Coronaviruses (especially SARS-CoV-2) are characterized by rapid mutation and wide spread. As these characteristics easily lead to global pandemics, studying the evolutionary relationship between viruses is essential for clinical diagnosis. DNA sequencing has played an important role in evolutionary analysis. Recent alignment-free methods can overcome the problems of traditional alignment-based methods, which consume both time and space. This paper proposes a novel alignment-free method called the correlation coefficient feature vector (CCFV), which defines a correlation measure of the L-step delay of a nucleotide location from its location in the original DNA sequence. The numerical feature is a 16×L-dimensional numerical vector describing the distribution characteristics of the nucleotide positions in a DNA sequence. The proposed L-step delay correlation measure is interestingly related to some types of L+1 spaced mers. Unlike traditional gene comparison, our method avoids the computational complexity of multiple sequence alignment, and hence improves the speed of sequence comparison. Our method is applied to evolutionary analysis of the common human viruses including SARS-CoV-2, Dengue virus, Hepatitis B virus, and human rhinovirus and achieves the same or even better results than alignment-based methods. Especially for SARS-CoV-2, our method also confirms that bats are potential intermediate hosts of SARS-CoV-2.
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http://dx.doi.org/10.1016/j.meegid.2021.105106 | DOI Listing |
Sci Rep
December 2024
Department of Orthodontics, Faculty of Dentistry, Mahidol University, Bangkok, Thailand.
This research evaluated the effectiveness of an online simulation-based serious game as a learning tool in diagnosis and treatment planning for oral lesions (SimOL) in comparison to a pre-recorded lecture-based approach and to determine its appropriate integration into the undergraduate dental curriculum. A crossover randomized control trial was conducted with a cohort of 77 dental undergraduates. They were randomly assigned into two groups.
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December 2024
Henan University of Engineering, Zhengzhou, 451191, China.
Social media generates vast amounts of spatio-temporal sequential data. However, current methods often ignore the complex spatio-temporal correlations within these data. This oversight makes it difficult to fully capture the dynamic features of the data.
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December 2024
School of Biological Sciences, University of the Punjab, Lahore, Pakistan.
The WRINKLED1 (WRI1) transcription factor controls carbon flow in plants through regulating the expression of glycolysis and fatty acid biosynthesis genes. The role of Gossypium hirsutum WRINKLED1 (GhWRI1) in seed-oil accumulation still needs to be explored. Multiple sequence alignment of WRI1 proteins confirmed the presence of two conserved AP2 domains.
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December 2024
College of Computer Science and Engineering, Guangxi Normal University, Guilin, 541000, China.
In today's competitive market environment, accurately identifying potential churn customers and taking effective retention measures are crucial for improving customer retention and ensuring the sustainable development of an organization. However, traditional machine learning algorithms and single deep learning models have limitations in extracting complex nonlinear and time-series features, resulting in unsatisfactory prediction results. To address this problem, this study proposes a hybrid neural network-based customer churn prediction model, CCP-Net.
View Article and Find Full Text PDFClin Radiol
November 2024
Department of Radiology, University Hospital Basel, Petersgraben 4, 4031 Basel, Switzerland.
Purpose: To quantitatively and qualitatively compare the magnitude of metal total hip arthroplasty-induced imaging artifacts in vivo between 1.5T and 0.55T MRI.
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