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Building Blocks of Nature
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作者 Hans Joachim Dudek 《Journal of High Energy Physics, Gravitation and Cosmology》 CAS 2024年第3期1226-1303,共78页
By means of a representation of the elementary objects by the Lagrange density and by the commutators of the communication relations, correlations can be formed using the Fourier transform, which under the conditions ... By means of a representation of the elementary objects by the Lagrange density and by the commutators of the communication relations, correlations can be formed using the Fourier transform, which under the conditions of the Hamilton principle, describes correlation structures of the elementary objects with oscillator properties. The correlation structures obtained in this way are characterized by physical information, the essential component of which is the action. The correlation structures describe the physical properties and their interactions under the sole condition of the Hamilton’s principle. The structure, the properties and the interactions of elementary objects can be led back in this way to a fundamental four dimensional structure, which is therefore in their different modifications the building block of nature. With the presented method, an alternative interpretation of elementary physical effects to quantum mechanics is obtained. This report provides an overview of the fundamentals and statements of physical information theory and its consequences for understanding the nature of elementary objects. 展开更多
关键词 Hamilton Principle as Global Law in Physics Physical Information Generated by Action Correlation Space Mass and Charge Formation interpretation of Physical Effects
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CTpredX:Enhancing missense variant pathogenicity prediction in childhood cancer predisposition genes
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作者 Ferdinando Bonfiglio Vito Alessandro Lasorsa +2 位作者 Giampiero Pirozzi Achille Iolascon Mario Capasso 《Genes & Diseases》 2026年第1期88-90,共3页
Effective clinical genome interpretation relies on accurately distinguishing between benign and pathogenic rare variants.Current machine learning-based variant prioritization tools are trained on genome-wide data and ... Effective clinical genome interpretation relies on accurately distinguishing between benign and pathogenic rare variants.Current machine learning-based variant prioritization tools are trained on genome-wide data and often overlook key parameters defining geneedisease relationships. 展开更多
关键词 machine learning based variant prioritization tools benign pathogenic rare variants distinguishing benign pathogenic rare variantscurrent effective clinical genome interpretation geneedisease relationships childhood cancer predisposition genes missense variant pathogenicity prediction variant prioritization
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