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Mean free path and Mean free time01:22

Mean free path and Mean free time

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Consider two sources of sound, that may or may not be in phase, emitting waves at a single frequency, and consider the frequencies to be the same.
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Although gaseous molecules travel at tremendous speeds (hundreds of meters per second), they collide with other gaseous molecules and travel in many different directions before reaching the desired target. At room temperature, a gaseous molecule will experience billions of collisions per second. The mean free path is the average distance a molecule travels between collisions. The mean free path increases with decreasing pressure; in general, the mean free path for a gaseous molecule will be...
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Substituents on the benzene ring that direct an incoming electrophile to undergo substitution at the meta position are called meta directors. All meta directors either have a positive charge on the atom directly bonded to the ring or a partial positive charge. These groups function by withdrawing electrons from the ring through inductive and resonance effects. Consider the carbocation intermediates formed upon the addition of an electrophile on nitrobenzene at the...
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meta-Directing Deactivators: –NO2, –CN, –CHO, –⁠CO2R, –COR, –CO2H01:13

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All meta-directing substituents are deactivating groups. These substituents withdraw electrons from the aromatic ring, making the ring less reactive toward electrophilic substitution. For example, the nitration of nitrobenzene is 100,000 times slower than that of benzene because of the deactivating effect of the nitro group. The first step in an electrophilic aromatic substitution is the addition of an electrophile to form a resonance-stabilized carbocation. The energy diagrams for...
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mirMachine: A One-Stop Shop for Plant miRNA Annotation
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Meta-Path Methods for Prioritizing Candidate Disease miRNAs.

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    This study introduces novel methods for predicting microRNA-disease associations by constructing similarity networks. The approach effectively identifies potential links, aiding disease research and understanding biological processes.

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    Area of Science:

    • Genomics
    • Bioinformatics
    • Computational Biology

    Background:

    • MicroRNAs (miRNAs) are crucial regulators of gene expression.
    • Dysregulation of miRNAs is linked to complex diseases, particularly cancers.
    • Predicting miRNA-disease associations aids in understanding disease pathogenesis and biological processes.

    Purpose of the Study:

    • To develop effective computational methods for predicting potential miRNA-disease associations.
    • To leverage reconstructed miRNA and disease similarity networks for enhanced prediction accuracy.
    • To enable prediction for diseases lacking known associated miRNAs.

    Main Methods:

    • Reconstruction of a miRNA functional similarity network using family, cluster, miRNA-target, and disease-miRNA data.
    • Reconstruction of a disease similarity network integrating functional and semantic information.
    • Application of Katz with specific weights and Katz with machine learning on a comprehensive heterogeneous network.

    Main Results:

    • Achieved Area Under the Curve (AUC) values of 0.897 and 0.919 with the proposed methods.
    • Demonstrated superior performance compared to existing miRNA-disease association prediction methods.
    • Successfully predicted associations for diseases without previously known related miRNAs.

    Conclusions:

    • The proposed methods, utilizing comprehensive data networks and robust considerations, offer high performance in predicting miRNA-disease associations.
    • These methods provide a valuable tool for exploring disease mechanisms and identifying novel therapeutic targets.
    • The developed web service facilitates access to prediction tools and data for the research community.