Application of the sparse density principle: a statistical evaluation of the phase ambiguity in the single-wavelength anomalous scattering method (SAS)

Author(s):  
P. Verwer ◽  
R. B. G. Ravelli ◽  
H. Krabbendam ◽  
J. Kroon

AbstractThe method of single-wavelength anomalous scattering (SAS) allows the determination of structure-factor phases apart from a two-fold phase ambiguity. A statistical procedure is described, based on the joint probability distribution of three structure-factors, in which

Author(s):  
Carmelo Giacovazzo

The title of this chapter may seem a little strange; it relates Fourier syntheses, an algebraic method for calculating electron densities, to the joint probability distribution functions of structure factors, which are devoted to the probabilistic estimate of s.i.s and s.s.s. We will see that the two topics are strictly related, and that optimization of the Fourier syntheses requires previous knowledge and the use of joint probability distributions. The distributions used in Chapters 4 to 6 are able to estimate s.i. or s.s. by exploiting the information contained in the experimental diffraction moduli of the target structure (the structure one wants to phase). An important tool for such distributions are the theories of neighbourhoods and of representations, which allow us to arrange, for each invariant or seminvariant Φ, the set of amplitudes in a sequence of shells, each contained within the subsequent shell, with the property that any s.i. or s.s. may be estimated via the magnitudes constituting any shell. The resulting conditional distributions were of the type, . . . P(Φ| {R}), (7.1) . . . where {R} represents the chosen phasing shell for the observed magnitudes. The more information contained within the set of observed moduli {R}, the better will be the Φ estimate. By definition, conditional distributions (7.1) cannot change during the phasing process because prior information (i.e. the observed moduli) does not change; equation (7.1) maintains the same identical algebraic form. However, during any phasing process, various model structures progressively become available, with different degrees of correlation with the target structure. Such models are a source of supplementary information (e.g. the current model phases) which, in principle, can be exploited during the phasing procedure. If this observation is accepted, the method of joint probability distribution, as described so far, should be suitably modified. In a symbolic way, we should look for deriving conditional distributions . . . P (Φ| {R}, {Rp}) , (7.2) . . . rather than (7.1), where {Rp} represents a suitable subset of the amplitudes of the model structure factors. Such an approach modifies the traditional phasing strategy described in the preceding chapters; indeed, the set {Rp} will change during the phasing process in conjunction with the model changes, which will continuously modify the probabilities (7.2).


2014 ◽  
Vol 70 (a1) ◽  
pp. C571-C571
Author(s):  
Nicholas Sauter ◽  
Aaron Brewster ◽  
Johan Hattne ◽  
Muhamed Amin ◽  
Jan Kern ◽  
...  

Femtosecond-scale XFEL pulses can produce diffraction free from radiation damage, under functional physiological conditions where reaction dynamics can be studied for systems such as photosystem II. However, it has been extremely difficult to derive accurate structure factors from the data since every shot is a still exposure from a distinct specimen. Accuracy can be improved by software methods implemented in the program cctbx.xfel, including optimal indexing and retention of data from multiple lattices, and separate determination of the resolution cutoff for individual lattices. Various techniques can produce well-conforming descriptions of the Bragg spot shape and crystal mosaicity, enabled in part by sub-pixel characterization of the detector geometry. By carefully discriminating between image pixels known to contain diffraction signal and the surrounding pixels containing only background noise, and by extending postrefinement techniques that lead to a better crystal orientation, we derive accurate structure factors with substantially fewer crystal specimen exposures. It is hoped that these developments will make it easier to measure small structure factor differences, such as those from anomalous scattering that will enable the de novo determination of macromolecular structure.


2017 ◽  
Vol 73 (3) ◽  
pp. 218-226 ◽  
Author(s):  
Maria Cristina Burla ◽  
Giovanni Luca Cascarano ◽  
Carmelo Giacovazzo ◽  
Giampiero Polidori

This study clarifies why, in the phantom derivative (PhD) approach, randomly created structures can help in refining phases obtained by other methods. For this purpose the joint probability distribution of target, model, ancil and phantom derivative structure factors and its conditional distributions have been studied. Since PhD may usenphantom derivatives, withn≥ 1, a more general distribution taking into account all the ancil and derivative structure factors has been considered, from which the conditional distribution of the target phase has been derived. The corresponding conclusive formula contains two components. The first is the classical Srinivasan & Ramachandran term, relating the phases of the target structure with the model phases. The second arises from the combination of two correlations: that between model and derivative (the first is a component of the second) and that between derivative and target. The second component mathematically codifies the information on the target phase arising from model and derivative electron-density maps. The result is new, and explains why a random structure, uncorrelated with the target structure, adds useful information on the target phases, provided a model structure is known. Some experimental tests aimed at checking if the second component really provides information on φ (the target phase) were performed; the favourable results confirm the correctness of the theoretical calculations and of the corresponding analysis.


1997 ◽  
Vol 15 (8) ◽  
pp. 1057-1066 ◽  
Author(s):  
K. M. Gierens ◽  
U. Schumann ◽  
H. G. J. Smit ◽  
M. Helten ◽  
G. Zängl

Abstract. Humidity and temperature fluctuations at pressure levels between 166 and 290 hPa on the grid scale of general circulation models for a region covered by the routes of airliners, mainly over the Atlantic, have been determined by evaluation of the data obtained with almost 2000 flights within the MOZAIC programme. It is found that the distributions of the fluctuations cannot be modelled by Gaussian distributions, because large fluctuations appear with a relatively high frequency. Lorentz distributions were used for the analytical representation of the fluctuation distributions. From these a joint probability distribution has been derived for simultaneous temperature and humidity fluctuations. This function together with the criteria for the formation and persistence of contrails are used to derive the maximum possible fractional coverage of persistent contrails in a grid cell of a GCM. This can be employed in a statistical formulation of contrail appearance in a climate model.


1999 ◽  
Vol 55 (2) ◽  
pp. 322-331 ◽  
Author(s):  
Carmelo Giacovazzo ◽  
Dritan Siliqi ◽  
Angela Altomare ◽  
Giovanni Luca Cascarano ◽  
Rosanna Rizzi ◽  
...  

The joint probability distribution function method has been developed in P1¯ for reflections with rational indices. The positional atomic parameters are considered to be the primitive random variables, uniformly distributed in the interval (0, 1), while the reflection indices are kept fixed. Owing to the rationality of the indices, distributions like P(F p 1 , F p 2 ) are found to be useful for phasing purposes, where p 1 and p 2 are any pair of vectorial indices. A variety of conditional distributions like P(|F p 1 | | |F p 2 |), P(|F p 1 | |F p 2 ), P(\varphi_{{\bf p}_1}|\,|F_{{\bf p}_1}|, F_{{\bf p}_2}) are derived, which are able to estimate the modulus and phase of F p 1 given the modulus and/or phase of F p 2 . The method has been generalized to handle the joint probability distribution of any set of structure factors, i.e. the distributions P(F 1, F 2,…, F n+1), P(|F 1| |F 2,…, F n+1) and P(\varphi1| |F|1, F 2,…, F_{n+1}) have been obtained. Some practical tests prove the efficiency of the method.


Sign in / Sign up

Export Citation Format

Share Document