Impact of fermented organic formulations combined with inorganic fertilizers on broccoli (Brassica oleracea L. var. italica Plenck) cv. Palam Samridhi.

A two-year field experiment (2018-19 and 2019-20) was laid out in a randomized complete block design (RCBD) with a spacing of 60 × 45 cm involving three replications with ten treatments having cow manurial amendments along with integrated nutrient management in a plot size of 3.0 m × 1.8 m. The effect of the integration of cow manurial amendments and mineral fertilizers on soil fertility, nutrient uptake, yield, and economics of broccoli was studied. The experiment was laid out during the rabi season in the mid-hills of Himachal Pradesh. T8 [90% RDN (112.5 N: 67.5 P: 46.8 K kg/ha) + 5% jeevamrit (1.5 l/m2) + 5% jeevamrit foliar spray] obtained the greatest organic carbon (20.93 g kg-1), available N (375.13 kg ha-1), P (48.46 kg ha-1), K (260.53 kg ha-1) in the soil as well as more uptake of N (60.58 kg ha-1), P (7.25 kg ha-1) and K (37.88 g ha-1) by the plants. Further, this treatment obtained the greatest value for yield (186.77 q ha-1 and 12.44 kg plot-1), net income (₹ 245840) and cost-benefit ratio (1.93). Outcomes of this investigation suggested that combined usage of cow manure, jeevamrit, beejamrit, and ghanjeevamrit with inorganic fertilizers proved to be useful for enhancing soil health, increasing nutrient uptake, and ensuring sustainable production of broccoli.

Automatic diagnosis of COVID-19 with MCA-inspired TQWT-based classification of chest X-ray images.

In this era of Coronavirus disease 2019 (COVID-19), an accurate method of diagnosis with less diagnosis time and cost can effectively help in controlling the disease spread with the new variants taking birth from time to time. In order to achieve this, a two-dimensional (2D) tunable Q-wavelet transform (TQWT) based on a memristive crossbar array (MCA) is introduced in this work for the decomposition of chest X-ray images of two different datasets. TQWT has resulted in promising values of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) at the optimum values of its parameters namely quality factor (Q) of 4, and oversampling rate (r) of 3 and at a decomposition level (J) of 2. The MCA-based model is used to process decomposed images for further classification with efficient storage. These images have been further used for the classification of COVID-19 and non-COVID-19 images using ResNet50 and AlexNet convolutional neural network (CNN) models. The average accuracy values achieved for the processed chest X-ray images classification in the small and large datasets are 98.82% and 94.64%, respectively which are higher than the reported conventional methods based on different models of deep learning techniques. The average accuracy of detection of COVID-19 via the proposed method of image classification has also been achieved with less complexity, energy, power, and area consumption along with lower cost estimation as compared to CMOS-based technology.

Evaluation of antibacterial activity from phytosynthesized silver nanoparticles against medical devices infected with Staphylococcus spp.

OBJECTIVES: Biofilm formation on the surface of medical devices, such as artificial prosthetics and catheters, are serious challenges to biomedical science. Most conventional methods, such as antibiotic therapy and medical device replacement, have failed because of low efficiency in medical environments. In the present study, we aimed to prevent infection by human pathogens Staphylococcus epidermidis (35984) and Staphylococcus aureus (740), which are resistant to antibiotic therapy. To prevent these infections, phytosynthesized silver nanoparticles (AgNPs) coating was tested. METHODS: The AgNPs were synthesized using aqueous extract of Berberis asiatica leaves and were characterized by UV-vis spectroscopy, X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), atomic force microscopy (AFM), energy dispersive spectroscopy (EDS), transmission electron microscopy (TEM), and selected area electron diffraction (SAED). The viable cells of bacteria were counted using a digital colony counter. RESULTS: AgNPs were 15 nm-35 nm in size and crystallized in a face-centred-cubic structure. Furthermore, the AgNPs coating on glass surfaces were bactericidal. CONCLUSIONS: This study suggested that phytosynthesized AgNPs capped with various biomolecules present in leaf extracts of B. asiatica coated on glass surface prevent S. epidermidis and S. aureus associated infections of medical devices. Thus, coating of phytosynthesized AgNPs on glass surfaces may provide efficient antibacterial treatment of infected medical devices.

Biosynthesis, characterization and antibacterial activity of silver nanoparticles using an endophytic fungal supernatant of Raphanus sativus.

In this study, biological synthesis of silver nanoparticles (AgNPs) from supernatant of endophytic fungus Alternaria sp. isolated from the healthy leaves of Raphanus sativus is studied. The synthesized AgNPs are characterized using UV-vis spectroscopy and Fourier transform-infrared spectroscopy (FTIR). The structural analysis is done by powder X-ray diffraction (XRD) method. The stability of AgNPs is studied by dynamic light scattering (DLS) method. The size and shape of AgNPs are observed by transmission electron microscopy (TEM) and atomic force microscopy (AFM) and found to be spherical with an average particles size of 4-30 nm. Further, these AgNPs have been found to be highly toxic against human pathogenic bacteria, suggesting the possibility of using AgNPs as efficient antibacterial agents.

Green synthesis of nanostructured silver particles and their catalytic application in dye degradation.

Today, discharge of hazardous dyes from textile industries in water bodies like lakes, rivers and groundwater has become a serious problem, which contributes to increase their pollution levels significantly. These pollutants are difficult to remove by traditional water treatment procedures. Thus, there is a need to develop more suitable methods of effluent treatment. Here, we describe use of green-synthesized nanostructured silver particles in degradation of hazardous dyes like Safranine O, Methyl red, Methyl orange and Methylene blue etc. The silver nanoparticles (AgNPs) used as nanocatalysts were synthesized using Zanthoxylum armatum leaves. The reduction of silver ions and the formation of AgNPs have been assessed by UV-Vis spectroscopy. DLS, SEM-EDX, TEM, SAED and XRD studies revealed that the AgNPs were crystalline in nature with size range from 15 to 50 nm. The report emphasizes that the AgNPs are observed to be an excellent catalyst on reduction of hazardous dyes, which is confirmed by a decrease in absorbance maximum values.