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Background Coronavirus Disease 2019 (COVID-19) is a viral pandemic disease that may induce severe pneumonia in humans. In this paper, we investigated the putative implication of 12 vaccines, including BCG, OPV and MMR in the protection against COVID-19. Sequences of the main antigenic proteins in the investigated vaccines and SARS-CoV-2 proteins were compared to identify similar patterns. The immunogenic effect of identified segments was, then, assessed using a combination of structural and antigenicity prediction tools. Results A total of 14 highly similar segments were identified in the investigated vaccines. Structural and antigenicity prediction analysis showed that, among the identified patterns, three segments in Hepatitis B, Tetanus, and Measles proteins presented antigenic properties that can induce putative protective effect against COVID-19. Conclusions Our results suggest a possible protective effect of HBV, Tetanus and Measles vaccines against COVID-19, which may explain the variation of the disease severity among regions.

Diabetic retinopathy, a common complication of diabetes, is further exacerbated by factors such as hypertension and obesity. This study introduces the Diabetic Retinopathy Compact Convolutional Transformer (DRCCT) model, which combines convolutional and transformer techniques to enhance the classification of retinal images. The DRCCT model achieved an impressive average F1-score of 0.97, reflecting its high accuracy in detecting true positives while minimizing false positives. Over 100 training epochs, the model demonstrated outstanding generalization capabilities, achieving a remarkable training accuracy of 99% and a validation accuracy of 95%. This consistent improvement underscores the model’s robust learning process and its effectiveness in avoiding overfitting. On a newly evaluated dataset, the model attained precision and recall scores of 96.93% and 98.89%, respectively, indicating a well-balanced handling of false positives and false negatives. The model’s ability to classify retinal images into five distinct diabetic retinopathy categories demonstrates its potential to significantly improve automated diagnosis and aid in clinical decision-making.

Background Coronaviruses, members of the Coronavirinae subfamily in the Coronaviridae family, are enveloped and positive-stranded RNA viruses that infect animals and humans, causing intestinal and respiratory infections. Coronavirus disease 2019 (COVID-19) is caused by a novel coronavirus, named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). This disease appeared, for the first time (December 2019), in China and has spread quickly worldwide causing a large number of deaths. Considering the global threat, the World Health Organization (WHO) has declared, in March 2020, COVID-19 as a pandemic. Many studies suggest the great effect of the existing vaccines to protect against symptomatic cases of death by the COVID-19 virus. This paper, proposes to compare the main antigenic proteins sequences of the existing vaccines with Spike (S) protein of the SARS-CoV-2 genome. Our choice of S protein is justified by the major role that plays it in the receptor recognition and membrane fusion process based on an intelligent system. Herein, we focus on finding a correlation between S protein and compulsory vaccines in the countries that have a less death number by COVID-19 virus. In this work, we have used a combination of coding methods, signal processing, and bioinformatic techniques with the goal to localize the similar patterns between the S gene of the SARS-Cov-2 genome and 14 investigated vaccines. Results A total of 8 similar sequences which have a size more than 6 amino acids were identified. Further, these comparisons propose that these segments can be implicated in the immune response against COVID-19, which may explain the wide variation by country in the severity of this viral threat. Conclusions Our in silico study suggests a possible protective effect of Poliovirus, HIB, Hepatitis B, PCV10, Measles, Mumps, and Rubella (MMR) vaccines against COVID-19.

Helitrons are mobile sequences which belong to the class 2 of eukaryotic transposons. Their specificity resides in their mechanism of transposition: the rolling circle mechanism. They play an important role in remodeling proteomes due to their ability to modify existing genes and introducing new ones. A major difficulty in identifying and classifying Helitron families comes from the complex structure, the unspecified length, and the unbalanced appearance number of each Helitron type. The Helitron’s recognition is still not solved in literature. The purpose of this paper is to characterize and classify Helitron types using spectral features and support vector machine (SVM) classification technique. Thus, the helitronic DNA is transformed into a numerical form using the FCGS2 coding technique. Then, a set of spectral features is extracted from the smoothed Fourier transform applied on the FCGS2 signals. Based on the spectral signature and the classification’s confusion matrix, we demonstrated that some specific classes which do not show similarities, such as HelitronY2 and NDNAX3, are easily discriminated with important accuracy rates exceeding 90%. However, some Helitron types have great similarities such as the following: Helitron1, HelitronY1, HelitronY1A, and HelitronY4. Our system is also able to predict them with promising values reaching 70%.

The Helitrons, an important sub-class of the transposable elements (TEs) class II, have been revealed in diverse eukaryotic genomes. They are mobile elements with great impact on genomic evolution. Till today, there is no systematic classification model of helitrons; that’s why we thought of creating an efficient automatic model to identify these sequences. This paper focuses on the discrimination between helitrons and non-helitrons using the Support Vector Machine (SVM). In this study, we use all the SVM kernels and the higher accuracy rates are obtained by reaching the optimal kernels-parameters (d, c and σ). Further, we introduce two methods to represent the genomic sequences in the form of features to be considered later for the classification task: (i) the temporal and the spectral features extracted from the Frequency Chaos Game Signals order 2 (FCGS2) (ii) the features extracted from the Continuous Wavelet Transform (CWT) applied to the FCGS2 signals. The dataset we used regards two types DNA classes in C.elegans: the helitrons and the repetitive DNA sequences that contain microsatellites and do not form helitrons. The classification results prove that the wavelet energy feature is more effective than the FCGS2 features in the helitron’s recognition system. The performance of our system achieves a high recognition rate (Globally accuracy rate) reaching the value of 92.27%.

Helitrons are typical rolling circle transposons which make up the bulk of eukaryotic genomes. Unlike of other DNA transposons, these transposable elements (TEs), don’t create target site duplications or end in inverted repeats, which make them particular challenge to identify and more difficult to annotate. To date, these elements are not well studied; they only attracted the interest of researchers in biology. The focus of this paper is oriented towards identifying the helitrons in C.elegans genome in the perspective of signal processing. Aiming at the helitron's identification, a novel methodology including two steps is proposed: the coding and the spectral analysis. The first step consists in converting DNA into a 1-D signal based on the statistical features of the sequence (FCGS coding). As for the second step, it aims to identify the global periodicities in helitrons using the Smoothed Fourier Transform. The resulting spectrum and spectrogram are shown to present a specific signature of each helitron’s type.

The Kingdom of Saudi Arabia's interest in developing the tourism sector as an alternative opportunity to fuel economy, and its attempt to activate this sector by sending master plans and economic reforms that help activate the role of tourism in achieving sustainable development, we decided to carry out this study, which aims to present the tourist activity, its importance and determinants that have an impact on sustainable development in the Kingdom of Saudi Arabia, by conducting a statistical field study based on a questionnaire consisting of 47 items, directed to a sample consisting of 197 individuals out of 587 specialists in tourism in the Kingdom of Saudi Arabia, and to analyze the questionnaire, SPSS 15.0 and LISREL 8.7 programs were used to process the data. After confirming the validity of the hypothesis that there are several determinants of tourism in the Kingdom of Saudi Arabia, which have a positive impact on achieving sustainable development. The most important results of the study were that the determinants have a significant impact on tourism activity and have a long-term relationship for tourism investment, and many other determinants. From this we conclude that tourism activity in the Kingdom of Saudi Arabia is linked to the performance of differents sectors, and other areas remain effective as well, due to its economic and social role.