Showing papers by "Indian Institute of Technology Indore published in 2021"

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)

Abstract: In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.

A massive rock and ice avalanche caused the 2021 disaster at Chamoli, Indian Himalaya

Abstract: On 7 Feb 2021, a catastrophic mass flow descended the Ronti Gad, Rishiganga, and Dhauliganga valleys in Chamoli, Uttarakhand, India, causing widespread devastation and severely damaging two hydropower projects. Over 200 people were killed or are missing. Our analysis of satellite imagery, seismic records, numerical model results, and eyewitness videos reveals that ~27x106 m3 of rock and glacier ice collapsed from the steep north face of Ronti Peak. The rock and ice avalanche rapidly transformed into an extraordinarily large and mobile debris flow that transported boulders >20 m in diameter, and scoured the valley walls up to 220 m above the valley floor. The intersection of the hazard cascade with downvalley infrastructure resulted in a disaster, which highlights key questions about adequate monitoring and sustainable development in the Himalaya as well as other remote, high-mountain environments.

Ensemble deep learning: A review.

Abstract: Ensemble learning combines several individual models to obtain better generalization performance. Currently, deep learning models with multilayer processing architecture is showing better performance as compared to the shallow or traditional classification models. Deep ensemble learning models combine the advantages of both the deep learning models as well as the ensemble learning such that the final model has better generalization performance. This paper reviews the state-of-art deep ensemble models and hence serves as an extensive summary for the researchers. The ensemble models are broadly categorised into ensemble models like bagging, boosting and stacking, negative correlation based deep ensemble models, explicit/implicit ensembles, homogeneous /heterogeneous ensemble, decision fusion strategies, unsupervised, semi-supervised, reinforcement learning and online/incremental, multilabel based deep ensemble models. Application of deep ensemble models in different domains is also briefly discussed. Finally, we conclude this paper with some future recommendations and research directions.

A Sliding Window Common Spatial Pattern for Enhancing Motor Imagery Classification in EEG-BCI

Abstract: Accurate binary classification of electroencephalography (EEG) signals is a challenging task for the development of motor imagery (MI) brain–computer interface (BCI) systems. In this study, two sliding window techniques are proposed to enhance the binary classification of MI. The first one calculates the longest consecutive repetition (LCR) of the sequence of prediction of all the sliding windows and is named SW-LCR. The second calculates the mode of the sequence of prediction of all the sliding windows and is named SW-Mode. Common spatial pattern (CSP) is used for extracting features with linear discriminant analysis (LDA) used for classification of each time window. Both the SW-LCR and SW-Mode are applied on publicly available BCI Competition IV-2a data set of healthy individuals and on a stroke patients’ data set. Compared with the existing state of the art, the SW-LCR performed better in the case of healthy individuals and SW-Mode performed better on stroke patients’ data set for left- versus right-hand MI with lower standard deviation. For both the data sets, the classification accuracy (CA) was approximately 80% and kappa ( $\kappa $ ) was 0.6. The results show that the sliding window-based prediction of MI using SW-LCR and SW-Mode is robust against intertrial and intersession inconsistencies in the time of activation within a trial and thus can lead to a reliable performance in a neurorehabilitative BCI setting.

Plant-derived natural polyphenols as potential antiviral drugs against SARS-CoV-2 via RNA-dependent RNA polymerase (RdRp) inhibition: an in-silico analysis.

Abstract: The sudden outburst of Coronavirus disease (COVID-19) caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) poses a massive threat to global public health. Currently, no therapeutic drug or vaccine exists to treat COVID-19. Due to the time taking process of new drug development, drug repurposing might be the only viable solution to tackle COVID-19. RNA-dependent RNA polymerase (RdRp) catalyzes SARS-CoV-2 RNA replication and hence, is an obvious target for antiviral drug design. Interestingly, several plant-derived polyphenols effectively inhibit the RdRp of other RNA viruses. More importantly, polyphenols have been used as dietary supplementations for a long time and played beneficial roles in immune homeostasis. We were curious to study the binding of polyphenols with SARS-CoV-2 RdRp and assess their potential to treat COVID-19. Herein, we made a library of polyphenols that have shown substantial therapeutic effects against various diseases. They were successfully docked in the catalytic pocket of RdRp. The investigation reveals that EGCG, theaflavin (TF1), theaflavin-3'-O-gallate (TF2a), theaflavin-3'-gallate (TF2b), theaflavin 3,3'-digallate (TF3), hesperidin, quercetagetin, and myricetin strongly bind to the active site of RdRp. Further, a 150-ns molecular dynamic simulation revealed that EGCG, TF2a, TF2b, TF3 result in highly stable bound conformations with RdRp. The binding free energy components calculated by the MM-PBSA also confirm the stability of the complexes. We also performed a detailed analysis of ADME prediction, toxicity prediction, and target analysis for their druggability. Overall, our results suggest that EGCG, TF2a, TF2b, TF3 can inhibit RdRp and represent an effective therapy for COVID-19. Communicated by Ramaswamy H. Sarma.

Grape Disease Detection Network Based on Multi-Task Learning and Attention Features

Abstract: The disease-free growth of a plant is highly influential for both environment and human life. However, there are numerous plant diseases such as viruses, fungus, and micro-organisms that affect the growth and agricultural production of a plant. Grape esca, black-rot, and isariopsis are multi-symptomatic soil-borne diseases. Often, these diseases may cause leaves drop or sometimes even vanishes the plant/plant vicinity. Hence, early detection and prevention becomes necessary and must be treated on time for better grape growth and productivity. The state-of-the-art either involve classical computer vision techniques such as edge detection/segmentation or regression-based object detection applied over UAV images. In addition, the treatment is not viable until detected leaves are classified for actual disease/symptoms. This results in increased time and cost consumption. Therefore, in this paper, a grape leaf disease detection network (GLDDN) is proposed that utilizes dual attention mechanisms for feature evaluation, detection, and classification. At evaluation stage, the experimentation performed over benchmark dataset confirms that disease detection network could be fairly befitting than the existing methods since it recognizes as well as detects the infected/diseased regions. With the proposed disease detection mechanism, we achieved an overall accuracy of 99.93% accuracy for esca, black-rot and isariopsis detection.

An Overview of Hardware Security and Trust: Threats, Countermeasures, and Design Tools

Abstract: Hardware security and trust have become a pressing issue during the last two decades due to the globalization of the semiconductor supply chain and ubiquitous network connection of computing devices. Computing hardware is now an attractive attack surface for launching powerful cross-layer security attacks, allowing attackers to infer secret information, hijack control flow, compromise system root-of-trust, steal intellectual property (IP), and fool machine learners. On the other hand, security practitioners have been making tremendous efforts in developing protection techniques and design tools to detect hardware vulnerabilities and fortify hardware design against various known hardware attacks. This article presents an overview of hardware security and trust from the perspectives of threats, countermeasures, and design tools. By introducing the most recent advances in hardware security research and developments, we aim to motivate hardware designers and electronic design automation tool developers to consider the new challenges and opportunities of incorporating an additional dimension of security into robust hardware design, testing, and verification.

A Novel Multivariate-Multiscale Approach for Computing EEG Spectral and Temporal Complexity for Human Emotion Recognition

Abstract: This work proposes a novel multivariate-multiscale approach for computing the spectral and temporal entropies from the multichannel electroencephalogram (EEG) signal. This facilitates the recognition of three human emotions: positive, neutral, and negative. The proposed approach is based on the application of the Fourier-Bessel series expansion based empirical wavelet transform (FBSE-EWT). We have extended the existing FBSE-EWT method for multichannel signals and derived FBSE-EWT based multivariate Hilbert marginal spectrum (MHMS) for computing spectral Shannon and K-nearest neighbor (K-NN) entropies. The multivariate FBSE-EWT decomposes the multichannel EEG signals into narrow band subband signals. The multiscaling operation adapted in the spectral domain is based on the selection of successive joint instantaneous amplitude and frequency functions of the subband signals. On the other hand, the time domain multiscale K-NN entropy is computed from the cumulatively added multidimensional subband signals. The extracted spectral and temporal entropy features are smoothed and fed to the sparse autoencoder based random forest (ARF) classifier architecture for emotion classification. The proposed approach is tested using multichannel EEG signals available in a public database (SJTU emotion EEG dataset (SEED)). The bivariate EEG signals from different channel pairs with distinct spatial locations over the scalp are considered as input to our proposed system. The obtained overall classification accuracy of 94.4% reveals that the proposed approach is useful in classifying human emotions. The method is also validated using DREAMER emotion EEG public database. The method outperforms the existing state-of-the-art methods evaluated in these databases.

Elucidating Biophysical Basis of Binding of Inhibitors to SARS-CoV-2 Main Protease by Using Molecular Dynamics Simulations and Free Energy Calculations

Abstract: The recent outbreak of novel "coronavirus disease 2019" (COVID-19) has spread rapidly worldwide, causing a global pandemic. In the present work, we have elucidated the mechanism of binding of two inhibitors, namely α-ketoamide and Z31792168, to SARS-CoV-2 main protease (Mpro or 3CLpro) by using all-atom molecular dynamics simulations and free energy calculations. We calculated the total binding free energy (ΔGbind) of both inhibitors and further decomposed ΔGbind into various forces governing the complex formation using the Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) method. Our calculations reveal that α-ketoamide is more potent (ΔGbind= - 9.05 kcal/mol) compared to Z31792168 (ΔGbind= - 3.25 kcal/mol) against COVID-19 3CLpro. The increase in ΔGbind for α-ketoamide relative to Z31792168 arises due to an increase in the favorable electrostatic and van der Waals interactions between the inhibitor and 3CLpro. Further, we have identified important residues controlling the 3CLpro-ligand binding from per-residue based decomposition of the binding free energy. Finally, we have compared ΔGbind of these two inhibitors with the anti-HIV retroviral drugs, such as lopinavir and darunavir. It is observed that α-ketoamide is more potent compared to lopinavir and darunavir. In the case of lopinavir, a decrease in van der Waals interactions is responsible for the lower binding affinity compared to α-ketoamide. On the other hand, in the case of darunavir, a decrease in the favorable intermolecular electrostatic and van der Waals interactions contributes to lower affinity compared to α-ketoamide. Our study might help in designing rational anti-coronaviral drugs targeting the SARS-CoV-2 main protease.Communicated by Ramaswamy H. Sarma.

Glaciohydrology of the Himalaya-Karakoram.

Abstract: Understanding the response of Himalayan-Karakoram (HK) rivers to climate change is crucial for ~1 billion people who partly depend on these water resources. Policy-makers tasked with sustainable water resources management require an assessment of the rivers' current status and potential future changes. We show that glacier and snow melt are important components of HK rivers, with greater hydrological importance for the Indus basin than for the Ganges and Brahmaputra basins. Total river runoff, glacier melt, and seasonality of flow are projected to increase until the 2050s, with some exceptions and large uncertainties. Critical knowledge gaps severely affect modeled contributions of different runoff components, future runoff volumes, and seasonality. Therefore, comprehensive field observation-based and remote sensing-based methods and models are needed.

An Update on Antiviral Therapy Against SARS-CoV-2: How Far Have We Come?

Abstract: COVID-19 pandemic has spread worldwide at an exponential rate affecting millions of people instantaneously. Currently, various drugs are under investigation to treat an enormously increasing number of COVID-19 patients. This dreadful situation clearly demands an efficient strategy to quickly identify drugs for the successful treatment of COVID-19. Hence, drug repurposing is an effective approach for the rapid discovery of frontline arsenals to fight against COVID-19. Successful application of this approach has resulted in the repurposing of some clinically approved drugs as potential anti-SARS-CoV-2 candidates. Several of these drugs are either antimalarials, antivirals, antibiotics or corticosteroids and they have been repurposed based on their potential to negate virus or reduce lung inflammation. Large numbers of clinical trials have been registered to evaluate the effectiveness and clinical safety of these drugs. Till date, a few clinical studies are complete and the results are primary. WHO also conducted an international, multi-country, open-label, randomized trials-a solidarity trial for four antiviral drugs. However, solidarity trials have few limitations like no placebos were used, additionally any drug may show effectiveness for a particular population in a region which may get neglected in solidarity trial analysis. The ongoing randomized clinical trials can provide reliable long-term follow-up results that will establish both clinical safety and clinical efficacy of these drugs with respect to different regions, populations and may aid up to worldwide COVID-19 treatment research. This review presents a comprehensive update on majorly repurposed drugs namely chloroquine, hydroxychloroquine, remdesivir, lopinavir-ritonavir, favipiravir, ribavirin, azithromycin, umifenovir, oseltamivir as well as convalescent plasma therapy used against SARS-CoV-2. The review also summarizes the data recorded on the mechanism of anti-SARS-CoV-2 activity of these repurposed drugs along with the preclinical and clinical findings, therapeutic regimens, pharmacokinetics, and drug-drug interactions.

Potential Therapeutic Targets and Vaccine Development for SARS-CoV-2/COVID-19 Pandemic Management: A Review on the Recent Update.

Abstract: Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a highly pathogenic novel virus that has caused a massive pandemic called coronavirus disease 2019 (COVID-19) worldwide. Wuhan, a city in China became the epicenter of the outbreak of COVID-19 in December 2019. The disease was declared a pandemic globally by the World Health Organization (WHO) on 11 March 2020. SARS-CoV-2 is a beta CoV of the Coronaviridae family which usually causes respiratory symptoms that resemble common cold. Multiple countries have experienced multiple waves of the disease and scientific experts are consistently working to find answers to several unresolved questions, with the aim to find the most suitable ways to contain the virus. Furthermore, potential therapeutic strategies and vaccine development for COVID-19 management are also considered. Currently, substantial efforts have been made to develop successful and safe treatments and SARS-CoV-2 vaccines. Some vaccines, such as inactivated vaccines, nucleic acid-based, and vector-based vaccines, have entered phase 3 clinical trials. Additionally, diverse small molecule drugs, peptides and antibodies are being developed to treat COVID-19. We present here an overview of the virus interaction with the host and environment and anti-CoV therapeutic strategies; including vaccines and other methodologies, designed for prophylaxis and treatment of SARS-CoV-2 infection with the hope that this integrative analysis could help develop novel therapeutic approaches against COVID-19.

Pareto optimization of WEDM process parameters for machining a NiTi shape memory alloy using a combined approach of RSM and heat transfer search algorithm

Abstract: Machining of shape memory alloys (SMAs) without losing the shape memory effect could immensely extend their applications. Herein, the wire electric discharge machining process was used to machine NiTi—a shape memory alloy. The experimental methodology was designed using a Box-Behnken design approach of the response surface methodology. The effects of input variables including pulse on time, pulse off time, and current were investigated on the material removal rate, surface roughness, and microhardness. ANOVA tests were performed to check the robustness of the generated empirical models. Optimization of the process parameters was performed using a newly formulated, highly efficient heat transfer search algorithm. Validation tests were conducted and extended for analyzing the retention of the shape memory effect of the machined surface by differential scanning calorimetry. In addition, 2D and 3D Pareto curves were generated that indicated the trade-offs between the selected output variables during the simultaneous output variables using the multi-objective heat transfer search algorithm. The optimization route yielded encouraging results. Single objective optimization yielded a maximum material removal rate of 1.49 mm3/s, maximum microhardness 462.52 HVN, and minimum surface roughness 0.11 µm. The Pareto curves showed conflicting effects during the wire electric discharge machining of the shape memory alloy and presented a set of optimal non-dominant solutions. The shape memory alloy machined using the optimized process parameters even indicated a shape memory effect similar to that of the starting base material.

A Survey on Higher-Order QAM Constellations: Technical Challenges, Recent Advances, and Future Trends

Abstract: Communication system’s performance is sensitive to bandwidth, power, cost etc. There have been various solutions to improve the performance, out of them, one of the fundamental solutions over the years is design of optimum modulation schemes. As the research on beyond 5G heats up, we survey and explore power and bandwidth efficient modulation schemes for the next generation communication systems in details. In the existing literature, initially square quadrature amplitude modulation (SQAM) was considered. However, only square constellations are not sufficient for varying channel conditions and rate requirements, thus, efficient odd power of 2 constellations were introduced. For odd power of 2 constellations, rectangular QAM (RQAM) is most commonly used. However, RQAM is not a good choice and modified cross QAM (XQAM) constellation is preferred which provides improved power efficiency over RQAM due to its energy efficient two dimensional (2D) structure. The increasing demand for high data-rates has further encouraged research towards more compact 2D constellations which leads to hexagonal lattice structure based hexagonal QAM (HQAM) constellations. In this work, various QAM constellations are discussed and detailed study of star QAM, XQAM, and HQAM is presented. Generation, peak and average energies, peak-to-average-power ratio, symbol-error-rate, decision boundaries, bit mapping, Gray code penalty, and bit-error-rate of star QAM, XQAM, and HQAM constellations for different constellation orders are presented. Finally, a comparative study of various QAM constellations is presented which justifies the supremacy of HQAM over other QAM constellations for various wireless communication systems and a potential modulation scheme for future standards.

Strength and Durability Assessment of Expansive Soil Stabilized with Recycled Ash and Natural Fibers

Abstract: This study explored the coupling effect of the recycled ash and natural fibers to control the expansive soil's strength and durability attributes The bottom ash (BA) was used to chemically treat the expansive soil and coir fibers (CF) as reinforcement against tensile cracking The sustainable use of BA and CF to stabilize the expansive soil has been demonstrated by assessing - swelling behavior, mechanical and chemical properties The expansive soil was stabilized with 5%, 10%, 15% and 20% BA and reinforced with 025%, 050% and 100% CF The curing period of 28 days was considered for the stabilization of the soil This study presents individual material's effect to stabilize the expansive soil subgrade and also the coupling effect of both fibers and ash The durability of stabilized expansive soil has been assessed by investigating the mechanical and chemical properties before and after 2nd, 4th, 6th, 8th and 10th freeze–thaw cycles The BA stabilized expansive soil exponentially reduces the upward swelling pressure and controls the plasticity behavior An increase in the percentage of BA has increased the calcite content, pH, and electrical conductivity The unconfined compressive strength and split tensile strength have been increased due to BA and CF The CF reinforced specimens shows less loss in mechanical strength during freeze–thaw cycles and gives higher tensile strength The effective mechanism of BA and CF stabilized expansive soil is discussed in detail The BA and CF can be effectively used to stabilize the expansive soil for the application of road pavements The approach used here to stabilize pavement subgrades is sustainable and will provide economical solutions

Mechanochromism and Aggregation-Induced Emission in Phenanthroimidazole Derivatives: Role of Positional Change of Different Donors in a Multichromophoric Assembly.

Abstract: Organic materials possessing solid-state emission responsive to external stimuli have significance in a variety of material, biomedical, and optoelectronic applications. Organic molecules having different donor-acceptor architectures integrated with aggregation-induced emission (AIE) fluorophores have been utilized in development of mechanofluorochromic (MFC) materials. In this work, we have designed and synthesized phenanthroimidazole (PI) based derivatives TPE-PI-1, TPE-PI-2, TPE-PI-3, PTZ-PI-1, PTZ-PI-2, and PTZ-PI-3 where in donors tetraphenylethylene-TPE (D) and phenothiazine-PTZ (D') of contrasting donor abilities are attached to the N and C atom positions of PI. The position and mode of attachment of the donors have been changed, and an additional PTZ spacer has been introduced which has a direct consequence on their photophysical and electronic properties. The PI derivatives manifest AIE, solvatochromic, and mechanochromic behavior. The single crystal X-ray analysis of TPE-PI-1 and PTZ-PI-2 reveals bent structures for the PTZ unit and a twisted conformation for TPE moieties. The density functional theory calculations were used to obtain optimized ground-state structures of the PI derivatives. The work shows a comprehensive comparison of the photophysical, electronic, AIE, and MFC properties of the PI derivatives as an effect of variations in the position of donor, donor-acceptor strength, and change in molecular conformation on use of spacer.

A novel G-quadruplex aptamer-based spike trimeric antigen test for the detection of SARS-CoV-2.

Abstract: The recent SARS-CoV-2 outbreak has been declared a global health emergency. It will take years to vaccinate the whole population to protect them from this deadly virus, hence the management of SARS-CoV-2 largely depends on the widespread availability of an accurate diagnostic test. Toward addressing the unmet need of a reliable diagnostic test in the current work by utilizing the power of Systematic Evolution of Ligands by EXponential enrichment, a 44-mer G-quadruplex-forming DNA aptamer against spike trimer antigen of SARS-CoV-2 was identified. The lead aptamer candidate (S14) was characterized thoroughly for its binding, selectivity, affinity, structure, and batch-to-batch variability by utilizing various biochemical, biophysical, and in silico techniques. S14 has demonstrated a low nanomolar KD, confirming its tight binding to a spike antigen of SARS-CoV-2. S14 can detect as low as 2 nM of antigen. The clinical evaluation of S14 aptamer on nasopharyngeal swab specimens (n = 232) has displayed a highly discriminatory response between SARS-CoV-2 infected individuals from the non-infected one with a sensitivity and specificity of ∼91% and 98%, respectively. Importantly, S14 aptamer-based test has evinced a comparable performance with that of RT-PCR-based assay. Altogether, this study established the utility of aptamer technology for the detection of SARS-CoV-2.

Adaptive User Pairing for NOMA Systems With Imperfect SIC

Abstract: Non-orthogonal multiple access (NOMA) has been recognized as a key driving technology for the fifth generation (5G) and beyond 5G cellular networks. For a practical downlink NOMA system with imperfect successive interference cancellation (SIC), we derive bounds on power allocation factors and formulate a minimum signal-to-interference-plus-noise ratio (SINR) difference criterion for NOMA pair formation. Through extensive simulations, we show the effect of imperfect SIC on the rate performance of a NOMA pair and the trade-off with its OMA (orthogonal multiple access) counterparts. We propose an adaptive user pairing (A-UP) algorithm for achieving better user rates. Further, the proposed A-UP algorithm results in better performance than state-of-the-art NOMA pairing algorithms in the presence of SIC imperfections.