: MDPI AG
Date: 21-02-2020
DOI: 10.3390/W12020590
Abstract: Numerical modelling increasingly generates massive, high-dimensional spatio-temporal datasets. Exploring such datasets relies on effective visualization. This study presents a generic workflow to (i) project high-dimensional spatio-temporal data on a two-dimensional (2D) plane accurately (ii) compare dimensionality reduction techniques (DRTs) in terms of resolution and computational efficiency (iii) represent 2D projection spatially using a 2D perceptually uniform background color map. Machine learning (ML) based DRTs for data visualization i.e., principal component analysis (PCA), generative topographic mapping (GTM), t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP) are compared in terms of accuracy, resolution and computational efficiency to handle massive datasets. The accuracy of visualization is evaluated using a quality metric based on a co-ranking framework. The workflow is applied to an output of an Australian Water Resource Assessment (AWRA) model for Tasmania, Australia. The dataset consists of daily time series of nine components of the water balance at a 5 km grid cell resolution for the year 2017. The case study shows that PCA allows rapid visualization of global data structures, while t-SNE and UMAP allows more accurate representation of local trends. Furthermore, UMAP is computationally more efficient than t-SNE and least affected by the outliers compared to GTM.
Publisher: Royal Society of Chemistry (RSC)
Date: 2019
DOI: 10.1039/C8NA00343B
Abstract: Silver nanoparticles (NPs) were synthesized using an efficient bioreducing agent from Fagonia cretica extract having the advantage of eco-friendliness over chemical and physical methods.
Publisher: MDPI AG
Date: 14-04-2018
DOI: 10.3390/W10040481
Location: Pakistan
No related grants have been discovered for Abeer Mazher.