ISOLATION AND CHARACTERIZATION OF ENDOPHYTIC FUNGI FROM MEDICINAL PLANT CRESCENTIA CUJETE L. AND THEIR ANTIBACTERIAL, ANTIOXIDANT AND ANTICANCER PROPERTIES

Objective: The present study was aimed to isolate endophytic fungi from ethano-medicinally important plant Crescentia cujete L. in view to screen their bioactive principles towards different pharmacological applications. Methods: A total of four morphologically distinct endophytic fungi were isolated and identified via analyzing their ITS region of 5.8s rRNA and sequences were submitted in Genbank. The recovered four isolates were further cultivated in Czapek-Dox broth, from this extra cellular bioactive metabolites has been extracted using ethyl acetate for their different biological activities. DPPH assay was performed to measure the free-radical scavenging activity of extracts and antibacterial property was assessed through disc diffusion method. On the other hand, the cytotoxic potential of fungal extracts against hepatocellular carcinoma cell lines (HepG2) was studied by MTT assay, AO-EB and Hoechest staining methods under in vitro condition. Most importantly, the active compounds present in the solvent extracts were identified through GC-MS analysis.Results: The fungal extracts showed a strong growth inhibitory effect against bacterial human pathogens and excellent free radical scavenging activity. It also exhibits excellent antiproliferative effect against hepatocellular carcinoma cells, further it was observed that the cell death was primarily mediated by apoptosis. The active compounds present in the extracts were identified through GC-MS analysis, which depicts the presence of aspirin and diethyl phthalate as the major constituents.Conclusion: Overall, this study strongly suggests that extracts of isolated endophytic fungi from C. cujete L. can be developed as a lead/drug molecule in view of pharmaceutical context.Â

ASPERGILLUS FLAVUS MEDIATED SILVER NANOPARTICLES SYNTHESIS AND EVALUATION OF ITS ANTIMICROBIAL ACTIVITY AGAINST DIFFERENT HUMAN PATHOGENS

Objective: Here, we report the extracellular synthesis of silver nanoparticles (AgNPs) using the cell-free extract of fungal isolate Aspergillus flavus and evaluation its inhibitory activity against bacterial pathogens. Methods: Synthesized AgNPs was characterized via high throughput instrumentation such as UV–Visible spectrophotometer (UV-Vis) and Fourier transform infrared spectroscopy, High-resolution transmission electron microscopy (HRTEM), X-ray Diffraction (XRD) and Energy dispersive X-ray spectroscopy (EDAX). Results: Formation of yellowish brown colour clearly indicates the synthesis of AgNPs which produces a SPR peak at 420 nm. Active protein metabolites present in the cell-free extract plays a crucial role in reduction and stabilization of AgNPs. It was clearly observed that synthesized AgNPs were faced-centered cubic crystalline in nature with the mean size of 22±11 nm. Further, synthesized AgNPs capped with protein moieties exhibits excellent inhibitory activity against tested bacterial pathogens. Conclusion: In this study, we have isolated the fungal strain A. flavus from the infected larvae of D. eucharis from the soil. The active metabolites of isolated A. flavus have been successfully used as an eco-friendly reducing agent to generate AgNPs and synthesized particles can be potentially developed as a drug candidature for antimicrobial therapy

Zinc deficiency in Indian soils with special focus to enrich zinc in peanut

In India, zinc (Zn) is now considered as fourth most important yield limiting nutrient in agricultural crops. Zn deficiency in Indian soils is likely to increase from 49 to 63% by 2025. India is leading in groundnut acreage but behind the China in production due to less productivity. Apart from raindependant cultivation and mineral nutrition play a vital role in groundnut productivity. Among the nutrients, Zn deficiency cause yield loss to the maximum of 40% in groundnut. The average response of groundnut to zinc fertilization ranged from 210 to 470 kg ha-1. Hence, it is ideal to follow suitable crop improvement and agronomic management strategies to enhance the uptake and availability of Zn in peanut. There are reports emerging that genetic variability exists among the peanut genotypes for zinc response and accumulation in kernel. This implies that high zinc dense confectionary peanut genotypes can be exploited for the further breeding programmes. In addition, Zn fertilization strategies viz., soil application of enriched Zn, seed coating and foliar application can be suitably adapted with available sources of Zn fertilizer to enhance Zn availability and uptake by peanut under changing climate. This article attempts to examine the status of Zn deficiency in semiarid tropics and approaches to enhance Zn content in peanut kernel through crop improvement and agronomic manipulation

Maximizing the Productivity and Profitability of Summer Irrigated Greengram (Vigna radiata L.) by Combining Basal Nitrogen Dose and Foliar Nutrition of Nano and Normal Urea

Field experiment was conducted at wetland farm of Tamil Nadu Agricultural University, Coimbatore, during summer season, 2022 with an objective of maximizing the productivity and profitability of greengram by adopting varied dose of basal nitrogen and foliar application of nano and normal urea at Flower Initiation (FI) stage and 15 days thereafter. The experiment was laid out in factorial randomized block design and replicated thrice with the following treatments viz., N1 - 100% RDN (25kg N ha-1), N2 - 80% RDN (20kg N ha-1), N3 - 60% RDN (15kg N ha-1) and N4 - Control as factor I, and F1 - Nano urea @ 2ml litre-1 of water, F2 - Nano urea @ 3ml litre-1 of water, F3 - Nano urea @ 4ml litre-1 of water and F4 - 1% urea as factor II. The experiment results revealed that, 100% RDN and nano urea foliar spray @ 4ml litre-1 of water significantly registered higher fertility co-efficient (71.2%), pods plant-1 (38.5 Nos.), seeds pod-1 (12.7 Nos.) and maximum grain yield (1291 kg ha-1). Nevertheless, it was on par with the application of 80% RDN and nano urea foliar spray @ 4ml    litre-1 of water, which recorded fertility co-efficient of 70.6%, 38.0 pods plant-1, 12.6 seeds pod-1 and grain yield of 1289 kg ha-1. In economics perspective also, application of 100% RDN and foliar supplement of nano urea @ 4ml litre-1 accounted maximum gross return (₹100114 ha-1), net return (₹53549 ha-1) and benefit-cost ratio (2.15), which was comparable with application of 80% RDN and foliar application of nano urea @ 4ml litre-1 of water at FI stage and 15 days thereafter. Based on the experimental results, it is concluded that reduced application of basal nitrogen i.e., 80% RDN with nano urea foliar spray @ 4ml litre-1 of water at FI stage and 15 days thereafter found to be the optimal nitrogen dose and nano urea foliar nutrition for maximizing the productivity and profitability of summer irrigated greengram

Strategies for dimensionality reduction in hyperspectral remote sensing: A comprehensive overview

The technological advancements in spectroscopy give rise to acquiring data about different materials on earth's surface which can be utilized in a variety of potential applications. But, the hundreds of spectral bands are generally equipped with highly correlated information with limited training samples. This will degrade the Hyperspectral Image (HSI) classification accuracy. So Dimensionality Reduction (DR) has become inevitable and necessary step need to incorporate before HSI classification. The main contribution of this work lies in comparative study and review on dimensionality reduction techniques for Hyperspectral remote sensing image classification. The related challenges and research directions are also discussed. This study will help the researchers in the Hyperspectral remote sensing community to choose the appropriate DR technique for classification which can be useful in various real time applications

Crop Classification for Agricultural Applications in Hyperspectral Remote Sensing Images

Hyperspectral imaging (HSI), measuring the reflectance over visible (VIS), near-infrared (NIR), and shortwave infrared wavelengths (SWIR), has empowered the task of classification and can be useful in a variety of application areas like agriculture, even at a minor level. Band selection (BS) refers to the process of selecting the most relevant bands from a hyperspectral image, which is a necessary and important step for classification in HSI. Though numerous successful methods are available for selecting informative bands, reflectance properties are not taken into account, which is crucial for application-specific BS. The present paper aims at crop mapping for agriculture, where physical properties of light and biological conditions of plants are considered for BS. Initially, bands were partitioned according to their wavelength boundaries in visible, near-infrared, and shortwave infrared regions. Then, bands were quantized and selected via metrics like entropy, Normalized Difference Vegetation Index (NDVI), and Modified Normalized Difference Water Index (MNDWI) from each region, respectively. A Convolutional Neural Network was designed with the finer generated sub-cube to map the selective crops. Experiments were conducted on two standard HSI datasets, Indian Pines and Salinas, to classify different types of crops from Corn, Soya, Fallow, and Romaine Lettuce classes. Quantitatively, overall accuracy between 95.97% and 99.35% was achieved for Corn and Soya classes from Indian Pines; between 94.53% and 100% was achieved for Fallow and Romaine Lettuce classes from Salinas. The effectiveness of the proposed band selection with Convolutional Neural Network (CNN) can be seen from the resulted classification maps and ablation study