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Abstract The multiple activities of neurons frequently generate several spiking-bursting variations observed within the neurological mechanism. We show that a discrete fractional-order activated nerve cell framework incorporating a Caputo-type fractional difference operator can be used to investigate the impacts of complex interactions on the surge-empowering capabilities noticed within our findings. The relevance of this expansion is based on the model’s structure as well as the commensurate and incommensurate fractional-orders, which take kernel and inherited characteristics into account. We begin by providing data regarding the fluctuations in electronic operations using the fractional exponent. We investigate two-dimensional Morris–Lecar neuronal cell frameworks via spiked and saturated attributes, as well as mixed-mode oscillations and mixed-mode bursting oscillations of a decoupled fractional-order neuronal cell. The investigation proceeds by using a three-dimensional slow-fast Morris–Lecar simulation within the fractional context. The proposed method determines a method for describing multiple parallels within fractional and integer-order behaviour. We examine distinctive attribute environments where inactive status develops in detached neural networks using stability and bifurcation assessment. We demonstrate features that are in accordance with the analysis’s findings. The Erdös–Rényi connection of asynchronization transformed neural networks (periodic and actionable) is subsequently assembled and paired via membranes that are under pressure. It is capable of generating multifaceted launching processes in which dormant neural networks begin to come under scrutiny. Additionally, we demonstrated that boosting connections can cause classification synchronization, allowing network devices to activate in conjunction in the future. We construct a reduced-order simulation constructed around clustering synchronisation that may represent the operations that comprise the whole system. Our findings indicate the influence of fractional-order is dependent on connections between neurons and the system’s stored evidence. Moreover, the processes capture the consequences of fractional derivatives on surge regularity modification and enhance delays that happen across numerous time frames in neural processing.

Nowadays, technology is increasingly being adopted in different kinds of businesses to process, store, and share sensitive information in digital environments that include enormous numbers of users. However, this has also increased the likelihood of cyberattacks and misuse of information, potentially causing severe damage. One promising technology, which can provide the required security services with an improved level of efficiency, is blockchain. This research explores the use of Ethereum blockchain and smart contracts to create a secure and efficient quality assurance system (QAS) for academic programs. By utilizing blockchain and smart contracts, the proposed approach improves the integrity and reliability of sensitive information processed by the QAS, promotes transparency and governance, and reduces the time and effort required for quality operations. The current approach uses an additional access control layer to further enhance user privacy. Smart contracts automate various quality transactions and saves time and resources, and hence increases the efficiency of the QAS. The interplanetary file system (IPFS) is used to address the challenge of size limitations in blockchain. Additionally, this research investigates the use of various cryptographic schemes to provide robust security services at the application layer. The experimental results showed that the use of a hybrid cryptosystem relying on an Elliptic curve digital signature and AES encryption (AES_ECCDSA) outperforms other counterparts’ cryptosystems using an RSA digital signature and AES encryption (AES_RSADSA) and Elliptic Curve Integrated Encryption Scheme (ECIES) in terms of speed. The performance results showed that AES_ECCDSA consumes 188 ms to perform the required cryptographic operations for a standard-quality document with a size of 8088 KB, compared to the 231 ms and 739 ms consumed by the AES_RSADSA and ECIES schemes, respectively. This study presents a prototype implementation of the blockchain-based QAS, which outlines the processing model and system requirements for key QAS processes. It has been found that the cost and time required for blockchain operations vary depending on the size of the input data—a larger data size requires more time and costs more to process. The results of the current study showed that the time delay for blockchain transactions ranges from 15 to 120 s, while the cost ranges from USD 50 to USD 400. This research provides evidence that blockchain and smart contract technologies have the potential to create a secure, efficient, and trustworthy QAS environment for academic programs.

ABSTRACTSpecific HBsAg mutations are known to hamper HBsAg recognition by neutralizing antibodies thus challenging HBV-vaccination efficacy. Nevertheless, information on their impact and spreading over time is limited. Here, we characterize the circulation of vaccine-escape mutations from 2005 to 2019 and their correlation with virological parameters in a large cohort of patients infected with HBV genotype-D (N = 947), dominant in Europe. Overall, 17.7% of patients harbours ≥1 vaccine-escape mutation with the highest prevalence in subgenotype-D3. Notably, complex profiles (characterized by ≥2 vaccine-escape mutations) are revealed in 3.1% of patients with a prevalence rising from 0.4% in 2005–2009 to 3.0% in 2010–2014 and 5.1% in 2015–2019 (P = 0.007) (OR[95%CI]:11.04[1.42–85.58], P = 0.02, by multivariable-analysis). The presence of complex profiles correlates with lower HBsAg-levels (median[IQR]:40[0–2905]IU/mL for complex profiles vs 2078[115–6037]IU/ml and 1881[410–7622]IU/mL for single or no vaccine-escape mutation [P

Power quality disturbance (PQD) signal classification is crucial for the real-time monitoring of modern power grids, assuring safe and reliable operation and user safety. Traditional power quality disturbance signal classification approaches are sensitive to noise, feature selection, etc. This study introduces a novel approach utilizing a data-driven convolutional neural network (CNN) to improve the effectiveness of power quality disturbance signal classification. Deep learning has been successfully used in various fields of recognition, yielding promising outcomes. Deep learning is often characterized as a complex system, with its filters and layers being determined through empirical investigations. A deep learning model was developed for the purpose of classifying PQDs, with the aim of narrowing down the search for unidentified PQDs to a specific problem domain. This approach demonstrates a high level of efficiency in accelerating the process of recognizing PQDs among a vast database of PQDs. In order to automatically identify the number of filters and the number of layers in the model in a PQD dataset, the proposed model uses pyramidal clustering, the Fukunaga–Koontz transform, and the ratio of the between-class scatter to the within-class scatter. The suggested model was assessed using the synthetic dataset generated, with and without the presence of noise. The proposed models outperformed both well-known pre-trained models and state-of-the-art PQD classification techniques in terms of classification accuracy.

In this paper, we introduce the concept of graded classical B-2-absorbing submodule as a generalization of graded classical 2-absorbing submodule and we give a number of results concerning such graded modules.

ABSTRACT The process of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genetic diversification is still ongoing and has very recently led to the emergence of a new variant of concern (VOC), defined as Omicron or B.1.1.529. Omicron VOC is the most divergent variant identified so far and has generated immediate concern for its potential capability to increase SARS-CoV-2 transmissibility and, more worryingly, to escape therapeutic and vaccine-induced antibodies. Nevertheless, a clear definition of the Omicron VOC mutational spectrum is still missing. Herein, we provide a comprehensive definition and functional characterization (in terms of infectivity and/or antigenicity) of mutations characterizing the Omicron VOC. In particular, 887,475 SARS-CoV-2 Omicron VOC whole-genome sequences were retrieved from the GISAID database and used to precisely define its specific patterns of mutations across the different viral proteins. In addition, the functional characterization of Omicron VOC spike mutations was finely discussed according to published manuscripts. Lastly, residues characterizing the Omicron VOC and the previous four VOCs (Alpha, Beta, Gamma, and Delta) were mapped on the three-dimensional structure of the SARS-CoV-2 spike protein to assess their localization in the different spike domains. Overall, our study will assist with deciphering the Omicron VOC mutational profile and will shed more light on its clinical implications. This is critical considering that Omicron VOC is currently the predominant variant worldwide. IMPORTANCE The Omicron variant of concern (VOC) has a peculiar spectrum of mutations characterized by the acquisition of mutations or deletions rarely detected in previously identified variants, particularly in the spike glycoprotein. Such mutations, mostly residing in the receptor-binding domain, could play a pivotal role in enhancing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infectivity (by increasing binding affinity for ACE2), jeopardizing spike recognition by therapeutic and vaccine-induced antibodies and causing diagnostic assay failure. To our knowledge, this is one of the first exhaustive descriptions of newly emerged mutations underlying the Omicron VOC and its biological and clinical implications.

The wide diversity of microbiota at the genera and species levels across sites and individuals is related to various causes and the observed differences between individuals. Efforts are underway to further understand and characterize the human-associated microbiota and its microbiome. Using 16S rDNA as a genetic marker for bacterial identification improved the detection and profiling of qualitative and quantitative changes within a bacterial population. In this light, this review provides a comprehensive overview of the basic concepts and clinical applications of the respiratory microbiome, alongside an in-depth explanation of the molecular targets and the potential relationship between the respiratory microbiome and respiratory disease pathogenesis. The paucity of robust evidence supporting the correlation between the respiratory microbiome and disease pathogenesis is currently the main challenge for not considering the microbiome as a novel druggable target for therapeutic intervention. Therefore, further studies are needed, especially prospective studies, to identify other drivers of microbiome diversity and to better understand the changes in the lung microbiome along with the potential association with disease and medications. Thus, finding a therapeutic target and unfolding its clinical significance would be crucial.

ABSTRACT Since the beginning of the coronavirus disease 2019 (COVID-19) pandemic caused by it, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been undergoing a genetic diversification leading to the emergence of new variants. Nevertheless, a clear definition of the genetic signatures underlying the circulating variants is still missing. Here, we provide a comprehensive insight into mutational profiles characterizing each SARS-CoV-2 variant, focusing on spike mutations known to modulate viral infectivity and/or antigenicity. We focused on variants and on specific relevant mutations reported by GISAID, Nextstrain, Outbreak.info, Pango, and Stanford database websites that were associated with any clinical/diagnostic impact, according to published manuscripts. Furthermore, 1,223,338 full-length high-quality SARS-CoV-2 genome sequences were retrieved from GISAID and used to accurately define the specific mutational patterns in each variant. Finally, mutations were mapped on the three-dimensional structure of the SARS-CoV-2 spike protein to assess their localization in the different spike domains. Overall, this review sheds light and assists in defining the genetic signatures characterizing the currently circulating variants and their clinical relevance. IMPORTANCE Since the emergence of SARS-CoV-2, several recurrent mutations, particularly in the spike protein, arose during human-to-human transmission or spillover events between humans and animals, generating distinct worrisome variants of concern (VOCs) or of interest (VOIs), designated as such due to their clinical and diagnostic impacts. Characterizing these variants and their related mutations is important in tracking SAR-CoV-2 evolution and understanding the efficacy of vaccines and therapeutics based on monoclonal antibodies, convalescent-phase sera, and direct antivirals. Our study provides a comprehensive survey of the mutational profiles characterizing the important SARS-CoV-2 variants, focusing on spike mutations and highlighting other protein mutations.

An identity management system (IDMS) manages and organizes identities and credentials information exchanged between users, identity providers (IDPs), and service providers (SPs) to ensure confidentiality and enhance privacy of users’ personal data. Traditional or centralized IDMS rely on a third party to store a user’s personal information, authenticate the user, and organize the entire process. This clearly constitutes threats to the privacy of the user, in addition to other issues, such as single point of failure (SPOF), user tracking, and data availability issues. Blockchain technology has many useful features that can contribute to solving traditional IDMS issues, such as decentralization, immutability, and anonymity. Blockchain represents an attractive solution for many issues related to traditional IDMS, including privacy, third-party control, data leakage, and SPOF, supported by Distributed Ledger Technology (DLT) security features and powerful smart contracts technology. The current study presents a systematic literature review and analysis for recently proposed solutions that adopt the traditional centralized approach, as well as solutions based on blockchain technology. The study also aims to provide a deep understanding of proposed IDMS solutions and best practices, and highlight the research gaps and open issues related to IDMSs and users’ privacy. In particular, the current research focuses on analyzing the blockchain-based solutions and illustrating their strengths and weaknesses, as well as highlighting the promising blockchain technology framework that can be utilized to enhance privacy and solve security issues in a centralized IDMS. Such a study is an important step towards developing efficient solutions that address the pressing needs in the field.

The SARS-CoV-2 non-structural protein 13 (nsp13) helicase is an essential enzyme for viral replication and has been identified as an attractive target for the development of new antiviral drugs. In detail, the helicase catalyzes the unwinding of double-stranded DNA or RNA in a 5′ to 3′ direction and acts in concert with the replication–transcription complex (nsp7/nsp8/nsp12). In this work, bioinformatics and computational tools allowed us to perform a detailed conservation analysis of the SARS-CoV-2 helicase genome and to further predict the druggable enzyme’s binding pockets. Thus, a structure-based virtual screening was used to identify valuable compounds that are capable of recognizing multiple nsp13 pockets. Starting from a database of around 4000 drugs already approved by the Food and Drug Administration (FDA), we chose 14 shared compounds capable of recognizing three out of four sites. Finally, by means of visual inspection analysis and based on their commercial availability, five promising compounds were submitted to in vitro assays. Among them, PF-03715455 was able to block both the unwinding and NTPase activities of nsp13 in a micromolar range.

The performance of diagnostic polymerase chain reaction (PCR) assays can be impacted by SARS-CoV-2 variability as this is dependent on the full complementarity between PCR primers/probes and viral target templates. Here, we investigate the genetic variability of SARS-CoV-2 regions recognized by primers/probes utilized by PCR diagnostic assays based on nucleotide mismatching analysis. We evaluated the genetic variation in the binding regions of 73 primers/probes targeting the Nucleocapsid (N, N = 36), Spike (S, N = 22), and RNA-dependent RNA-polymerase/Helicase (RdRp/Hel, N = 15) of the publicly available PCR-based assays. Over 4.9 million high-quality SARS-CoV-2 genome sequences were retrieved from GISAID and were divided into group-A (all except Omicron, >4.2 million) and group-B (only Omicron, >558 thousand). In group-A sequences, a large range of variability in primers/probes binding regions in most PCR assays was observed. Particularly, 87.7% (64/73) of primers/probes displayed ≥1 mismatch with their viral targets, while 8.2% (6/73) contained ≥2 mismatches and 2.7% (2/73) contained ≥3 mismatches. In group-B sequences, 32.9% (24/73) of primers/probes were characterized by ≥1 mismatch, 13.7% (10/73) by ≥2 mismatches, and 5.5% (4/73) by ≥3 mismatches. The high rate of single and multiple mismatches- found in the target regions of molecular assays used worldwide for SARS-CoV-2 diagnosis reinforces the need to optimize and constantly update these assays according to SARS-CoV-2 genetic evolution and the future emergence of novel variants.

The role of novel HBV markers in predicting Hepatitis B virus reactivation (HBV-R) in HBsAg-negative/anti-HBc-positive oncohaematological patients was examined. One hundred and seven HBsAg-negative/anti-HBc-positive oncohaematological patients, receiving anti-HBV prophylaxis for >18 months, were included. At baseline, all patients had undetectable HBV DNA, and 67.3% were anti-HBs positive. HBV-R occurred in 17 (15.9%) patients: 6 during and 11 after the prophylaxis period. At HBV-R, the median (IQR) HBV-DNA was 44 (27–40509) IU/mL, and the alanine aminotransferase upper limit of normal (ULN) was 44% (median (IQR): 81 (49–541) U/L). An anti-HBc > 3 cut-off index (COI) plus anti-HBs persistently/declining to p < 0.001)). The detection of highly sensitive (HS) HBsAg and/or HBV-DNA confirmed at >2 time points, also predicts HBV-R (OR (95% CI): 13.8 (3.6–52.6); 50% of positive vs. 7% of negative patients to these markers experienced HBV-R (p = 0.001)). HS-HBs and anti-HBc titration proved to be useful early markers of HBV-R. The use of these markers demonstrated that HBV-R frequently occurs in oncohaematological patients with signs of resolved HBV infection, raising issues of proper HBV-R monitoring.

Herein, we report a case of an Italian male infected by Delta sublineage AY.4 harboring an atypical deletion, leading to a N gene target failure (NGTF) by a commercial molecular assay for SARS-CoV-2 diagnosis (AllplexTM SARS-CoV-2 Assay, Seegene). A 59-year-old unvaccinated patient was hospitalized for pulmonary embolism, with first negative results obtained by both molecular and antigen tests. After several days of viral negativity, he presented positive results for E and RdRP/S genes, but negative in N gene. Negativity in N gene was repeatedly confirmed in the following days. Suspecting an infection by the Omicron variant, SARS-CoV-2 genome sequencing was rapidly performed from nasopharyngeal swab by MiSeq and revealed the presence of the Delta sublineage AY.4 variant with an atypical deletion of six nucleotides, leading to G214-G215 deletion in the Nucleocapsid, thus responsible for NGTF. The analysis of GISAID sequences (N = 2,618,373 12 January 2022) showed that G214-G215 deletion is rarely occurring in most circulating Delta lineages and sublineages in the globe and Europe, with an overall prevalence never exceeding 0.2%. Hence, this study highlights the importance to perform SARS-CoV-2 sequencing and to characterize novel mutations/deletions that could jeopardize the proper interpretation of molecular diagnostic tests. Based on these assumptions, the role of deletions in the recently identified Omicron variant deserves further investigation.