Showing 1 - 20 results of 13,253 for search '(( significant chip based ) OR ( significant ((non decrease) OR (nn decrease)) ))', query time: 0.62s Refine Results
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    Data_Sheet_1_On-Chip TaOx-Based Non-volatile Resistive Memory for in vitro Neurointerfaces.pdf by Maksim Zhuk (8495154)

    Published 2020
    “…<p>The development of highly integrated electrophysiological devices working in direct contact with living neuron tissue opens new exciting prospects in the fields of neurophysiology and medicine, but imposes tight requirements on the power dissipated by electronics. On-chip preprocessing of neuronal signals can substantially decrease the power dissipated by external data interfaces, and the addition of embedded non-volatile memory would significantly improve the performance of a co-processor in real-time processing of the incoming information stream from the neuron tissue. …”
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    Traits at cellular, leaf, and whole-plant scales which were significantly different [increased (↑) or decreased (↓)] or non-significant between tolerant and sensitive cultivars in the N-deficient treatment. by Ranjeeta Adhikari (9905958)

    Published 2023
    “…<p>Traits at cellular, leaf, and whole-plant scales which were significantly different [increased (↑) or decreased (↓)] or non-significant between tolerant and sensitive cultivars in the N-deficient treatment.…”
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    ChIP flowchart. by Kate Curtis (3799945)

    Published 2021
    Subjects:
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    Depleted antibody secreting cells did not result in decreased donor specific antibody in non-sensitized animals. by Natalie M. Bath (6316517)

    Published 2022
    “…(A) T (CD3<sup>+</sup>) and (B) B (CD45R<sup>+</sup>) cell flow crossmatch was performed in non-sensitized animals. IgG1<sup>+</sup> in the T cell flow crossmatch was the only DSA that BLyS<sup>-/-</sup> significantly decreased compared to APRIL<sup>-/-</sup> (p<0.03). …”
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    CHIP-STS experimental results. by Zhen Luo (281825)

    Published 2025
    “…The proposed method achieves F1 scores of 65.26%, 80.31%, and 86.73% on the CMeEE-V2, IMCS-V2-NER, and CHIP-STS datasets, respectively, outperforming other models and demonstrating significant improvements in medical entity recognition accuracy and multiple evaluation metrics.…”
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