The melting of the coding and non-coding classes of natural DNA sequences was investigated using a program, MELTSIM, which simulates DNA melting based upon an empirically parameterized nearest neighbor thermodynamic model. We calculated T(m) results of 8144 natural sequences from 28 eukaryotic organisms of varying F(GC) (mole fraction of G and C) and of 3775 coding and 3297 non-coding sequences derived from those natural sequences. These data demonstrated that the T(m) vs. F(GC) relationships in coding and non-coding DNAs are both linear but have a statistically significant difference (6.6%) in their slopes. These relationships are significantly different from the T(m) vs. F(GC) relationship embodied in the classical Marmur-Schildkraut-Doty (MSD) equation for the intact long natural sequences. By analyzing the simulation results from various base shufflings of the original DNAs and the average nearest neighbor frequencies of those natural sequences across the F(GC) range, we showed that these differences in the T(m) vs. F(GC) relationships are largely a direct result of systematic F(GC)-dependent biases in nearest neighbor frequencies for those two different DNA classes. Those differences in the T(m) vs. F(GC) relationships and biases in nearest neighbor frequencies also appear between the sequences from multicellular and unicellular organisms in the same coding or non-coding classes, albeit of smaller but significant magnitudes.
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http://dx.doi.org/10.1016/j.bpc.2004.01.001 | DOI Listing |
Sensors (Basel)
January 2025
School of Economics and Management, Beijing Information Science & Technology University, Beijing 100192, China.
With the proliferation of mobile terminals and the rapid growth of network applications, fine-grained traffic identification has become increasingly challenging. Methods based on machine learning and deep learning have achieved remarkable results, but they heavily rely on the distribution of training data, which makes them ineffective in handling unseen samples. In this paper, we propose AG-ZSL, a zero-shot learning framework based on traffic behavior and attribute representations for general encrypted traffic classification.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Curtin School of Allied Health, Curtin University, Perth 6102, Australia.
In hospitals, timely interventions can prevent avoidable clinical deterioration. Early recognition of deterioration is vital to stopping further decline. Measuring the way patients position themselves in bed and change their positions may signal when further assessment is necessary.
View Article and Find Full Text PDFMaterials (Basel)
January 2025
Nanoscience and Nanoengineering Programme, İstanbul Technical University, Maslak Campus, İstanbul 34469, Turkey.
We propose a temperature-dependent optimization procedure for the second-nearest neighbor (2NN) * tight-binding (TB) theory parameters to calculate the effects of strain, structure dimensions, and alloy composition on the band structure of heterostructure spherical core/shell quantum dots (QDs). We integrate the thermoelastic theory of solids with the 2NN * TB theory to calculate the strain, core and shell dimensions, and composition effects on the band structure of binary/ternary CdSe/Cd(Zn)S and ZnSe/Zn(Cd)S QDs at any temperature. We show that the 2NN * TB theory with optimized parameters greatly improves the prediction of the energy dispersion curve at and in the vicinity of L and X symmetry points.
View Article and Find Full Text PDFInt J Mol Sci
January 2025
School of Environmental Science and Engineering, Hainan University, Haikou 570228, China.
Hepatocellular carcinoma (HCC), a leading liver tumor globally, is influenced by diverse risk factors. Cellular senescence, marked by permanent cell cycle arrest, plays a crucial role in cancer biology, but its markers and roles in the HCC immune microenvironment remain unclear. Three machine learning methods, namely k nearest neighbor (KNN), support vector machine (SVM), and random forest (RF), are utilized to identify eight key HCC cell senescence markers (HCC-CSMs).
View Article and Find Full Text PDFDiagnostics (Basel)
January 2025
Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig 23119, Turkey.
Electroencephalography (EEG) signal-based machine learning models are among the most cost-effective methods for information retrieval. In this context, we aimed to investigate the cortical activities of psychotic criminal subjects by deploying an explainable feature engineering (XFE) model using an EEG psychotic criminal dataset. In this study, a new EEG psychotic criminal dataset was curated, containing EEG signals from psychotic criminal and control groups.
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