论文标题
信息：收获，拥有并保持
Information: to Harvest, to Have and to Hold
论文作者
论文摘要
信噪比（SNRS）和香农 - 哈特利通道容量是指标，可帮助定义已知信息的丢失，同时通过嘈杂的通道传输数据。这些指标不能用于量化相反过程：收获新信息。相关函数和相关系数确实在从嘈杂来源收集新信息方面起着重要作用。然而，Bershad和Rockmore [1974]将其公式基于矛盾的真实空间和傅立叶空间的先验假设。随后将它们的制剂从字面上复制到电子显微镜的实用科学，在这些假设中，这些假设现在扭曲了冷冻EM中最质量的指标。近年来，Cryo-Em取得了巨大的成功[2017年Wiley Award;诺贝尔化学奖2017年]，成为揭示核糖体，病毒或电晕病毒尖峰等生物复合物结构的首选方法，在当前的Covid-19大流行期间至关重要。这些早期的误解现在干扰了独立获得的结果的客观比较。我们发现，这些问题的根源大大提前了1970年代的出版物，并且已经是原始SNR定义固有的。我们在这里提出了新的指标，以评估实验中收获的信息的量，该信息以位测量。这些新指标评估对象上收集的信息总量以及该对象内的信息密度分布。可以在收集，处理，压缩或比较数据的任何地方应用新的指标。例如，我们比较了两个最近发表的SARS-COV-2尖峰蛋白的结构。我们还介绍了许多科学中的换能器质量评估的新指标，包括：冷冻EM，生物医学成像，显微镜，信号处理，摄影，层析成像等。
Signal-to-Noise Ratios (SNRs) and the Shannon-Hartley channel capacity are metrics that help define the loss of known information while transferring data through a noisy channel. These metrics cannot be used for quantifying the opposite process: the harvesting of new information. Correlation functions and correlation coefficients do play an important role in collecting new information from noisy sources. However, Bershad and Rockmore [1974] based their formulas on contradictory a priori assumptions in Real-space and in Fourier-space. Their formulations were subsequently copied literally to the practical science of electron microscopy, where those a priori assumptions now distort most quality metrics in Cryo-EM. Cryo-EM became a great success in recent years [Wiley Award 2017; Nobel prize for Chemistry 2017] and became the method of choice for revealing structures of biological complexes like ribosomes, viruses, or corona-virus spikes, vitally important during the current COVID-19 pandemic. Those early misconceptions now interfere with the objective comparison of independently obtained results. We found that the roots of these problems significantly pre-date those 1970s publications and were already inherent in the original SNR definitions. We here propose novel metrics to assess the amount of information harvested in an experiment, information which is measured in bits. These new metrics assess the total amount of information collected on an object, as well as the information density distribution within that object. The new metrics can be applied everywhere where data is collected, processed, compressed, or compared. As an example, we compare the structures of two recently published SARS-CoV-2 spike proteins. We also introduce new metrics for transducer-quality assessment in many sciences including: cryo-EM, biomedical imaging, microscopy, signal processing, photography, tomography, etc.
