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Showing 1,501 - 1,520 results of 1,714 for search 'significantly ((larger decrease) OR (small decrease))', query time: 0.28s Refine Results
  1. 1501

    Table 6_Identification of potential miRNA biomarkers for neurobrucellosis diagnosis.xlsx by Meiling Liu (390553)

    Published 2025
    “…This study aimed to analyze the microRNA (miRNA) profile of cerebrospinal fluid (CSF) in NB patients to identify potential biomarkers for NB diagnosis.</p>Methods<p>Small RNA sequencing was utilized to identify differential expressed miRNAs (DEmiRNAs) in CSF samples from both control and NB patients (in-house cohort). …”
  2. 1502

    <i>DANCR</i> regulated genes in SK-MEL-28 cells. by Stephanie M. E. Jones (12883691)

    Published 2025
    “…MITF and c-MYC, key melanoma transcription factors, regulate human <i>DANCR</i> expression and melanoma patients with high <i>DANCR</i> display significantly decreased survival. <i>DANCR</i> is a melanoma oncogene that controls cancer-associated gene expression networks to promote human melanoma cell proliferation and migration. …”
  3. 1503

    Data Sheet 1_Preparation of biosorbent from avocado seeds for preconcentration and simultaneous extraction of trace parabens in environmental wastewater samples.docx by Aluwani Sharon Nephiphidi (22696835)

    Published 2025
    “…The reusability and regeneration adsorption percentage recoveries significantly decreased after the fifth cycle. Parabens extraction using the 30% KOH- magnetic activated carbon was considered a capable biosorbent for the extraction of parabens because it demonstrated a strong potential for extracting parabens pollutants simultaneously in wastewater samples.…”
  4. 1504

    Table 5_Identification of potential miRNA biomarkers for neurobrucellosis diagnosis.xlsx by Meiling Liu (390553)

    Published 2025
    “…This study aimed to analyze the microRNA (miRNA) profile of cerebrospinal fluid (CSF) in NB patients to identify potential biomarkers for NB diagnosis.</p>Methods<p>Small RNA sequencing was utilized to identify differential expressed miRNAs (DEmiRNAs) in CSF samples from both control and NB patients (in-house cohort). …”
  5. 1505

    Presentation 1_Anti-PD1 therapies induce an early expansion of Ki67+CD8+ T cells in metastatic non-oncogene addicted NSCLC patients.pptx by Alain Gelibter (6409364)

    Published 2024
    “…Concurrently, 14 soluble immune checkpoints were analyzed by Luminex assay. Immunotherapy significantly increased the levels of Ki67<sup>+</sup>(total and CD8<sup>+</sup>) T cells, PMN(Lox1<sup>+</sup>)-MDSCs, non-suppressive Tregs (nsTregs), and soluble PD1 from T0 to T1 in the entire NSCLC population, while decreased active Tregs. …”
  6. 1506

    Fig 4 - by Helene Dumont (19812720)

    Published 2024
    “…However, there was a significant difference in the estimates of the base amplitude for inversion and negation effects, with a higher CrI for the inversion effect, indicating that the effect of inversion was overall larger that the effect of negation.…”
  7. 1507

    Ablation Experiment GradCAM Heatmap. by Chunhua Yang (346871)

    Published 2025
    “…The slice-assisted super-reasoning algorithm is integrated to improve the recognition ability of YOLOv10 network for long-distance and small-target tea. The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. …”
  8. 1508

    Space-to-depth convolution. by Chunhua Yang (346871)

    Published 2025
    “…The slice-assisted super-reasoning algorithm is integrated to improve the recognition ability of YOLOv10 network for long-distance and small-target tea. The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. …”
  9. 1509

    Data augmentation. by Chunhua Yang (346871)

    Published 2025
    “…The slice-assisted super-reasoning algorithm is integrated to improve the recognition ability of YOLOv10 network for long-distance and small-target tea. The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. …”
  10. 1510

    Side angle tea picking. by Chunhua Yang (346871)

    Published 2025
    “…The slice-assisted super-reasoning algorithm is integrated to improve the recognition ability of YOLOv10 network for long-distance and small-target tea. The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. …”
  11. 1511

    Comparison results of ablation experiments. by Chunhua Yang (346871)

    Published 2025
    “…The slice-assisted super-reasoning algorithm is integrated to improve the recognition ability of YOLOv10 network for long-distance and small-target tea. The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. …”
  12. 1512

    Table of dataset division. by Chunhua Yang (346871)

    Published 2025
    “…The slice-assisted super-reasoning algorithm is integrated to improve the recognition ability of YOLOv10 network for long-distance and small-target tea. The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. …”
  13. 1513

    Striking image. by Chunhua Yang (346871)

    Published 2025
    “…The slice-assisted super-reasoning algorithm is integrated to improve the recognition ability of YOLOv10 network for long-distance and small-target tea. The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. …”
  14. 1514

    Precision, recall, F1-Score curve. by Chunhua Yang (346871)

    Published 2025
    “…The slice-assisted super-reasoning algorithm is integrated to improve the recognition ability of YOLOv10 network for long-distance and small-target tea. The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. …”
  15. 1515

    Model comparison experimental results. by Chunhua Yang (346871)

    Published 2025
    “…The slice-assisted super-reasoning algorithm is integrated to improve the recognition ability of YOLOv10 network for long-distance and small-target tea. The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. …”
  16. 1516

    Slicing aided hyper inference algorithm. by Chunhua Yang (346871)

    Published 2025
    “…The slice-assisted super-reasoning algorithm is integrated to improve the recognition ability of YOLOv10 network for long-distance and small-target tea. The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. …”
  17. 1517

    Improved YOLOv10 network structure. by Chunhua Yang (346871)

    Published 2025
    “…The slice-assisted super-reasoning algorithm is integrated to improve the recognition ability of YOLOv10 network for long-distance and small-target tea. The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. …”
  18. 1518

    Loss function variation curve. by Chunhua Yang (346871)

    Published 2025
    “…The slice-assisted super-reasoning algorithm is integrated to improve the recognition ability of YOLOv10 network for long-distance and small-target tea. The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. …”
  19. 1519

    Sex and tissue-specificity of piRNA regulation in adult mice following perinatal lead (Pb) exposure by Bambarendage P. U. Perera (20190175)

    Published 2024
    “…Piwi-interacting RNAs (piRNAs) are small non-coding RNAs that associate with PIWIL proteins to induce DNA methylation. …”
  20. 1520

    Different model detection results comparison. by Chunhua Yang (346871)

    Published 2025
    “…The slice-assisted super-reasoning algorithm is integrated to improve the recognition ability of YOLOv10 network for long-distance and small-target tea. The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. …”