Aligarh Muslim University

About: Aligarh Muslim University is a education organization based out in Aligarh, Uttar Pradesh, India. It is known for research contribution in the topics: Population & Adsorption. The organization has 8218 authors who have published 16416 publications receiving 289068 citations. The organization is also known as: AMU.

Brassinosteroids protect Lycopersicon esculentum from cadmium toxicity applied as shotgun approach

Abstract: Surface-sterilized seeds of two tomato cultivars (cv. K-25 and Sarvodya) were soaked in 100 μM CdCl2 for 8 h (shotgun approach). The resulting 59-day-old seedlings were sprayed with 10−8 M of 28-homobrassinolide (HBL) or 24-epibrassinolide (EBL) to their foliage. Both cultivars showed significantly different response to Cd stress. Cadmium severely restricted the growth, photosynthetic efficiency, and activity of nitrate reductase (E.C. 1.6.6.1) and carbonic anhydrase (E.C. 4.2.1.1) in Sarvodya as compared to K-25. However, the activities of antioxidative enzymes were significantly higher in K-25. This result may be considered an indication of better tolerance of the K-25 cultivars to Cd stress. Moreover, the spray of both the brassinosteroids (HBL/EBL) were found very effective in neutralizing the adverse effects generated by metals that reflect in better photosynthetic performance by the cultivars. An interesting aspect of this study is that HBL or EBL spray caused a further increase in proline content and antioxidative enzyme activities, which were already enhanced by Cd stress. This effect of brassinosteroids (HBL/EBL) was more pronounced in K-25 than in Sarvodya, representing the tolerance and adoptable behavior of K-25.

Alkaline protease from Spilosoma obliqua: potential applications in bio-formulations.

Abstract: Some properties of the purified alkaline protease from larvae of the insect Spilosoma obliqua (Lepidoptera) and its potential application as an additive in various bio-formulations are reported. The novel feature of the present study is the use of insect protease. The protease was found to be compatible with some of the commercial detergents tested, and was also effective in cleaving various protein substrates tested, albeit to different extents, implying broader substrate specificity and effectiveness of the protease against a wide variety of stains. This property of the protease can also be exploited by using it as an active component in enzymic debriders in view of its ability to digest various protein substrates. The insect protease appears to be potentially useful as an additive in detergent, stain remover and other bio-formulations.

Biomimetic Synthesis of Selenium Nanospheres by Bacterial Strain JS-11 and Its Role as a Biosensor for Nanotoxicity Assessment: A Novel Se-Bioassay

Abstract: Selenium nanoparticles (Se-NPs) were synthesized by green technology using the bacterial isolate Pseudomonas aeruginosa strain JS-11. The bacteria exhibited significant tolerance to selenite (SeO32−) up to 100 mM concentration with an EC50 value of 140 mM. The spent medium (culture supernatant) contains the potential of reducing soluble and colorless SeO32− to insoluble red elemental selenium (Se0) at 37°C. Characterization of red Se° product by use of UV-Vis spectroscopy, X-ray diffraction (XRD), atomic force microscopy (AFM) and transmission electron microscopy (TEM) with energy dispersive X-ray spectrum (EDX) analysis revealed the presence of stable, predominantly monodispersed and spherical selenium nanoparticles (Se-NPs) of an average size of 21 nm. Most likely, the metabolite phenazine-1-carboxylic acid (PCA) released by strain JS-11 in culture supernatant along with the known redox agents like NADH and NADH dependent reductases are responsible for biomimetic reduction of SeO32− to Se° nanospheres. Based on the bioreduction of a colorless solution of SeO32− to elemental red Se0, a high throughput colorimetric bioassay (Se-Assay) was developed for parallel detection and quantification of nanoparticles (NPs) cytotoxicity in a 96 well format. Thus, it has been concluded that the reducing power of the culture supernatant of strain JS-11 could be effectively exploited for developing a simple and environmental friendly method of Se-NPs synthesis. The results elucidated that the red colored Se° nanospheres may serve as a biosensor for nanotoxicity assessment, contemplating the inhibition of SeO32− bioreduction process in NPs treated bacterial cell culture supernatant, as a toxicity end point.

GIS-based landslide susceptibility modeling: A comparison between fuzzy multi-criteria and machine learning algorithms

Abstract: Hazards and disasters have always negative impacts on the way of life. Landslide is an overwhelming natural as well as man-made disaster that causes loss of natural resources and human properties throughout the world. The present study aimed to assess and compare the prediction efficiency of different models in landslide susceptibility in the Kysuca river basin, Slovakia. In this regard, the fuzzy decision-making trial and evaluation laboratory combining with the analytic network process (FDEMATEL-ANP), Naive Bayes (NB) classifier, and random forest (RF) classifier were considered. Initially, a landslide inventory map was produced with 2000 landslide and non-landslide points by randomly divided with a ratio of 70%:30% for training and testing, respectively. The geospatial database for assessing the landslide susceptibility was generated with the help of 16 landslide conditioning factors by allowing for topographical, hydrological, lithological, and land cover factors. The ReliefF method was considered for determining the significance of selected conditioning factors and inclusion in the model building. Consequently, the landslide susceptibility maps (LSMs) were generated using the FDEMATEL-ANP, Naive Bayes (NB) classifier, and random forest (RF) classifier models. Finally, the area under curve (AUC) and different arithmetic evaluation were used for validating and comparing the results and models. The results revealed that random forest (RF) classifier is a promising and optimum model for landslide susceptibility in the study area with a very high value of area under curve (AUC = 0.954), lower value of mean absolute error (MAE = 0.1238) and root mean square error (RMSE = 0.2555), and higher value of Kappa index (K = 0.8435) and overall accuracy (OAC = 92.2%).