Approach For Identification Research Articles

Hybrid deep learning-based identification of microseismic events in TBM tunnelling

For TBM’s safe and efficient tunnelling, the microseismic monitoring technique has been widely applied in rockburst warning. However, useful microseismic events are often mixed with noisy events, which seriously affects warning accuracy. To this end, this study proposes several hybrid deep learning-based microseismic identification approaches in TBM tunnelling, where recurrent neural networks directly treat monitored microseismic events as time series and avoid complex feature pre-extraction, and grey wolf optimization and attention mechanism are embedded to optimize model hyper-parameters and recognize value information. Additionally, the learning curve and early-stopping strategy are also integrated to reduce overfitting and underfitting risks. Results in a real TBM-excavated water conveyance tunnel indicate that the bidirectional long short-term memory network model combined with grey wolf optimization and attention mechanism achieves the greatest identification performance among discussed models and provides an effective support for intelligent identification of microseismic events under TBM tunnelling environments.

Impactor identification with spallogenic Cr isotopes: The Wabar impact craters (Saudi Arabia)

AbstractPrecise measurements of Cr isotopic composition of terrestrial impactites have successfully provided evidence for the presence of extraterrestrial material and have, in some cases, allowed the identification of the type of impactor responsible for the formation of the impact structure. The high Cr abundance in most meteorite groups aids in detecting extraterrestrial contamination while their distinct isotopic compositions can help with the identification of the nature of the projectile. However, this common approach of detection and identification of extraterrestrial contamination using mass‐independent 53Cr and 54Cr variations fails when the impactor type is an iron meteorite because of their low Cr abundances (which are in a similar range to terrestrial rocks). The present study demonstrates the viability of a spallogenic Cr contribution in iron meteorites (resulting from their long cosmic ray exposure times), which compensates for their low Cr abundances and facilitates the identification of iron‐meteoritic contamination in terrestrial impactites. Thus, it broadens the scope of impactors (and impactites) that can be investigated using mass‐independent Cr isotopes from solely chondrites and primitive achondrites to include iron meteorites. The Wabar impact craters are an optimal candidate for this study, characterized by low weathering, diverse impactites, partial meteorite survival, substantial impactor material contamination, and a felsic target lithology with low background Cr concentration. The Cr isotopic composition of the Wabar background sand, which represents the target lithology, is indistinguishable from the terrestrial Cr isotopic composition range, whereas the Wabar iron meteorites show coupled spallogenic excesses in ε53Cr and ε54Cr. The Cr isotopic compositions of Wabar impactites show resolved deviations from the terrestrial Cr isotopic composition, thereby indicating the presence of Wabar meteoritic contamination. Moreover, the study demonstrates that even an impactor with a non‐carbonaceous chondritic origin, such as a IIIAB iron meteorite, can have a carbonaceous chondrite‐like signature in ε54Cr anomalies due to spallogenic Cr contamination. The study advocates for a comprehensive investigation combining platinum group elements and Cr (and/or Ni, Ru) isotopes to accurately characterize impactor types.

An image processing approach for fatigue crack identification in cellulose acetate replicas

The cellulose acetate replication technique is an important method for studying material fatigue. However, extracting accurate information from pictures of cellulose replicas poses challenges because of distortions and numerous artifacts. This paper presents an image processing procedure for effective fatigue crack identification in plastic replicas. The approach employs thresholding, adaptive Gaussian thresholding, and Otsu binarization to convert gray-scale images into binary ones, enhancing crack visibility. Morphological operations refine object shapes, and Connected Components Analysis facilitates crack identification. Despite limited data, the handcrafted feature extraction algorithm proves robust, addressing challenges. The algorithm shows efficacy in detecting cracks as small as 30 μm, even in the presence of cellulose replication artifacts. The results highlight ability to capture significant cracks’ orientation, length, and growth stages, essential for understanding fatigue mechanisms. Analysis of results, especially evaluation metrics encompassing false positives and false negatives, provides a comprehensive understanding of the algorithm’s strengths and limitations. The proposed tool is available on GitHub.

Identification of switched gated recurrent unit neural networks with a generalized Gaussian distribution

Due to the limitations of the model itself, the performance of switched autoregressive exogenous (SARX) models will face potential threats when modeling nonlinear hybrid dynamic systems. To address this problem, a robust identification approach of the switched gated recurrent unit (SGRU) model is developed in this paper. Firstly, all submodels of the SARX model are replaced by gated recurrent unit neural networks. The obtained SGRU model has stronger nonlinear fitting ability than the SARX model. Secondly, this paper departs from the conventional Gaussian distribution assumption for noise, opting instead for a generalized Gaussian distribution. This enables the proposed model to achieve stable prediction performance under the influence of different noises. Notably, no prior assumptions are imposed on the knowledge of operating modes in the proposed switched model. Therefore, the EM algorithm is used to solve the problem of parameter estimation with hidden variables in this paper. Finally, two simulation experiments are performed. By comparing the nonlinear fitting ability of the SGRU model with the SARX model and the prediction performance of the SGRU model under different noise distributions, the effectiveness of the proposed approach is verified.

Multi-weighted symbolic sequence entropy: a novel approach to fault diagnosis and degradation monitoring of rotary machinery

Abstract Structural health monitoring relies heavily on measurements. Entropy theory is emerging as a critical quantitative analysis technique for interpreting measured data for both health monitoring and fault identification of structural systems. This paper introduces the algorithms of weighted symbolic sequence entropy (WSSE) and its multi-weighted derivation, termed multi-weighted symbolic sequence entropy (MWSSE). WSSE optimizes the existing algorithms, including symbolic sequence entropy (SSE) and improved symbolic sequence entropy (ISSE), by introducing a weighting factor in the computation process of the algorithm. WSSE significantly contributes to enlarging the difference between normal and fault sequences of signals in rotary machinery. The multi-weighted derivation of WSSE, namely MWSSE, more effectively depicts the dynamical characteristics of rotary machinery by utilizing entropy values of multiple weighting factors as health indicators. The MWSSE-based degradation monitoring and fault identification approaches developed by the authors are validated through application research and comparative analysis. These novel entropy algorithms offer innovative quantitative analysis techniques for fault diagnosis in rotary machinery and structural health monitoring and hold potential for application in broader research fields.

Quality by design approach to improve quality and decrease cost of in vitro transcription of mRNA using design of experiments.

In vitro transcription (IVT) reaction is an RNA polymerase-catalyzed production of messenger RNA (mRNA) from DNA template, and the unit operation with highest cost of goods in the mRNA drug substance production process. To decrease the cost of mRNA production, reagents should be optimally utilized. Due to the catalytic, multicomponent nature of the IVT reaction, optimization is a multi-factorial problem, ideally suited to design-of-experiment approach for optimization and identification of design space. We derived a data-driven model of the IVT reaction and explored factors that drive process yield (in g/L), including impact of nucleoside triphosphate (NTP) concentration and Mg:NTP ratio on reaction yield and how to optimize the main cost drivers RNA polymerase and DNA template, while minimizing dsRNA formation, a critical quality attribute in mRNA products. We report a methodological approach to derive an optimum reaction design, with which cost efficiency of the reaction was improved by 44%. We demonstrate the validity of the model on mRNA construct of different lengths. Finally, we maximized the yield of the IVT reaction to 24.9 ± 1.5 g/L in batch, thus doubling the highest ever reported IVT yield.

Digital Humanities and Cultural Identification of Prefec-ture-Level Cities in China and Sustainable Development: A 3-Year Empirical Study (2021-2023) on Digital-Based Literary Narration in Short Videos

The advent of technology has revolutionized the methods by which modern citizens access information and engage in literary narration (through storytelling), transforming it into a multidimensional and intricately interwoven tapestry that encompasses various disciplines such as communications and media. As an exciting frontier of study, numerous scholars are dedicated to theoretical analyses within the realm of digital humanities, presenting vast opportunities for its practical application. This article explores the digital transformation of literary narration within the realm of short videos, which not only serves as a primary means of acquiring information (which engages with a large amount of demographic information) but also facilitates cultural identification with prefecture-level cities in China. The major cities enjoy the “say” in the media landscape; hence, seeking outlets in short videos (which do not need advanced infrastructure) serves as a “life-saving straw” for prefecture-level cities. The dominance of short videos in terms of extensive coverage surpasses that of print media, television, and other traditional media. This new form of literary narration incorporates elements such as pictures, animations, sound effects, and catering to diverse audience preferences and features the grand vitality of digital humanities. This article conducts comprehensive research from 2021 to 2023, aiming to unveil an innovative approach for the cultural identification of prefecture-level cities and the effectiveness of digital humanities applications with regard to literary transformation in the era of short video. Moreover, it endeavors to capture precise data directly from local residents, thereby assessing the efficacy of technology-based literary narration in short video creation and dissemination. Ultimately, these efforts aim to fortify the construction of prefecture-level cities against the challenges posed by metropolises. After 3 years of the experiment, we found that digitally transformed literary narration in short video helps the cultural identification of prefecture-level cities and brings cohesion to their citizens. Therefore, the survival of prefecture-level cities in China necessitates the “snatch” of opportunity for this new form of literary narration and sheds light on the further application of digital humanities.

A systematic search for RNA structural switches across the human transcriptome

RNA structural switches are key regulators of gene expression in bacteria, but their characterization in Metazoa remains limited. Here, we present SwitchSeeker, a comprehensive computational and experimental approach for systematic identification of functional RNA structural switches. We applied SwitchSeeker to the human transcriptome and identified 245 putative RNA switches. To validate our approach, we characterized a previously unknown RNA switch in the 3ʹ untranslated region of the RORC (RAR-related orphan receptor C) transcript. In vivo dimethyl sulfate (DMS) mutational profiling with sequencing (DMS-MaPseq), coupled with cryogenic electron microscopy, confirmed its existence as two alternative structural conformations. Furthermore, we used genome-scale CRISPR screens to identify trans factors that regulate gene expression through this RNA structural switch. We found that nonsense-mediated messenger RNA decay acts on this element in a conformation-specific manner. SwitchSeeker provides an unbiased, experimentally driven method for discovering RNA structural switches that shape the eukaryotic gene expression landscape.

Causal inference with hidden mediators

Summary Proximal causal inference was recently proposed as a framework to identify causal effects from observational data in the presence of hidden confounders for which proxies are available. In this paper, we extend the proximal causal inference approach to settings where identification of causal effects hinges upon a set of mediators that are not observed, yet error prone proxies of the hidden mediators are measured. Specifically, (i) we establish causal hidden mediation analysis, which extends classical causal mediation analysis methods for identifying natural direct and indirect effects under no unmeasured confounding to a setting where the mediator of interest is hidden, but proxies of it are available; (ii) we establish a hidden front-door criterion, criterion to allow for hidden mediators for which proxies are available; (iii) we show that the identification of a certain causal effect called the population intervention indirect effect remains possible with hidden mediators in settings where challenges in (i) and (ii) might co-exist. We view (i)–(iii) as important steps towards the practical application of front-door criteria and mediation analysis as mediators are almost always measured with error and, thus, the most one can hope for in practice is that the measurements are at best proxies of mediating mechanisms. We propose identification approaches for the parameters of interest in our considered models. For the estimation aspect, we propose an influence function-based estimation method and provide an analysis for the robustness of the estimators.

Automatic damage detection and data completion for operational bridge safety using convolutional echo state networks

Structural response data might be partially missing during acquisition and transmission by a structural health monitoring (SHM) system, affecting subsequent structural damage identification. Conventional deep learning methods require a substantial amount of data and have low training efficiency. With this in mind, this paper proposes a hybrid approach for data reconstruction and damage identification using an echo state network embedded with convolutional layers. The network has a training-free reservoir layer and focuses on historical information and multi-channel spatial features. As data redundancy occurs in most measured structural vibration responses, the extended Frobenius norm-principal component analysis method is applied to select valuable samples for updating the network, which greatly enhances the training efficiency. Experimental and numerical data from a physical bridge model is used to verify the data reconstruction capability and damage identification accuracy of the proposed method, and its performance is further compared with several existing deep learning methods.

Linear modeling techniques for liquid height regulation in two tank - Two variable system

In this paper, two linear modeling techniques are presented for regulating the liquid height in two tank - two variable system (TT-TVS). Initially, the nonlinear dynamics of TT-TVS are linearized using the Taylor series linearization technique. However, this method reveals that some parameters of TT-TVS are inexact, necessitating the adoption of system identification techniques or mathematical approaches to accurately identify the linear dynamics. To address this, two new approaches are proposed: (i) a mathematical approach utilizing real-time input-output data, and (ii) a Linear Variable Parameter Transfer Function (LVPTF) model identification approach, which employs real-time data and MATLAB curve fitting tool. A comparative analysis between the proposed identification techniques and existing methods from the literature is also presented. The results indicate that the LVPTF modeling technique offers superior accuracy in identifying the linear dynamics of TT-TVS.

Relay feedback identification and PI-PD control of asymmetrical heating and cooling processes

A common type of nonlinear system in the field of process industry is asymmetrical heating and cooling processes. These systems have two operating modes, heating and cooling, each with a particular dynamic feature. This study deals with identifying and controlling such processes. The relay feedback identification method based on the state-space approach, which results in exact estimates, is used in the identification procedure to achieve two first-order plus dead time (FOPDT) process models. Following process identification, two sets of PI-PD controller parameters are acquired in line with the user-defined gain and phase margin specifications, and the system is controlled using a gain-scheduled PI-PD control method. Some simulation examples are provided to demonstrate the use and profitability of the suggested identification and control approaches. Comparisons with a similar study are provided for both identification and control approaches to clearly demonstrate the benefits of the suggested procedures.

RNA editing regulates glutamatergic synapses in the frontal cortex of a molecular subtype of Amyotrophic Lateral Sclerosis

BackgroundAmyotrophic Lateral Sclerosis (ALS) is a highly heterogenous neurodegenerative disorder that primarily affects upper and lower motor neurons, affecting additional cell types and brain regions. Underlying molecular mechanisms are still elusive, in part due to disease heterogeneity. Molecular disease subtyping through integrative analyses including RNA editing profiling is a novel approach for identification of molecular networks involved in pathogenesis.MethodsWe aimed to highlight the role of RNA editing in ALS, focusing on the frontal cortex and the prevalent molecular disease subtype (ALS-Ox), previously determined by transcriptomic profile stratification. We established global RNA editing (editome) and gene expression (transcriptome) profiles in control and ALS-Ox cases, utilizing publicly available RNA-seq data (GSE153960) and an in-house analysis pipeline. Functional annotation and pathway analyses identified molecular processes affected by RNA editing alterations. Pearson correlation analyses assessed RNA editing effects on expression. Similar analyses on additional ALS-Ox and control samples (GSE124439) were performed for verification. Targeted re-sequencing and qRT-PCR analysis targeting CACNA1C, were performed using frontal cortex tissue from ALS and control samples (n = 3 samples/group).ResultsWe identified reduced global RNA editing in the frontal cortex of ALS-Ox cases. Differentially edited transcripts are enriched in synapses, particularly in the glutamatergic synapse pathway. Bioinformatic analyses on additional ALS-Ox and control RNA-seq data verified these findings. We identified increased recoding at the Q621R site in the GRIK2 transcript and determined positive correlations between RNA editing and gene expression alterations in ionotropic receptor subunits GRIA2, GRIA3 and the CACNA1C transcript, which encodes the pore forming subunit of a post-synaptic L-type calcium channel. Experimental data verified RNA editing alterations and editing-expression correlation in CACNA1C, highlighting CACNA1C as a target for further study.ConclusionsWe provide evidence on the involvement of RNA editing in the frontal cortex of an ALS molecular subtype, highlighting a modulatory role mediated though recoding and gene expression regulation on glutamatergic synapse related transcripts. We report RNA editing effects in disease-related transcripts and validated editing alterations in CACNA1C. Our study provides targets for further functional studies that could shed light in underlying disease mechanisms enabling novel therapeutic approaches.

Managing Patients with Hypereosinophilic Syndrome: A Statement from the Italian Society of Allergy, Asthma, and Clinical Immunology (SIAAIC).

Hypereosinophilic syndrome (HES) encompasses a heterogeneous and complex group of different subtypes within the wider group of hypereosinophilic disorders. Despite increasing research interest, several unmet needs in terms of disease identification, pathobiology, phenotyping, and personalized treatment remain to be addressed. Also, the prospective burden of non-malignant HES and, more in general, HE disorders is currently unknown. On a practical note, shortening the diagnostic delay and the time to an appropriate treatment approach probably represents the most urgent issue, even in light of the great impact of HES on the quality of life of affected patients. The present document represents the first action that the Italian Society of Allergy, Asthma, and Clinical Immunology (SIAAIC) has finalized within a wider project aiming to establish a collaborative national network on HES (InHES-Italian Network on HES) for patients and physicians. The first step of the project could not but focus on defining a common language as well as sharing with all of the medical community an update on the most recent advances in the field. In fact, the existing literature has been carefully reviewed in order to critically integrate the different views on the topic and derive practical recommendations on disease identification and treatment approaches.

An Innovative Approach for Precise Identification of the Lowest Unoccupied Molecular Orbital Using the Parametric Equation of Motion.

The lowest unoccupied molecular orbital (LUMO) plays a crucial role in quantum chemistry, but current quantum chemistry calculations fail to provide useful virtual orbitals, making it challenging to explore various processes such as photochemical reactions, electron attachment, reduction, or excitation processes. The LUMO obtained from the self-consistent field (SCF) solution can not be relied upon and needs to be identified as they are often present among the continuum states having almost similar energies. The nuclear charge stabilization method has been proven useful in identifying LUMO. Herein, we have proposed the application of parametric equations of motion (PEM) in conjunction with nuclear charge stabilization method to identify the LUMO obtained from the SCF solution exhibiting stability with different basis sets including diffuse functions.

Affinity-Resolved Size Exclusion Chromatography Coupled to Mass Spectrometry: A Novel Tool to Study the Attribute-and-Function Relationship in Therapeutic Monoclonal Antibodies.

Assessment of critical quality attributes (CQAs) is an important aspect during the development of therapeutic monoclonal antibodies (mAbs). Attributes that affect either the target binding or Fc receptor engagement may have direct impacts on the drug safety and efficacy and thus are considered as CQAs. Native size exclusion chromatography (SEC)-based competitive binding assay has recently been reported and demonstrated significant benefits compared to conventional approaches for CQA identification, owing to its faster turn-around and higher multiplexity. Expanding on the similar concept, we report the development of a novel affinity-resolved size exclusion chromatography-mass spectrometry (AR-SEC-MS) method for rapid CQA evaluation in therapeutic mAbs. This method features wide applicability, fast turn-around, high multiplexity, and easy implementation. Using the well-studied Fc gamma receptor III-A (FcγRIIIa) and Fc interaction as a model system, the effectiveness of this method in studying the attribute-and-function relationship was demonstrated. Further, two case studies were detailed to showcase the application of this method in assessing CQAs related to antibody target binding, which included unusual N-linked glycosylation in a bispecific antibody and Met oxidation in a monospecific antibody, both occurring within the complementarity-determining regions (CDRs).

PSPI: A deep learning approach for prokaryotic small protein identification.

Small Proteins (SPs) are pivotal in various cellular functions such as immunity, defense, and communication. Despite their significance, identifying them is still in its infancy. Existing computational tools are tailored to specific eukaryotic species, leaving only a few options for SP identification in prokaryotes. In addition, these existing tools still have suboptimal performance in SP identification. To fill this gap, we introduce PSPI, a deep learning-based approach designed specifically for predicting prokaryotic SPs. We showed that PSPI had a high accuracy in predicting generalized sets of prokaryotic SPs and sets specific to the human metagenome. Compared with three existing tools, PSPI was faster and showed greater precision, sensitivity, and specificity not only for prokaryotic SPs but also for eukaryotic ones. We also observed that the incorporation of (n, k)-mers greatly enhances the performance of PSPI, suggesting that many SPs may contain short linear motifs. The PSPI tool, which is freely available at https://www.cs.ucf.edu/∼xiaoman/tools/PSPI/, will be useful for studying SPs as a tool for identifying prokaryotic SPs and it can be trained to identify other types of SPs as well.

Rapid identification of Africanized honey bee Apis mellifera (hymenoptera: apidae) using high-resolution melting analysis DNA from honey

Genetic characterization of honey bee populations, particularly the identification of Africanized honey bees, has traditionally been based on different molecular methods that involve the amplification and analysis of different regions of the mitochondrial or nuclear genome which often take a long time. We introduce high-resolution melting analysis (HRM) as a novel approach for rapid and cost-effective identification of the African lineage in Apis mellifera populations from mitochondrial DNA extracted from honey. Samples were collected from 111 apiaries in 13 Argentinean provinces between 2012 and 2018. Total DNA was extracted from 10 g honey sediment and subjected to quantitative PCR (qPCR) using specific primers (APIS-F/Afr207R) amplifying a 207 bp product. Positive controls (Africanized/Non-Africanized) were used for mitotype validation and a subset of qPCR products were sequenced to compare different haplotypes. HRM analysis enabled the quick detection of sequence polymorphisms in short amplicons generated by qPCR. Among the analyzed apiaries, 26 located above 35° S in Misiones, La Rioja, Catamarca, Formosa, Corrientes, Córdoba, and Santa Fé exhibited the Africanized mitotypes while the remaining apiaries below this latitude, mainly in Buenos Aires, Rio Negro, Neuquén, and Entre Ríos, showed the European mitotypes. These results align with previous characterizations in the region. HRM analysis demonstrated high sensitivity and effectiveness, requiring minimal DNA input, and reducing costs and sampling efforts. This approach provides a rapid and reliable method for identifying the African lineage, facilitating honey bee breeding programs, conservation efforts, and management strategies in regions with Africanized honey bee populations.

Leak and Burst Detection in Water Distribution Network Using Logic- and Machine Learning-Based Approaches

Urban water systems worldwide are confronted with the dual challenges of dwindling water resources and deteriorating infrastructure, emphasising the critical need to minimise water losses from leakage. Conventional methods for leak and burst detection often prove inadequate, leading to prolonged leak durations and heightened maintenance costs. This study investigates the efficacy of logic- and machine learning-based approaches in early leak detection and precise location identification within water distribution networks. By integrating hardware and software technologies, including sensor technology, data analysis, and study on the logic-based and machine learning algorithms, innovative solutions are proposed to optimise water distribution efficiency and minimise losses. In this research, we focus on a case study area in the Sunbury region of Victoria, Australia, evaluating a pumping main equipped with Supervisory Control and Data Acquisition (SCADA) sensor technology. We extract hydraulic characteristics from SCADA data and develop logic-based algorithms for leak and burst detection, alongside state-of-the-art machine learning techniques. These methodologies are applied to historical data initially and will be subsequently extended to live data, enabling the real-time detection of leaks and bursts. The findings underscore the complementary nature of logic-based and machine learning approaches. While logic-based algorithms excel in capturing straightforward anomalies based on predefined conditions, they may struggle with complex or evolving patterns. Machine learning algorithms enhance detection by learning from historical data, adapting to changing conditions, and capturing intricate patterns and outliers. The comparative analysis of machine learning models highlights the superiority of the local outlier factor (LOF) in anomaly detection, leading to its selection as the final model. Furthermore, a web-based platform has been developed for leak and burst detection using a selected machine learning model. The success of machine learning models over traditional logic-based approaches underscores the effectiveness of data-driven, probabilistic methods in handling complex data patterns and variations. Leveraging statistical and probabilistic techniques, machine learning models offer adaptability and superior performance in scenarios with intricate or dynamic relationships between variables. The findings demonstrate that the proposed methodology can significantly enhance the early detection of leaks and bursts, thereby minimising water loss and associated economic costs. The implications of this study are profound for the scientific community and stakeholders, as it provides a scalable and efficient solution for water pipeline monitoring. Implementing this approach can lead to more proactive maintenance strategies, ultimately contributing to the sustainability and resilience of urban water infrastructure systems.

Reverse vaccinology approach for identification of epitopes from E1 protein as peptide vaccine against HCV: A proof of concept

The development of effective vaccines against Hepatitis C Virus (HCV) remains a global health priority and challenge. In this study, we employed an integrative approach combining computational epitope prediction with experimental validation to identify immunogenic peptides targeting the E1 glycoprotein of HCV. In the present report, computational data from various epitope prediction algorithms such as IEDB and SYFPEITHI, followed by molecular dynamics (MD) simulations and immuno-informatics analysis is presented. Through computational screening, we identified potential epitope candidates, with QVRNSSGLY (P3) and QLFTFSPRH (P7) emerging as promising candidates. MD simulations revealed stable interactions between these epitopes and MHC molecule, further validated by free energy estimations using MMPBSA method. Immuno-informatics analysis supported these findings, showing high binding potential and immunogenicity scores for the selected peptides. Subsequent synthesis and characterization of epitope peptides confirmed their structural integrity and purity required for conducting immune activation assays. Experimental immunological assays carried out in this study involved epitope peptide induced activation of CD8 + and CD4 + T cells from healthy human subjects and HCV- recovered patients. Data from experimental validation revealed significant cytokine release upon exposure to epitope peptides, particularly TNF-a, IL-6, and GM-CSF, indicative of robust immune responses. Notably, peptides P3 and P7 exhibited the most pronounced cytokine induction profiles, underscoring their potential as vaccine candidates. Further investigations addressing the mechanism of action of these epitope peptides under preclinical and clinical settings may help in developing effective vaccine against HCV.