Agrowaste-generated biochar for the sustainable remediation of refractory pollutants

The rapid growth of various industries has led to a significant, alarming increase in recalcitrant pollutants in the environment. Hazardous dyes, heavy metals, pesticides, pharmaceutical products, and other associated polycyclic aromatic hydrocarbons (such as acenaphthene, fluorene, fluoranthene, phenanthrene, and pyrene) have posed a significant threat to the surroundings due to their refractory nature. Although activated carbon has been reported to be an adsorbent for removing contaminants from wastewater, it has its limitations. Hence, this review provides an elaborate account of converting agricultural waste into biochar with nanotextured surfaces that can serve as low-cost adsorbents with promising pollutant-removing properties. A detailed mechanism rationalized that this strategy involves the conversion of agrowaste to promising adsorbents that can be reduced, reused, and recycled. The potential of biowaste-derived biochar can be exploited for developing biofuel for renewable energy and also for improving soil fertility. This strategy can provide a solution to control greenhouse gas emissions by preventing the open burning of agricultural residues in fields. Furthermore, this serves a dual purpose for environmental remediation as well as effective management of agricultural waste rich in both organic and inorganic components that are generated during various agricultural operations. In this manner, this review provides recent advances in the use of agrowaste-generated biochar for cleaning the environment

Adiantum philippense

Development of an ecofriendly, reliable, and rapid process for synthesis of nanoparticles using biological system is an important bulge in nanotechnology. Antioxidant potential and medicinal value of Adiantum philippense L. fascinated us to utilize it for biosynthesis of gold and silver nanoparticles (AuNPs and AgNPs). The current paper reports utility of aqueous extract of A. philippense L. fronds for the green synthesis of AuNPs and AgNPs. Effect of various parameters on synthesis of nanoparticles was monitored by UV-Vis spectrometry. Optimum conditions for AuNPs synthesis were 1 : 1 proportion of original extract at pH 11 and 5 mM tetrachloroauric acid, whereas optimum conditions for AgNPs synthesis were 1 : 1 proportion of original extract at pH 12 and 9 mM silver nitrate. Characterization of nanoparticles was done by TEM, SAED, XRD, EDS, FTIR, and DLS analyses. The results revealed that AuNPs and AgNPs were anisotropic. Monocrystalline AuNPs and polycrystalline AgNPs measured 10 to 18 nm in size. EDS and XRD analyses confirmed the presence of elemental gold and silver. FTIR analysis revealed a possible binding of extract to AuNPs through –NH2 group and to AgNPs through C=C group. These nanoparticles stabilized by a biological capping agent could further be utilized for biomedical applications

Synthesis of Gold Nanoanisotrops Using Dioscorea bulbifera

Biosynthesis of metal nanoparticles employing plant extracts and thereby development of an environmentally benign process is an important branch of nanotechnology. Here, the synthesis of gold nanoparticles using Dioscorea bulbifera tuber extract (DBTE) as the reducing agent is reported. Field emission scanning electron microscopy (FESEM), energy-dispersive spectroscopy (EDX), X-ray diffraction (XRD), and UV-visible absorption spectroscopy confirmed the reduction of gold ions to AuNPs. The anisotropic nanoparticles consist of a mixture of gold nanotriangles, nanoprisms, nanotrapezoid, and spheres. The kinetics of particle formation was time dependent and was enhanced by the increase of temperature from 6°C to 50°C, the optimum being 50°C. The optimum concentration of chloroauric acid was found to be 1 mM. Complete reduction of the metal ions within 5 hours by DBTE highlights the development of a novel ecofriendly route of biological synthesis of gold nanoparticles. This is the first paper on synthesis of gold nanoparticles using DBTE

Antidiabetic Activity of Gnidia glauca

Diabetes is a metabolic disorder affecting about 220 million people worldwide. One of the most critical complications of diabetes is post-prandial hyper-glycemia (PPHG). Glucosidase inhibitor and α-amylase inhibitors are class of compounds that help in managing PPHG. Low-cost herbal treatment is recommended due to their lesser side effect for treatment of diabetes. Two plants with significant traditional therapeutic potential, namely, Gnidia glauca and Dioscorea bulbifera, were tested for their efficiency to inhibit α-amylase and α-glucosidase. Stem, leaf, and flower of G. glauca and bulb of D. bulbifera were sequentially extracted with petroleum ether, ethyl acetate, and methanol as well as separately with 70% ethanol. Petroleum ether extract of flower of G. glauca was found to inhibit α-amylase significantly (78.56%). Extracts were further tested against crude murine pancreatic, small intestinal, and liver glucosidase enzyme which revealed excellent inhibitory properties. α-glucosidase inhibition provided a strong in vitro evidence for confirmation of both G. glauca and D. bulbifera as excellent antidiabetic remedy. This is the first report of its kind that provides a strong biochemical basis for management of type II diabetes using G. glauca and D. bulbifera. These results provide intense rationale for further in vivo and clinical study

Recognition of cancer mediating genes using MLP-SDAE model

This article introduces a predictive deep learning model called MLP-SDAE, which combines Multilayer Perceptron (MLP) and Stacked Denoising Auto-encoder (SDAE) techniques. Our model, MLP-SDAE is trained using Stacked Denoising Auto-Encoder for feature selection, and backpropagation is employed within the MLP structure. We have incorporated dropout to enhance the model’s performance and prevent overfitting. The primary objective of the MLP-SDAE model is to identify associations among genes that have undergone significant alterations from a normal to a diseased state based on their expression behaviors. This concept allows us to predict disease-mediating genes and their altered associations. The methodology involves calculating gene-based correlation coefficients and selecting a subset of genes based on this analysis. We have demonstrated the effectiveness of our methods using four gene expression datasets related to human leukemia, lung, colon, and breast cancer. As a result, we have identified several potentially important genes, such as CACLA, HBA, IGFBP3, EFGR, TFN, TP53, LI6, and TMTC1, which may play a crucial role in developing these cancers. Furthermore, we conducted a comprehensive comparative study with other deep learning techniques, including Recurrent Neural Network (RNN), Deep Belief Network (DBN), Deep Boltzmann Machine (DBM), Auto-encoder (AE), and Denoising Auto-encoder (DAE). Our results have been validated through biochemical pathway analysis, t-tests, F-score, Gene Ontology (GO) identification, and the NCBI database. These validations demonstrate that our proposed MLP-SDAE model outperforms existing methods

Technology Transfer in Spatial Competition when Licensees are Asymmetric

We study technology transfer in a spatial competition with two asymmetric licensees (firms) with an outside innovator who decides how many licenses to offer and the optimal licensing contract. We show the optimal licensing policy is pure royalty contract to both licensees leading to a complete diffusion of the new technology. The result holds irrespective of the cost differentials between the licensees and for innovation of all sizes, that is, drastic or non‐drastic. This robust finding although supports the dominance of royalty licensing in practice; however, consumers may not be necessarily better off. We also throw light on the situation where the innovator sells the patent right to one of the firms. Interestingly, we find that the inefficient firm acquires the new technology and further licenses it to the efficient rival

Sustainability Factors of Self-Help Groups in Disaster-Affected Communities

Self-help groups are informal associations that use social capital to overcome resource constraints and act as a catalyst for rural development, women, and social empowerment. This study tries to identify the factors that affect the sustainability of self-help groups in natural disaster-affected communities. Natural calamities in the form of droughts, floods, or cyclones pose major challenges to livelihood in disaster-prone regions. The study is based on survey data from two different disaster-prone locations: the cyclone- and flood-prone Sundarbans, and drought-prone Bankura in West Bengal, India. Applying principal component analysis to the responses of 143 self-help group members, the study identifies four factors responsible for the sustainability of these self-help groups. This study shows that managerial functions, trust, fund utilization, and easy financing are the factors that matter the most. The findings suggest that policymakers and local governments can focus on these aspects to ensure the effectiveness of self-help groups in meeting their social objectives