15 research outputs found

    Additional file 1: of DeviCNV: detection and visualization of exon-level copy number variants in targeted next-generation sequencing data

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    Note S1. Performance comparison based on the mean target depth for a sample. Note S2. Performance evaluation of DeviCNV by qPCR. Note S3. Performance comparison to VisCap, XHMM, and CODEX. Note S4. Sample collection description of for the inherited metabolic disorder panel. Note S5. Visual inspection process to find pathogenic CNVs in patients. Note S6. Performance comparison based on the number of input samples. Note S7. Performance comparison based on the configuration of the sample set used as an input. Note S8. Differences in the number of data points for each exon based on input intervals. Note S9. Performance comparison based on MQV thresholds. Note S10. Low-quality sample filter by using sample-to-sample correlation. Note S11. Performance comparison based on duplication and deletion thresholds for read depth ratios. Note S12. Unique scoring system for selecting high-confidence CNV candidates. Note S13. Inherited metabolic disorder (IMD) panel description. Note S14. Generating targeted NGS data. Note S15. Failure rate of DeviCNV, VisCap, XHMM, and CODEX. Note S16. List of abbreviations. (PDF 908 kb

    Additional file 2: of IL-27 enhances IL-15/IL-18-mediated activation of human natural killer cells

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    Figure S1. Gating strategy for flow cytometry. Figure S2. Gating strategy to identify NK subsets. Figure S3. NK cytotoxicity against ovarian cancer cells. Figure S4. Detection of NK cell surface receptor expression in NK cell lines, NK-92, and NK-92MI. Figure S5. Western blotting analysis for perforin and granzyme B. Figure S6. Overview of human cytokine-mediated NK cell responses. (DOCX 1791 kb

    Additional file 1: of IL-27 enhances IL-15/IL-18-mediated activation of human natural killer cells

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    Table S1. Distribution of PBMCs and human NK cell number in healthy donors (n = 26). Table S2. Percentage of NK cells receptor positive cells in CD3-CD56+ primary NK cells from healthy donors (n = 9). Table S3. Percentage of NK cells receptor positive cells in CD3-CD56+ NK cells from healthy donors (n = 3). (DOCX 37 kb

    Nonvolatile High-Speed Switching Zn-O‑N Thin-Film Transistors with a Bilayer Structure

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    Zinc oxynitride (ZnON) has the potential to overcome the performance and stability limitations of current amorphous oxide semiconductors because ZnON-based thin-film transistors (TFTs) have a high field-effect mobility of 50 cm2/Vs and exceptional stability under bias and light illumination. However, due to the weak zinc–nitrogen interaction, ZnON is chemically unstableN is rapidly volatilized in air. As a result, recent research on ZnON TFTs has focused on improving air stability. We demonstrate through experimental and first-principles studies that the ZnF2/ZnON bilayer structure provides a facile way to achieve air stability with carrier controllability. This increase in air stability (e.g., nitrogen non-volatilization) occurs because the ZnF2 layer effectively protects the atomic mixing between ZnON and air, and the decrease in the ZnON carrier concentration is caused by a shallow-to-deep electronic transition of nitrogen deficiency diffused from ZnON into the interface. Further, the TFT based on the ZnF2/ZnON bilayer structure enables long-term air stability while retaining an optimal switching property of high field-effect mobility (∼100 cm2/Vs) even at a relatively low post-annealing temperature. The ZnF2/ZnON-bilayer TFT device exhibits fast switching behavior between 1 kHz and 0.1 MHz while maintaining a stable and clear switching response, paving the way for next-generation high-speed electronic applications

    Additional file 1: of The natural course of nonculprit coronary artery lesions; analysis by serial quantitative coronary angiography

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    Table S1. Baseline demographic and clinical characteristics of the patients. Table S2. Baseline demographic and clinical characteristics of the patients. Table S3. Table S4. Lesion characteristic and initial QCA of the total lesions. Table S5. Initial and final Diameter Stenosis according to Diabetes and Lesion type. Figure S1. Histogram of DS progression and velocity of DS progression. (DOCX 204 kb

    Data Supplement from Evidence for Molecular Differences in Prostate Cancer between African American and Caucasian Men

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    Supplemental Table S1: Association of Clinical and Pathologic Characteristics with Molecular Abnormalities (CaM Cohort Only). Supplemental Table S2: Association of Clinical and Pathologic Characteristics with Molecular Abnormalities (AAM Cohort Only).</p
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