Structural analysis of cotton stalk Puller and Shredder Machine

World scenario after pandemic COVID-19 has been drastically changing and researchers more focusing on, to minimize the post-pandemic effects on economy, energy sustainability and food security. Agriculture sector is playing pivotal role in world food security and energy sustainability. There is high need to optimize the mechanization technologies to increase the yield in limited energy inputs and operation time to fulfill the world growing food demand. This research is mainly focused on the design development and structural analysis aiding with Finite Element Analysis (FEA) approach for Cotton Stalk Puller and Shredder machine (CSPS) to cut the crop leftovers, soil conditioning (shredding the plant waste into soil) and sowing of next crop in single run by conserving input resources. The experimental trials revealed that there is high pressure on cutting blades, chocking of shredder section and excessive pulling load on tractor hitches, which affected the machine’s performance. To mitigate deficiencies and design optimization to improve the machine safety/reliability, the structure analysis carried out. Six core components of machine including baseplate, blade, gear system, root digger, pulley and shaft has investigated as per field conditions. The results revealed that the material of blade, root digger and teeth of gear system receiving the high stress under the operational conditions which results the edge wear and damage. The carbonization up to one-millimeter thickness can provide the extra strength to bear the excessive load on edge layers.

Cheminformatics-Based Discovery of Potential Chemical Probe Inhibitors of Omicron Spike Protein.

During the past two decades, the world has witnessed the emergence of various SARS-CoV-2 variants with distinct mutational profiles influencing the global health, economy, and clinical aspects of the COVID-19 pandemic. These variants or mutants have raised major concerns regarding the protection provided by neutralizing monoclonal antibodies and vaccination, rates of virus transmission, and/or the risk of reinfection. The newly emerged Omicron, a genetically distinct lineage of SARS-CoV-2, continues its spread in the face of rising vaccine-induced immunity while maintaining its replication fitness. Efforts have been made to improve the therapeutic interventions and the FDA has issued Emergency Use Authorization for a few monoclonal antibodies and drug treatments for COVID-19. However, the current situation of rapidly spreading Omicron and its lineages demands the need for effective therapeutic interventions to reduce the COVID-19 pandemic. Several experimental studies have indicated that the FDA-approved monoclonal antibodies are less effective than antiviral drugs against the Omicron variant. Thus, in this study, we aim to identify antiviral compounds against the Spike protein of Omicron, which binds to the human angiotensin-converting enzyme 2 (ACE2) receptor and facilitates virus invasion. Initially, docking-based virtual screening of the in-house database was performed to extract the potential hit compounds against the Spike protein. The obtained hits were optimized by DFT calculations to determine the electronic properties and molecular reactivity of the compounds. Further, MD simulation studies were carried out to evaluate the dynamics of protein-ligand interactions at an atomistic level in a time-dependent manner. Collectively, five compounds (AKS-01, AKS-02, AKS-03, AKS-04, and AKS-05) with diverse scaffolds were identified as potential hits against the Spike protein of Omicron. Our study paves the way for further in vitro and in vivo studies.

Deciphering the Impact of Mutations on the Binding Efficacy of SARS-CoV-2 Omicron and Delta Variants With Human ACE2 Receptor

The pandemic of COVID-19, caused by SARS-CoV-2, has globally affected the human health and economy. Since the emergence of the novel coronavirus SARS-CoV-2, the life-threatening virus continues to mutate and evolve. Irrespective of acquired natural immunity and vaccine-induced immunity, the emerging multiple variants are growing exponentially, crossing the territorial barriers of the modern world. The rapid emergence of SARS-CoV-2 multiple variants challenges global researchers regarding the efficacy of available vaccines and variant transmissibility. SARS-CoV-2 surface-anchored S-protein recognizes and interacts with the host-cell ACE2, facilitating viral adherence and entrance into the cell. Understanding the interfacial interactions between the spike protein of SARS-CoV-2 variants and human ACE2 receptor is important for the design and development of antiviral therapeutics against SARS-CoV-2 emerging variants. Despite extensive research, the crucial determinants related to the molecular interactions between the spike protein of SARS-CoV-2 variants and host receptors are poorly understood. Thus, in this study, we explore the comparative interfacial binding pattern of SARS-CoV-2 spike RBD of wild type, Delta, and Omicron with the human ACE2 receptor to determine the crucial determinants at the atomistic level, using MD simulation and MM/GBSA energy calculations. Based on our findings, the substitution of Q493R, G496S, Q498R, and Y505H induced internal conformational changes in Omicron spike RBD, which leads to higher binding affinity than Delta spike RBD with the human ACE2 receptor, eventually contributing to higher transmission and infectivity. Taken together, these results could be used for the structure-based design of effective antiviral therapeutics against SARS-CoV-2 variants.

Performance Evaluation of Soft Computing Approaches for Forecasting COVID-19 Pandemic Cases.

An unexpected outbreak of deadly Covid-19 in later part of 2019 not only endangered the economies of the world but also posed threats to the cultural, social and psychological barriers of mankind. As soon as the virus emerged, scientists and researchers from all over the world started investigating the dynamics of this disease. Despite extensive investments in research, no cure has been officially found to date. This uncertain situation rises severe threats to the survival of mankind. An ultimate need of the time is to investigate the course of disease transfer and suggest a future projection of the disease transfer to be enabled to effectively tackle the always evolving situations ahead. In the present study daily new cases of COVID-19 was predicted using different forecasting techniques; Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing/Error Trend Seasonality (ETS), Artificial Neural Network Models (ANN), Gene Expression Programming (GEP), and Long Short-Term Memory (LSTM) in four countries; Pakistan, USA, India and Brazil. The dataset of new daily confirmed cases of COVID-19 from the date on which first case was registered in the respective country to 30 November 2020 is analyzed through these five forecasting models to forecast the new daily cases up to 31st January 2020. The forecasting efficiency of each model was evaluated using well known statistical parameters R 2, RMSE, and NSE. A comparative analysis of all above-mentioned models was performed. Finally, the study concluded that Long Short-Term Memory (LSTM) neural network-based forecasting model projected the future cases of COVID-19 pandemic best in all the selected four stations. The accuracy of the model ranges from coefficient of determination value of 0.85 in Brazil to 0.96 in Pakistan. NSE value for the model in India is 0. 99, 0.98 in USA and Pakistan and 0.97 in Brazil. This high-accuracy forecast of COVID-19 cases enables the projection of possible peaks in near future in the aforementioned countries and, therefore, prove to be helpful in formulating strategies to get prepared for the potential hard times ahead.