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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
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The Nursing Code of Ethics sets the ethical benchmark for the profession, and guides nurses in ethical analysis and decision making at the societal, organizational, and clinical levels. The code encompasses showing compassion and respect for the patient, their families, and communities in all circumstances while committing to providing patient-centered care. In addition, the code states that nurses must advocate for the patient by defending a cause or recommendation to protect their rights,...
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A reversible chemical reaction represents a chemical process that proceeds in both forward (left to right) and reverse (right to left) directions. When the rates of the forward and reverse reactions are equal, the concentrations of the reactant and product species remain constant over time and the system is at equilibrium. A special double arrow is used to emphasize the reversible nature of the reaction. The relative concentrations of reactants and products in equilibrium systems vary greatly;...
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Synaptic integration mainly includes the summation of graded potentials. Graded potentials, regardless of their type, cause subtle alterations in membrane voltage, resulting in either depolarization or hyperpolarization. These incremental changes, when combined or summed, can propel the neuron toward its threshold. Consider, for example, a membrane experiencing a +15 mV shift, causing it to depolarize from -70 mV to -55 mV. In this scenario, graded potentials govern the membrane's ability to...
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Focal Ca2+ Transient Detection in Smooth Muscle
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编码事件焦点堆用于在动态场景中持续重定位.

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    此摘要是机器生成的。

    这项研究引入了一种新的编码事件焦点堆方法,用于捕获后重新聚焦图像. 它有效地处理动态场景的运动和深度变化,超过现有的技术.

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    科学领域:

    • 计算机视觉 计算机视觉
    • 计算机摄影的使用
    • 图像处理 图像处理

    背景情况:

    • 传统相机在动态场景中由于镜头的限制而难以聚焦.
    • 现有的捕捉后重定位方法,如深度学习和光场摄像头,在时间一致性或空间分辨率方面存在问题.

    研究的目的:

    • 引入一种新的方法来捕捉和重定位动态场景.
    • 克服当前重定焦技术在时间一致性和空间分辨率方面的局限性.

    主要方法:

    • 在调制焦点扫描过程中使用事件流开发了一个编码的事件焦点堆.
    • 同时捕获运动和深度信息.
    • 启用了在任意焦距上重新聚焦的生成.

    主要成果:

    • 与最先进的方法相比,展示了优越的重定位能力.
    • 在动态场景中展示了有效性,具有复杂的运动和深度变化.
    • 在合成和现实数据集上验证了性能.

    结论:

    • 编码事件焦点堆是先进图像重定焦的有希望的方法.
    • 这种技术为动态场景提供了更好的性能,增强了时间一致性和空间分辨率.