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  • Ultrasensitive detection of clinical pathogens through a target-amplification-free collateral-cleavage-enhancing CRISPR-CasΦ tool

    Ultrasensitive detection of clinical pathogens through a target-amplification-free collateral-cleavage-enhancing CRISPR-CasΦ tool

    Working principle of one-pot, collateral-cleavage-enhancing TCC

    TCC achieves one-pot isothermal amplification-free POC diagnosis through enzymatic CCE amplification enabled by a tailored DNA signal amplifier for CasΦ, termed the TCC amplifier…

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  • Toxic cultures: e-cigarettes and the oral microbial exposome

    Toxic cultures: e-cigarettes and the oral microbial exposome

    Certain chemicals identified in e-cigarette aerosol have known cytotoxic effects

    We began our analysis by investigating the chemical composition of a commercial brand of tobacco-flavored nicotine-containing and nicotine-free e-cigarette aerosol…

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  • Machine learning-based prediction of heating values in municipal solid waste

    Machine learning-based prediction of heating values in municipal solid waste

    Descriptive statistics for dry sample weight, elemental composition, and heating value

    Table 3 presents the descriptive statistics for the dry sample weight (DSW), elemental composition (including carbon, hydrogen, oxygen, nitrogen, sulfur, and…

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  • Intelligent diagnosis of gearbox in data heterogeneous environments based on federated supervised contrastive learning framework

    Intelligent diagnosis of gearbox in data heterogeneous environments based on federated supervised contrastive learning framework

    Datasets

    We assess the performance of the FSCL framework using two gearbox fault datasets: the Drivetrain Dynamic Simulator (DDS) dataset30, and our laboratory’s Wind Turbine Drivetrain Simulator (WTDS) dataset.

    The DDS gearbox dataset includes a…

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  • Investigation of cadmium removal using tin oxide nanoflowers through process optimization, isotherms and kinetics

    Investigation of cadmium removal using tin oxide nanoflowers through process optimization, isotherms and kinetics

    Characterization of tin oxide nanoflowers

    Formation of tin oxide nanoflowers was confirmed by SEM, XRD, FTIR, particle size distribution and point of zero charge (pHPZC) analysis. Figure 2a shows the FTIR spectrum of tin oxide nanoflowers between…

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