Bifunctional role of some biogenic nanoparticles in controlling wilt disease and promoting growth of common bean.

In the present era, nanomaterials are emerging as a powerful tool for management of plant disease and improving crop production to meet the growing global need for food. Thus, this paper was conducted to explore the effectiveness of five different types of nanoparticles (NPs) viz., Co3O4NPs, CuONPs, Fe3O4NPs, NiONPs, and ZnONPs as treatments for Fusarium wilt as well as their role in promoting growth of the common bean plant. The five types of NPs were applied as a treatment for wilt in two ways, therapeutic and protective plans under greenhouse conditions. In vivo experiments showed that all types of NPs significantly increased disease control and diminished the symptoms of Fusarium wilt for both incidence and severity. The recorded values for disease control using the respective NPs during the protective plan were 82.77, 60.17, 49.67, 38.23, and 70.59%. Meanwhile these values were 92.84, 64.67, 51.33, 45.61, 73.84% during the therapeutic plan. Moreover, CuONPs during the protective plan were the best among the five types of NPs employed in terms of wilt disease management. Regarding the use of these NPs as growth promoters, the obtained results confirmed the effectiveness of the five types of NPs in enhancing vegetative growth of the plant under greenhouse conditions, in comparison with control. Among the five NPs, CuONPs improved the plant vegetative growth and particularly increased the content of the photosynthetic pigments; chlorophyll-a (2.96 mg/g), -b (1.93 mg/g), and total carotenoids (1.16 mg/g). These findings suggest the successful and potential exploitation of nanomaterials in agriculture deployed as nano-based products including nano-fungicides and nano-fertilizers. In terms of sustainability, this promising and exceptional multifunctional role of these nanomaterials will surely exert positive impacts on both the environment and sustainable agriculture.

Exploiting the exceptional biosynthetic potency of the endophytic Aspergillus terreus in enhancing production of Co3O4, CuO, Fe3O4, NiO, and ZnO nanoparticles using bioprocess optimization and gamma irradiation.

Developing a suitable applicative process and scaling up the microbial synthesis of nanomaterials is an attractive and emerging prospect for a future sustainable industrial production. In this paper, optimization of fermentation conditions for enhanced production of Co3O4, CuO, Fe3O4, NiO, and ZnO nanoparticles by the endophytic A. terreus ORG-1 was studied. Different cultivation conditions were evaluated. Then, a response surface methodology program was used to optimize physical conditions controlling the biosynthesis of these NPs. Finally, the use of gamma irradiation for improvement of NPs' production was adopted. Under the optimum conditions and after gamma irradiation, the final yields of the respective NPs reached 545.71, 651.67, 463.19, 954.88, 1356.42 mg L-1. To the best of our knowledge, this is the first report on the production and enhancement of different types of nanomaterials from one microbial culture that can open up the way towards the industrialization of the microbial production of nanomaterials.

Novel mycosynthesis of Co3O4, CuO, Fe3O4, NiO, and ZnO nanoparticles by the endophytic Aspergillus terreus and evaluation of their antioxidant and antimicrobial activities.

Several fungal endophytes were isolated and screened for their ability to biosynthesize a variety of nanoparticles (NPs), as a potentially simple and eco-friendly method with low cost. Among these fungi, a promising isolate named ORG-1 was found able to synthesize five different NPs types: Co3O4NPs, CuONPs, Fe3O4NPs, NiONPs, and ZnONPs. The ORG-1 strain was identified as Aspergillus terreus according to the morphological and molecular studies. Synthesis of these NPs was initially monitored by UV-Vis spectroscopy and further characterized by Fourier transform infrared spectroscopy. X-ray diffraction patterns revealed their crystalline structure. Dynamic light scattering analysis was applied to study the particle size distribution and stability. Transmission electron microscope studies indicated the morphology of the synthesized NPs. Additionally, the biological activities of the in vitro antioxidant and antimicrobial potentials were evaluated. Co3O4NPs, CuONPs, Fe3O4NPs, NiONPs, and ZnONPs showed promising antioxidant activity with 50% inhibitory concentrations of 85.44, 96.74, 102.41, 87.41, and 108.67 µg mL-1, respectively. The synthesized NPs exhibited potent antimicrobial activities against several plant and human pathogens. To our knowledge, this is the first report on the use of one microbial strain for the synthesis of a variety of NPs. This study suggests endophytic fungi as new and alternate platforms with an exceptional potentiality for the synthesis of NPs with promising activities. KEY POINTS: • Discovery of a promising endophytic fungus for synthesis of five different types of NPs. • Mycosynthesis and characterization of all the synthesized NPs were investigated. • The synthesized NPs showed promising antioxidant and antimicrobial activities.

Spectral clustering for TRUS images.

BACKGROUND: Identifying the location and the volume of the prostate is important for ultrasound-guided prostate brachytherapy. Prostate volume is also important for prostate cancer diagnosis. Manual outlining of the prostate border is able to determine the prostate volume accurately, however, it is time consuming and tedious. Therefore, a number of investigations have been devoted to designing algorithms that are suitable for segmenting the prostate boundary in ultrasound images. The most popular method is the deformable model (snakes), a method that involves designing an energy function and then optimizing this function. The snakes algorithm usually requires either an initial contour or some points on the prostate boundary to be estimated close enough to the original boundary which is considered a drawback to this powerful method. METHODS: The proposed spectral clustering segmentation algorithm is built on a totally different foundation that doesn't involve any function design or optimization. It also doesn't need any contour or any points on the boundary to be estimated. The proposed algorithm depends mainly on graph theory techniques. RESULTS: Spectral clustering is used in this paper for both prostate gland segmentation from the background and internal gland segmentation. The obtained segmented images were compared to the expert radiologist segmented images. The proposed algorithm obtained excellent gland segmentation results with 93% average overlap areas. It is also able to internally segment the gland where the segmentation showed consistency with the cancerous regions identified by the expert radiologist. CONCLUSION: The proposed spectral clustering segmentation algorithm obtained fast excellent estimates that can give rough prostate volume and location as well as internal gland segmentation without any user interaction.