Structure Determination from X-ray Scattering: Parameter extraction from cosmology for nanobiology

传统的医学检查使用对X射线的吸收来获得有关生物组织结构的信息。然而，对这一过程中散射的X射线的测量可以产生更丰富的结构信息。现在可以在非常小的尺度上--微米的一小部分--表征分子结构，这现在可以在短时间尺度上重复完成，短到微秒或毫秒。这类实验在分析方面提出了许多挑战。一个问题是如何首先描述这些结构。在典型情况下，表征将是复杂的，相当多的成分需要根据X射线数据来确定。这在计算上可能非常苛刻，并且实时处理通常是不可行的。只有在实验结束后，才能确定实验结果。一个合理的目标是拥有一种足够快的分析方法，使实时处理成为可能，从而为立即获取更多数据开辟了可能性。人们当然可以设想许多情况，在这些情况下，操纵样本或改变条件可能是一个巨大的优势。第二个挑战是，所收集的数据质量可能相当差，因为与背景“噪声”相比，信号的质量可能相当小；事实上，在某些情况下，人们可能希望减少对X射线的暴露，以避免损坏，在这种情况下，数据将变得更加嘈杂。在这种情况下，找出微弱的信号可能很困难。必须知道可以从数据中可靠地提取出多小的信号，因为这可能会允许较小的X射线暴露，这对生物样本可能是避免损坏的关键。最后，要分析的数据量可能会导致瓶颈。在概念上，类似的问题在天文学中也会遇到，通常是通过牢固植根于贝叶斯概率理论的方法来解决的。特别是在宇宙学方面，研究人员努力从他们的数据中提取尽可能多的信息，这些数据在许多情况下是巨大的、非常噪声的，并且可能依赖于相对大量的模型参数。这一领域中使用的几种技术可以应用于材料表征问题。这些方法包括非常快速地找到：最合适的解决方案；解决方案的不确定性程度；解决方案是否唯一。除了常用的工具外，PI还拥有一种名为MOPED的专利算法，这种算法确实可以极快地完成这类任务。对于一些问题，已经实现了几个数量级的加速。如果MOPE适用于这些问题，那么实时分析可能是我们力所能及的。

Conventional medical examinations use the absorption of X-rays to yield information about the structure of biological tissues. The measurement of X-rays scattered in this process can however yield a richer wealth of structural information. Characterising molecular structures on very small scales - fractions of a micrometre - are now possible, and this can now be done repeatedly on short timescales, as short as micro- or milliseconds. This sort of experiment presents a number of challenges in analysis. One is how to characterise the structures in the first place. In typical cases the characterisation will be complex, with a fairly large numbers of components to be determined from the X-ray data. This can be very demanding computationally, and real-time processing is not generally feasible. The results of the experiment are then only determined after it has finished. A reasonable goal would be to have a fast enough analysis method that real-time processing is possible, opening up the possibility of taking further data immediately. One can certainly envisage many situations where manipulation of a sample or a change in conditions could be a great advantage. A second challenge is that the data collected may be rather poor quality in the signal may be rather small in comparison with background 'noise'; indeed in some circumstances one may wish to reduce the exposure to X-rays to avoid damage, in which case the data will become more noisy. Picking out faint signals in these circumstances can be difficult. It is essential to know how small a signal can be reliably extracted from the data, as this might allow smaller X-ray exposure which for biological samples may be critical to avoid damage. Finally, the quantity of data to be analysed can lead to a bottleneck. Conceptually similar problems are encountered in Astronomy, and are generally tackled via methods which are firmly rooted in Bayesian probability theory. In cosmology in particular, researchers strive to extract as much information as possible from their data, which in many cases are substantial in size, very noisy, and may depend on a relatively large number of model parameters. Several techniques used in this field may be applied to the materials characterisation problem. These include very rapid methods to find: the best-fitting solution; how uncertain the solution is; whether the solution is unique or not. In addition to commonly-applied tools, the PI holds a patented algorithm called MOPED, which can do this sort of task extremely rapidly indeed. For some problems, acceleration by several orders of magnitude has been achieved. If MOPED is applicable to these problems, then real-time analysis could be within our reach.