Modern Developments in Short Peptide Viral Vaccine Design

Virus-specific CD8+ T cell responses play a major role in the antiviral immune response in hepatitis C virus (HCV) infection. In this context, it has recently been shown that the host Human Leukocyte Antigen (HLA) background plays a crucial role in the outcome of HCV infection. The HLA allele A26 is particularly interesting since it is one of the most frequent HLA alleles in Asia, present in up to 20% of the asian population and since it is associated with slow disease progression in human immunodeficiency virus (HIV) infection. Currently, there is no information available about the role of HLA-A26 in HCV infection, especially since no specific epitopes have been identified thus far. In this study, we describe the identification and characterisation of the first six HLA-A26 restricted HCV-specific CD8+ T cell epitopes. Consequently, HLA-A26 restricted HCV-specific CD8+ T cell responses have been studied in a cohort of acute resolving (three patients) and chronically HCV infected (sixteen patients) patients. Of note, the novel HLA-A26 restricted epitopes turned out to be immunodominant in acute resolving and chronically infected patients and were present both in the peripheral blood as well as the intrahepatic compartment. Viral sequence analysis of the targeted epitopes revealed that they are highly conserved in chronically infected patients, indicating the lack of viral escape mutations. Finally, the capacity of these epitopes to be immunogenic in the context of different HLA molecules was tested. Interestingly, we observed that these epitopes are recognized in a broad range of HLA class I molecules, suggesting degenerate immunogenicity even across supertype boundaries (e.g. HLA-A1 and HLA-A26). In sum, these findings shed light on the immunological meaning of HLA-A26 in HCV infection and have important implications for the future design of an epitope-based therapeutic vaccine.

ABSTRACTThis paper reviews the activity-travel behaviour literature that employs Machine Learning (ML) techniques for empirical analysis and modelling. Machine Learning algorithms, which attempt to build intelligence utilizing the availability of large amounts of data, have emerged as powerful tools in the fields of pattern recognition and big data analysis. These techniques have been applied in activity-travel behaviour studies since the early ’90s when Artificial Neural Networks (ANN) were employed to model mode choice decisions. AMOS, an activity-based modelling system developed in the mid-’90s, has ANN at its core to model and predict individual responses to travel demand management measures. In the dawn of 2000, ALBATROSS, a comprehensive activity-based travel demand modelling system, was proposed by Arentze and Timmermans using Decision Trees. Since then researchers have been exploring ML techniques like Support Vector Machines (SVM), Decision Trees (DT), Neural Networks (NN), Bayes Classifiers, and more recently, Ensemble Learners to model and predict activity-travel behaviour. A large number of publications over the years and an upward trend in the number of published articles over time indicate that Machine Learning is a promising tool for activity-travel behaviour analysis and prediction. This article, first of its kind in the literature, reviews these studies and explores the trends in activity-travel behaviour research that apply ML techniques. The review finds that mode choice decisions have received wide attention in the literature on ML applications. It was observed that most of the studies identify the lack of interpretability as a serious shortcoming in ML techniques. However, very few studies have attempted to improve the interpretability of the models. Further, some studies report the importance of feature engineering in ML-based studies, but very few studies adopt feature engineering before model development. Spatiotemporal transferability of models is another issue that has received minimal attention in the literature. In the end, the paper discusses possible directions for future research in the area of activity-travel behaviour modelling using ML techniques.

A machine learning approach for classifying healthy and infarcted patients using heart rate variabilities derived vector magnitude

Impact of pixel-based machine-learning techniques on automated frameworks for delineation of gross tumor volume regions for stereotactic body radiation therapy

This study examines the use of machine learning (ML) techniques to optimize the basic machining parameters and protrusion dimensions that affect tool shape degeneration in die-sinking electric discharge machining (EDM). The primary objective is to decrease errors and enhance prediction and optimization effectiveness. This study introduces a completely novel tool geometry model aimed at minimizing tool shape degeneration, which, to our knowledge, has not been previously documented in the literature. Additionally, this research represents the first instance of employing ML techniques to generate data for addressing this specific type of problem, further advancing the field of die-sinking EDM. The pivotal machining parameters include discharge current, pulse time and machining depth. Three ML approaches are implemented in this investigation: Artificial Neural Network (ANN), Adaptive-Network-Based Fuzzy Inference System (ANFIS), and Support Vector Machine (SVM). In comparison with experimental outcomes, the ANN technique exhibited superior predictive ability with an coefficient of determination (R 2) of 0.99985 and an Mean Relative Error (MRE) of 0.854%. Four distinct EDM machining scenarios are presented and machining parameters and protrusion dimensions are optimized using the ANN technique to decrease tool shape degeneration. Optimizing the machining parameters and diagonal dimensions of the protrusion substantially reduced tool shape degeneration. This research demonstrates the effectiveness of ANN in optimizing machining parameters and improving tool performance in die-sinking EDM. A significant reduction in total wear area of 66.7% was achieved with a considerably lower time cost through the optimized ANN network. While the study demonstrates promising results, its reliance on specific datasets for training may limit the generalizability of the model to broader machining scenarios.

Machine Learning (ML) is used to learn a system. One of the purposes of this automatic learning is the construction of new computational models. ML shows success in various areas such as reference systems, brain-computer interface, robotics, and chemistry. Recently, Perturbation Theory (PT) operators and ML techniques have been combined to create powerful PTML (PT+ML) models, which are applied to complex biological systems in predicting drug-protein interaction. As for those target proteins involved in the dopamine pathway, nanotechnology, material science, etc. The PTML models add the values ​​of the operators to the values ​​of . Therefore, we need to calculate the values ​​of the PTOs (Perturbation Theory Operators) in the data processing step. This allows us to carry out a process of merging information with variables and conditions from different sources. Moving Averages (MA), multi-condition MA (MMA), double MA, covariance operators, etc., are some examples of useful PTOs. Then, we can use Multiple Linear Regression (MLR), Linear Discriminant Analysis (LDA), or other linear ML techniques to find the PTML model. In non-linear cases, we can fit PTML models using Artificial Neural Networks (ANN), Support Vector Machines (SVM), Classification Trees, and other ML methods. One of the important applications of ANN is found in the study of chemical reactions as an alternative to classical regression and classification techniques. Therefore, in this master's thesis, PTML-ANN models are developed with the intention of visualizing possible improvements in classical techniques. In this context, this term is presented in a generic way. ANNs are inspired by the biological neural networks of the human brain. They are made up of elements that behave in a similar way to the biological neuron in its most common functions. The ANN aside from "resembling" the brain present a series of characteristics of the brain. For example, ANNs learn from experience, generalize from previous examples to new examples, and abstract the main characteristics of a data series. With which these networks function as interconnected neurons that create stimuli. ANN-type network is considered to be a complex biomolecular network consisting of nodes and edges. These networks are formed by "neurons", where each of them is a function, which will take a certain amount of data and provide an output response. The ANN presents three different types of functions which are: • Input function: It is the sum function obtained by multiplying the input and output data by their weights. • Excitation or activation function: This will take as previous input / output data. The most common types of functions depending on the input / output will be the threshold (sigmoid) or hyperbolic tangent. • Transfer functions: It is the function used to close. The value provided by the trigger functions is the input to the ANN network.

Gaining knowledge from health data may contribute to novel approaches or improved patient care plans. To intelligently acquire knowledge from the stroke data, machine learning (ML) technique is utilised and healthcare systems are used. Globally, security has become a major concern, and biometric technologies such as face detection and recognition technologies have been developed to solve security issues and security threats in the world. It has become possible because of developments in technology in the fields of automatic face analysis, machine learning, and pattern recognition. This chapter proposes a prototype to classify brain stroke and face recognition using ML techniques in the medical image. Face recognition and classification of stroke through ML technique like Decision Tree (DT), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Deep Learning with Convolution Neural Network (CNN) based on texture features and statistical features. A CNN model is experimentally used along with a collection of hyper-parameters for deep face recognition and stroke classification. Promising experimental results, with a total accuracy of 98.86% and 98.5%, are obtained, which demonstrates the effectiveness of deep face identification and stroke classification, respectively, in biometric systems.

Particulate matter (PM) emissions, including soot, pose a significant environmental concern. Traditional emission control approaches, such as engine calibration, often struggle to maintain consistent performance across varying driving conditions, engine speeds, and loads. This inconsistency leads to variations in pollutant output, making it challenging for internal combustion engines to comply with stringent emission standards under real-world driving conditions. Physics-based soot models, grounded in complex mathematical equations, have been developed to capture the mechanisms of soot formation. However, these models often face accuracy limitations and require substantial computational resources, rendering them impractical for real-time emission control. In contrast, Machine Learning (ML) techniques offer a more versatile and efficient approach to soot prediction. This paper introduces a machine learning methodology for predicting Soot Particle Numbers (PN). The approach integrates both physics-based and black-box strategies to create a grey-box model for predicting soot emissions from a 1600 cc Gasoline Direct Injection (GDI) engine across diverse load and speed conditions. The physical model is based on a validated 1D engine model in GT-SUITE, while the black-box model is formulated using multiple ML approaches exclusively on measured data. Ultimately, the grey-box model is developed through ML techniques based on measured data and combustion-related parameters obtained from the 1D engine physical model in GT-SUITE. The grey-box model’s results are compared with those of the black-box model. The ML techniques employed in this study include Gaussian Process Regression (GPR), Support Vector Machine (SVM), Artificial Neural Network (ANN), and an Ensemble of Regression Trees (ERT). The Minimum Redundancy Maximum Relevance (MRMR) and RreliefF feature selection methods are used to identify the most crucial features in soot PN prediction. The results indicate that the grey-box GPR exhibits superior performance for both single and multi-pulse injection strategies, achieving a coefficient of determination (R2) of 0.99.

This paper reports on the employment of the machine learning (ML) techniques, namely support vector machine (SVM), artificial neural networks (ANN), and random forest (RF), for predicting the tensile behavior of friction stir processed (FSP) dissimilar aluminium alloys joints (6083-T651 and 8011-H14). The dissimilar aluminium joints are fabricated using the friction stir welding (FSW) process. After that, the friction-stir welded joints are subjected to the FSP procedure at different combinations of process parameters. The rotational speed, traverse speed, and tilt angle are used as the input parameters, while tensile strength and grain size are used as the output parameters. In addition, three performance characteristics (i.e., coefficient of correlation (CC), mean absolute error (MAE), and root mean square error (RMSE)) are used to check the adequacy of the developed model of ML techniques. It is observed that support vector machine_radial basis function kernel is the most accurate modeling technique for predicting the tensile behavior of processes samples. Furthermore, the optical microscope is also utilized to check the grain size of the nugget zone (NZ) of the weld bead for FSP. It is found that the minimum grain size (i.e., 5.06 µm) is obtained for the FSP sample and this grain size corresponded to the high ultimate tensile strength (UTS). Moreover, the fractographic analysis showed the ductile behavior of FSW and FSP samples.

Human Activity Recognition: A Survey

Recently, Speech synthesis is one of the growing techniques in the research domain that takes input as text and provides output as acoustical form. The speech synthesis system is more advantageous to physically impaired people. In execution process, there arise some complications by surrounding noises and communication style. To neglect such unnecessary noises various machine learning techniques are employed. In this paper, we described various techniques adopted to improve the naturalness and quality of synthesized speech. The main contribution of this paper is to elaborate and compare the characteristics of techniques utilized in speech synthesis for different languages. The techniques such as support vector machine, Artificial Neural Network, Gaussian mixture modeling, Generative adversarial network, Deep Neural Network and Hidden Markov Model are employed in this work to enhance the speech naturalness and quality of synthesized speech signals. KeywordsText to speechSyllablesDatabaseArtificial neural networkGaussian mixture modelingDeep neural networkHidden Markov model

PurposeThis study aims to emphasize utilization of Predictive Six Sigma to achieve process improvements based on machine learning (ML) techniques embedded in define, measure, analyze, improve, control (DMAIC). With this aim, this study presents selection and utilization of ML techniques, including multiple linear regression (MLR), artificial neural network (ANN), random forests (RF), gradient boosting machines (GBM) and k-nearest neighbors (k-NN) in the analyze and improve phases of Six Sigma DMAIC.Design/methodology/approachA data set containing 320 observations with nine input and one output variables is used. To achieve the objective which was to decrease the number of fabric defects, five ML techniques were compared in terms of prediction performance and best tools were selected. Next, most important causes of defects were determined via these tools. Finally, parameter optimization was conducted for minimum number of defects.FindingsAmong five ML tools, ANN, GBM and RF are found to be the best predictors. Out of nine potential causes, “machine speed” and “fabric width” are determined as the most important variables by using these tools. Then, optimum values for “machine speed” and “fabric width” for fabric defect minimization are determined both via regression response optimizer and ANN surface optimization. Ultimately, average defect number was decreased from 13/roll to 3/roll, which is a considerable decrease attained through utilization of ML techniques in Six Sigma.Originality/valueAddressing an important gap in Six Sigma literature, in this study, certain ML techniques (i.e. MLR, ANN, RF, GBM and k-NN) are compared and the ones possessing best performances are used in the analyze and improve phases of Six Sigma DMAIC.

Modelling of the pavement acoustic longevity in Hong Kong through machine learning techniques

Background and Objectives: Partial nephrectomy (PN) is the preferred option for treating localized cT1 renal cell carcinoma (RCC), as it preserves renal function in most patients and offers non-inferior oncological outcomes compared to radical nephrectomy. In this study, we aimed to construct a predictive model for estimating the glomerular filtration rate (GFR) at one year after PN in patients with RCC, using various machine learning techniques. Methods: Retrospective data were collected from two academic centers, covering surgeries performed between 2010 and 2022. GFR was estimated using the Chronic Kidney Disease Epidemiology Collaboration 2021 (CKD-EPI) formula. Univariable linear regression (LR) was used to identify significant clinical predictors of 1-year postoperative GFR, followed by multivariable LR. The dataset was split into training and testing cohorts in a 70:30 ratio. Internal validation was performed on the test cohort, and various machine learning methods, including artificial neural networks (ANNs), support vector machines (SVMs), random forests (RFs), and XGBoost, were compared. Results: Among 615 patients treated with PN, 415 had complete follow-up GFR data and were included in the analysis. Only 8.7% of patients experienced significant GFR loss (>30% decrease) at 1 year. Multivariable LR identified baseline GFR (Estimate: 0.76, p < 0.001), tumor diameter on imaging (Estimate: -1.65, p = 0.005), and Charlson Comorbidity Index (Estimate: -1.95, p < 0.001) as independent predictors of 1-year GFR (R2 = 0.67). A 10-fold cross-validation of the multivariable model yielded an R2 of 0.68. In the testing cohort, ANN, SVM, RF, and XGBoost did not outperform the LR model, with R2 values of 0.68, 0.66, 0.64, and 0.55, respectively. Conclusions: Preoperative factors, including baseline GFR, tumor size on imaging, and Charlson Comorbidity Index, are effective predictors of GFR at 1 year following PN. Our study demonstrates that a conventional LR model based on preoperative variables provides acceptable accuracy for predicting GFR after PN and is not inferior to more complex machine learning techniques.

Background: The field of sports science has been fundamentally transformed by the integration of artificial intelligence (AI) technologies. AI has enabled advances in areas such as performance optimization, training personalization, injury risk assessment and prevention, talent recognition, rehabilitation, athlete monitoring, and wellness optimization. Objective: This review aims to explore the diverse impact of AI in sports science, highlighting advances in AI techniques, the challenges and limitations of integrating AI tools, and the emerging role of AI chatbots in shaping the future of sports research and applications. Methods: The review examines the existing literature on the application of machine learning, deep learning and other AI techniques in various aspects of sports science research and practice. It provides an overview of how these technologies have been used to improve personalized training programs, video analysis, injury risk prediction, talent identification, and rehabilitation. Results: The article describes how AI-powered tools and techniques have revolutionized sports science, enabling personalized, data-driven and efficient approaches to performance optimization. Sophisticated machine learning algorithms such as artificial neural networks, decision trees and support vector machines have been used to develop predictive models for injury risk assessment and prevention, leading to improvements in athletes' wellbeing and long-term performance. AI-driven talent identification and selection processes have also shown promise in recognizing exceptional athletes with greater accuracy. In addition, the integration of AI into athlete monitoring and rehabilitation has led to greater personalization, better decision making and faster return to play. Conclusion: The integration of AI and machine learning techniques into sports science has the potential to transform the field and lead to improved athlete performance, reduced risk of injury, improved well-being, and more personalized and effective interventions. The emergence of AI chatbots expands the applications of these technologies in sports science and offers new opportunities to streamline research processes, provide personalized advice to athletes and support sports medicine and rehabilitation