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

传统的医学检查利用x射线的吸收来获得有关生物组织结构的信息。然而，在这个过程中散射的x射线的测量可以产生更丰富的结构信息。在非常小的尺度上——微米的几分之一——表征分子结构现在是可能的，而且现在可以在短时间内重复完成，短到微或毫秒。这类实验在分析中提出了许多挑战。首先是如何描述这些结构。在典型情况下，表征将是复杂的，有相当多的成分需要从x射线数据中确定。这在计算上要求很高，而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，从而使立即获取更多数据成为可能。人们当然可以设想许多情况下，对样品的操作或条件的改变可能是一个很大的优势。第二个挑战是，收集到的数据质量可能相当差，与背景“噪声”相比，信号可能相当小；的确，在某些情况下，人们可能希望减少x射线照射以避免损坏，在这种情况下，数据将变得更嘈杂。在这种情况下，找出微弱的信号可能很困难。必须知道从数据中可靠提取的信号有多小，因为这可能允许更小的x射线暴露，这对于生物样品来说可能是避免损害的关键。最后，要分析的数据量可能会导致瓶颈。在天文学中也会遇到概念上类似的问题，并且通常通过牢牢扎根于贝叶斯概率论的方法来解决。特别是在宇宙学中，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规模很大，非常嘈杂，并且可能依赖于相对大量的模型参数。该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：快速找到最合适的解决方案；解决方案的不确定性；解是否唯一。除了常用的工具外，PI还拥有一种名为MOPED的专利算法，它可以非常快速地完成这类任务。对于某些问题，已经实现了几个数量级的加速。如果mopd适用于这些问题，那么实时分析就可以实现。

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.