Indian Institute of Information Technology Allahabad

Introduction Multidrug-resistant (MDR) E. coli presents a significant challenge in clinical settings, necessitating the exploration of novel therapeutic agents. Phytochemicals from Punica granatum (pomegranate) leaves have shown potential antibacterial properties. This study aims to identify and evaluate the efficacy of these phytochemicals against MDR E. coli. Objectives This study aims to identify and evaluate the efficacy of most potential phytochemical of Punica granatum leaf against MDR E. coli. through molecular docking, adme, toxicity, molecular dynamic simulation, MMPBSA and DFT approaches. Methods We performed molecular docking of 11 phytochemicals from the IMPPAT database with four MDR E. coli targets: 1AJ6, 1FJ8, 4BJP, and 6BU3. Granatin B demonstrated the best binding affinity and was further analyzed. ADME (Absorption, Distribution, Metabolism, and Excretion) and toxicity analyses were conducted to assess its pharmacokinetic properties and safety profile. Molecular Dynamics (MD) simulations were performed to evaluate the stability of Granatin B with the targets. Finally, density functional theory (DFT) analysis was carried out to understand the electronic properties and reactivity of Granatin B. Results Granatin B exhibited the highest binding affinity among the 11 phytochemicals, indicating strong potential as an inhibitor of MDR E. coli. ADME analysis revealed favorable pharmacokinetic properties and toxicity analysis confirmed that Granatin B is non-toxic. MD simulations showed stable interactions between Granatin B and all four targets. DFT analysis provided insights into the electronic properties and reactive sites of Granatin B, supporting its potential mechanism of action. Result Granatin B exhibited the highest binding affinity among the 11 phytochemicals, indicating strong potential as an inhibitor of MDR E. coli. ADME analysis revealed favorable pharmacokinetic properties, and toxicity analysis confirmed that Granatin B is non-toxic. MD simulations showed stable interactions between Granatin B and all four targets. DFT analysis provided insights into the electronic properties and reactive sites of Granatin B, supporting its potential mechanism of action. Conclusion Granatin B from Punica granatum leaves is a promising candidate for treating MDR E. coli infections. The integration of molecular docking, ADME, toxicity, MD simulations, and DFT analysis underscores its therapeutic potential and paves the way for further experimental validation and development as a novel antibacterial agent.

This paper examines the secrecy performance of a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) empowered wireless communication system, where a base station sends its confidential data through a STAR-RIS to trusted outdoor and indoor users while facing threats from outdoor and indoor eavesdroppers. We also leverage the benefits of direct links between BS and outdoor users along with the STAR-RIS link, whereas indoor users only rely on the STAR-RIS link due to the severe blockages. We derive the secrecy outage probability (SOP) and ergodic secrecy capacity (ESC) expressions for both the users over Nakagami-m fading channels. In addition, we present the asymptotic SOP expressions in high signal-to-noise ratio (SNR) and main-to-eavesdropper ratio (MER) regimes to reveal more insights into the secrecy diversity orders of both users. We then analytically discuss that the high SNR slopes of ESCs for both users are equal to zero. Additionally, we provide an analytical framework to demonstrate the impact of STAR elements on the SOP and ESC performance under two cases of interest: 1) when STAR-RIS is out of service and 2) when RIS consists of a very large number of STAR elements. A tradeoff between the energy efficiency and secrecy capacity is also discussed. Finally, numerical and simulation studies verify our analytical findings.

Piezoelectric energy harvesting (PEH) has surfaced as an innovative technology for supplying power to low‐power electronic devices by converting mechanical energy into electrical energy. This technology utilizes the piezoelectric effect, in which specific materials produce an electric charge when they experience mechanical stress. Piezoelectric materials can be categorized into three main types: single crystal, composite, and polymeric. Single‐crystal materials exhibit elevated piezoelectric coefficients and stability; however, they tend to be costly and fragile. Composite materials integrate piezoelectric ceramics with polymer matrices, enhancing flexibility and lowering costs. Polymeric materials exhibit lightweight, flexible, and biocompatibility characteristics, rendering them ideal for wearable and implantable applications. Although PEH presents considerable promise, it is essential to tackle challenges, including low power output, material constraints, and environmental influences. Future investigations will focus on creating innovative materials that exhibit improved piezoelectric characteristics, refining device architecture for optimal energy conversion, and incorporating piezoelectric harvesting technology into intelligent systems. By addressing these challenges and investigating creative solutions, PEH can significantly advance sustainable and self‐powered electronic devices.

Social Media acts as a primary source of information, opinion, and news source for millions of individuals every day for over a decade. This has never been as apparent as the global pandemic of COVID-19, wherein a span of more than just a year, the evolution of emotions amongst the users of social media has never been so swift and fickle. This study uses Reddit post-extraction and classifies 60,370 posts between the time frame of February 11th, 2020 to January 26th, 2021 from the two major subreddits of r/COVID and r/COVID19. With the help of the Lexicon Approach, the posts are classified into positive, negative, and neutral sentiment polarities, and then distributive frequencies and valence scores are measured for measuring emotional contagion. The findings reveal that there is a high presence of negative sentiments in the posts, and the increase in sentimental extremities occurred in three time-frames, the initial pandemic stage; the implementation of massive lockdowns stage; and the approval and administration of vaccines stage. It also shows that there is a linear relationship between the valence of exposed stimuli and their response. Emotional contagion is present in both positive as well as negative posts. The important implications can be drawn for the emotional wellbeing, perspective, and contagion of the users of Reddit.

Pathological significance of interaction of Synphilin-1 with mutated alpha-synuclein is well known to have serious consequences in causing the formation of inclusion bodies that are linked to Parkinson's disease (PD). Information extracted so far pointed out that specific mutations, A53T, A30P, and E46K, in alpha-synuclein promote such interactions. However, a detailed structural study of this interaction is pending due to the unavailability of the complete structures of the large protein Synphilin-1 of chain length 919 residues and the mutated alpha-synuclein having all the reported specific mutations so far. In this study, a semi-automatic pipeline-based meta-predictor, AlphaLarge, is created to predict high-fidelity structures of large proteins like Synphilin-1 given the limitations of the existing protocols. AlphaLarge recruits a novel augmented AlphaFold model that uses a divide and conquer based strategy on the foundation of a self-sourced template dataset to choose the best structure model through their standard validations. The structure models were re-validated by a Protein Mediated Interaction Analysis (PMIA) formalism that uses the existing structurally relevant information of these proteins. For the training dataset, the new method, AlphaLarge, performed reasonably better than AlphaFold. Also, the new residue-and domain-based structural details of interactions of resultant best structure models of Synphilin-1 and both wild and mutated alpha-synuclein are extracted using PMIA. This result paves the way for better screening of target specific drugs to control the progression of PD, in particular, and research on any kind of pathophysiology involving large proteins of unknown structures, in general.

These past few years, technology has simplified the process of gathering and arranging time series data. This paves the way for tremendous opportunities to gain helpful insights by analysing these data. Historically, statistical models have been used for time series analysis. These models work well for linear or univariate data but struggle to accurately capture complex nonlinear trends or when unpredictable external factors impact the data (such as stock or trading prices). Long Short-Term Memory (LSTM) has emerged as one of the most popular options for analysing time series data efficiently. However, there are still a few challenges associated with it. The main reason for this study is to discuss these shortcomings and propose an intelligent system to deal with these flaws. One of the challenges is the choice of hyperparameters, which are handled successfully using the Genetic Algorithm (GA) to optimise the LSTM method’s hyperparameters and its variants. In this case, the GA solves the combinatorial optimization problem of finding the optimal hyper-parameters for the LSTM model and selecting the appropriate features from the data set. The proposed model has experimented on the National Stock Exchange-Fifty (NIFTY-50) dataset and found that the accuracy of all LSTM variants was improved. On average, the GA improved the performance of LSTM models by 60.11%. The Bidirectional LSTM model performs best with a root mean square error of an average of 66.39% and mean absolute error of an average of 49.43% after optimization, followed by the classic LSTM and the stacked LSTM models. The combination of LSTM with GA holds promise for enhancing the predictive power of time series analysis in various fields.

Energy Internet is well-known nowadays for enabling bidirectional V2G communication; however, with communication and computation abilities, V2G systems become vulnerable to cyber-attacks and unauthorised access. An authentication protocol verifies the identity of an entity, establishes trust, and allows access to authorized resources while preventing unauthorized access. Research challenges for vehicle-to-grid authentication protocols include quantum security, privacy, resilience to attacks, and interoperability. The majority of authentication protocols in V2G systems are based on public-key cryptography and depend on some hard problems like integer factorization and discrete logs to guarantee security, which can be easily broken by a quantum adversary. Besides, ensuring both information security and entity privacy is equally crucial in V2G scenarios. Consequently, this work proposes a quantum-secured privacy-preserving key authentication and communication (QSKA) protocol using superdense coding and a hash function for unconditionally secure V2G communication and privacy. QSKA uses a password-based authentication mechanism, enabling V2G entities to securely transfer passwords using superdense coding. The QSKA security verification is performed in proof-assistant Coq. The security analysis and performance evaluation of the QSKA show its resiliency against well-known security attacks and reveal its enhanced reliability and efficiency with respect to state-of-the-art protocols in terms of computation, communication, and energy overhead.

Ceramics have long been recognized for their exceptional properties, ranging from mechanical stability to biocompatibility, making them highly promising candidates for various medical and healthcare innovations. The chapter elucidates the essential processes involved in ceramic synthesis, from precursor selection to material shaping and different heat treatments. It delves into diverse synthesis methods, including traditional solid-state reactions and advanced techniques like sol–gel and hydrothermal routes. The discussion extends to the crucial principles underlying ceramic synthesis, including crystallography, phase transformations, and microstructural development. These principles serve as the cornerstone for designing ceramics with optimal properties for biomedical applications. The chapter explores how variations in synthesis parameters influence material characteristics, leading to tailored properties such as mechanical strength, surface reactivity, and porosity for their applications in tissue engineering, drug delivery, and medical diagnostics. By synergistically addressing the synthesis intricacies and the biomedical landscape, this chapter provides a comprehensive resource for harnessing the potential of ceramics to revolutionize healthcare technologies. Ultimately, it underscores how a deep understanding of synthesis processes and principles lays the groundwork for unlocking the transformative capabilities of ceramics in the dynamic field of biomedicine as well.

A comparative genomic analysis approach provides valuable information about genetic variations and evolutionary relationships among microorganisms, aiding not only in the identification of functional genes responsible for traits such as pathogenicity, antibiotic resistance, and metabolic capabilities but also in enhancing our understanding of microbial genomic diversity and their ecological roles, such as supporting plant growth promotion, thereby enabling the development of sustainable strategies for agriculture. We used two strains from different Bacillus species, Bacillus velezensis AK-0 and Bacillus atrophaeus CNY01, which have previously been reported to have PGP activity in apple, and performed comparative genomic analysis to understand their evolutionary process and obtain a mechanistic understanding of their plant growth-promoting activity. We identified genomic features such as mobile genetic elements (MGEs) that encode key proteins involved in the survival, adaptation and growth of these bacterial strains. The presence of genomic islands and intact prophage DNA in Bacillus atrophaeus CNY01 and Bacillus velezensis AK-0 suggests that horizontal gene transfer has contributed to their diversification and acquisition of adaptive traits, enhancing their evolutionary advantage. We also identified novel DNA motifs that are associated with key physiological processes and metabolic pathways.

Meta-learning algorithms learn from other learning algorithms to solve new tasks with only a few labeled instances. Despite being effective for quick learning, it has some limitations. During meta-training phase, inconsequential connections are frequently seen, which leads to an over-parameterized neural network with unnecessary extra gradient computation and memory overhead. To overcome these limitations, we propose a meta-learning method, Meta-LTH, that utilizes the lottery ticket hypothesis technique to prune the neural network using magnitude pruning for retaining essential connections. The pruning process during meta-training generates indispensable connections that can be utilized effectively to solve the few-shot learning problem. Meta-LTH achieves two goals: (a) a sub-network that can adapt more efficiently to meta-learning test tasks, and (b) learns new low-level features of unseen tasks and combines them with the already learned features during the meta-testing phase. Experimental results demonstrate that the Meta-LTH method outperforms the existing first-order MAML algorithm for three classification datasets. The improvement in the classification accuracy by approximately 2% (20-way 1-shot task setting) for the Omniglot and FC100 datasets indicates the ability of Meta-LTH to quickly learn to learn features that are absent in the meta-training data and combine newly learnt feature with the existing features.

Lanthanide-based light-emitting coordination polymers (CPs) and CP gels (CPGs) have significance for applications in optical systems, image processing/multiplexing, and optical sensors. In this study, we report two new luminescent CPs (EuL-CP (1) and TbL-CP (2)) and CPGs (EuL-gel (5) and TbL-gel (6)) using lanthanide(III) ions (Ln(III) = Eu(III) and Tb(III)) and 4-(4-carboxyphenyl)-2,2:6,2-terpyridine ligand (L) capable of forming stable thermoreversible gels. Probable structures of EuL-CP (1) and TbL-CP (2) and the formations of EuL-gel (5) and TbL-gel (6) are proposed based on adequate computational studies. The CPGs are stimuli-responsive and could be utilized as invisible security inks for encryption. Further, poly(methyl methacrylate) (PMMA) polymer doped with respective CPs (0.75 wt %) is found to be suitable for forming composite films with UV-shielding properties.

Ebola virus (EBOV), a member of the Filoviridae family, causes EBOV disease (EVD), a highly fatal and neglected tropical disease (NTD). Despite its severity, treatment options remain limited. While therapies such as monoclonal antibodies have shown some efficacy, they are insufficient to fully control the virus’s spread. Given the urgent need for novel treatments, antiviral peptides (AVPs), a type of antimicrobial peptides (AMPs), are emerging as promising alternatives due to their high selectivity, specificity, and low toxicity. However, there is no dedicated resource for AVPs against EBOV i.e., anti-Ebola peptides (AEPs). To address this gap, we have developed the Anti-Ebola Peptide Database (AEPDB), the first comprehensive database specifically for AEPs. AEPDB compiles 69 AEPs (67 unique) targeting multiple EBOV strains, sourced from literatures. The resource includes detailed information on peptide sequences, target EBOV strains, biological targets, mechanisms of action, and validation status, with cross-references to PubMed, UniProt, and PDB databases. We have designed AEPDB with an intuitive user interface (UI), offering basic, advanced, and list-based search options, detailed browsing, and data exports in various formats. AEPDB is a crucial resource for advancing research into EBOV therapeutics, supporting the development of new drugs to combat this deadly virus. AEPDB is freely accessible via any web browser at the URL: https://bblserver.org.in/aepdb/.

The electrostatic behavior of a hydrogen sulfide (H$_2$S)-based double-gate (DG) single-electron transistor (SET) for the charge detection of toxic H$_2$S gas has been investigated and its potential for switching applications with different orientations of H$_2$S quantum dot explored. The electronic properties of the SET operating in the coulomb blockage region have been analyzed using advanced modeling techniques like density functional theory (DFT) and non-equilibrium Green’s function formalism, implemented in the QuantumWise-ATK. Through simulations, the charging energies of H$_2$S molecules within the SET environment have been calculated, and the plot of total energy with gate voltage developed, which serves as a basis to generate the charge stability diagram. This diagram illustrates the nature of electron conduction in different charge states, which act as unique electronic fingerprints for the identification of H$_2$S gas in different orientations. Moreover, it is observed that operating this SET model under negative gate bias is more energetically efficient than under positive bias.

The main aim of this paper is to study an obvious linear representation of a multiplicative Lie algebra. Also, we find some criteria to determine all possible multiplicative Lie algebra structures on a general linear group and we show that the general linear group on a finite field is a Lie simple group.

This Editorial summarizes the content of a focus collection on the synthesis, characterization and applications of low dimensional materials. The collection groups original research and review articles providing recent results and prospects in the field, with particular attention on optoelectronic applications, dielectric and magnetic properties, and electrochemical and biological performances.