Novel moisture retaining dustable powder containing Steinernema abbasi effectively controls damage of subterranean termite in wheat and chickpea.

The application of biocontrol agents in farm operations for pest control programs is gaining priority and preference globally. Effective delivery, infectivity of the biocontrol agents, and quality shelf-life products containing these bioagents are vital parameters responsible for the success of biopesticides under field conditions. In the present study, moisture-retaining bio-insecticidal dustable powder formulation (SaP) of Steinernema abbasi (Sa) infective juveniles (IJs) was developed and assessed for its shelf life, physicochemical profile, and bio-efficacy against subterranean termite under field conditions. Formulation exhibited free-flowing character, with pH of 6.50-7.50, and apparent density in the range 0.50-0.70 g cm-3. The bioefficacy study for two rabi seasons (2020-2021, and 2021-2022) in wheat and chickpea grown in an experimental farm heavily infested with subterranean termites (Odontotermes obesus) revealed a significant reduction in plant damage due to pest attack in formulation-treated plots, monitored in terms of relative number of infested tillers in wheat and infested plants in chickpea fields. The reduced damage to the crop caused by termite was reflected in the relative differences in the growth and yield attributes as well. The study establishes the potential of the developed product as a biopesticide suitable for organic farming and integrated pest management operations.

Utilization of Phyllanthus emblica fruit stone as a Potential Biomaterial for Sustainable Remediation of Lead and Cadmium Ions from Aqueous Solutions

In the present work, an effort has been made to utilize Phyllanthus emblica (PE) fruit stone as a potential biomaterial for the sustainable remediation of noxious heavy metals viz. Pb(II) and Cd(II) from the aqueous solution using adsorption methodology. Further, to elucidate the adsorption potential of Phyllanthus emblica fruit stone (PEFS), effective parameters, such as contact time, initial metal concentration, temperature, etc., were investigated and optimized using a simple batch adsorption method. It was observed that 80% removal for both the heavy metal ions was carried out within 60 min of contact time at an optimized pH 6. Moreover, the thermodynamic parameters results indicated that the adsorption process in the present study was endothermic, spontaneous, and feasible in nature. The positive value of entropy further reflects the high adsorbent-adsorbate interaction. Thus, based on the findings obtained, it can be concluded that the biosorbent may be considered a potential material for the remediation of these noxious impurities and can further be applied or extrapolated to other impurities.

Role of Artificial Intelligence in Drug Discovery and Target Identification in Cancer.

Drug discovery and development (DDD) is a highly complex process that necessitates precise monitoring and extensive data analysis at each stage. Furthermore, the DDD process is both time-consuming and costly. To tackle these concerns, artificial intelligence (AI) technology can be used, which facilitates rapid and precise analysis of extensive datasets within a limited timeframe. The pathophysiology of cancer disease is complicated and requires extensive research for novel drug discovery and development. The first stage in the process of drug discovery and development involves identifying targets. Cell structure and molecular functioning are complex due to the vast number of molecules that function constantly, performing various roles. Furthermore, scientists are continually discovering novel cellular mechanisms and molecules, expanding the range of potential targets. Accurately identifying the correct target is a crucial step in the preparation of a treatment strategy. Various forms of AI, such as machine learning, neural-based learning, deep learning, and network-based learning, are currently being utilised in applications, online services, and databases. These technologies facilitate the identification and validation of targets, ultimately contributing to the success of projects. This review focuses on the different types and subcategories of AI databases utilised in the field of drug discovery and target identification for cancer.

Perceptually Unimportant Information Reduction and Cosine Similarity-Based Quality Assessment of 3D-Synthesized Images.

Quality assessment of 3D-synthesized images has traditionally been based on detecting specific categories of distortions such as stretching, black-holes, blurring, etc. However, such approaches have limitations in accurately detecting distortions entirely in 3D synthesized images affecting their performance. This work proposes an algorithm to efficiently detect the distortions and subsequently evaluate the perceptual quality of 3D synthesized images. The process of generation of 3D synthesized images produces a few pixel shift between reference and 3D synthesized image, and hence they are not properly aligned with each other. To address this, we propose using morphological operation (opening) in the residual image to reduce perceptually unimportant information between the reference and the distorted 3D synthesized image. The residual image suppresses the perceptually unimportant information and highlights the geometric distortions which significantly affect the overall quality of 3D synthesized images. We utilized the information present in the residual image to quantify the perceptual quality measure and named this algorithm as Perceptually Unimportant Information Reduction (PU-IR) algorithm. At the same time, the residual image cannot capture the minor structural and geometric distortions due to the usage of erosion operation. To address this, we extract the perceptually important deep features from the pre-trained VGG-16 architectures on the Laplacian pyramid. The distortions in 3D synthesized images are present in patches, and the human visual system perceives even the small levels of these distortions. With this view, to compare these deep features between reference and distorted image, we propose using cosine similarity and named this algorithm as Deep Features extraction and comparison using Cosine Similarity (DF-CS) algorithm. The cosine similarity is based upon their similarity rather than computing the magnitude of the difference of deep features. Finally, the pooling is done to obtain the objective quality scores using simple multiplication to both PU-IR and DF-CS algorithms. Our source code is available online: https://github.com/sadbhawnathakur/3D-Image-Quality-Assessment.