Recent advances in spin crossover research

Thermal spin transition (spin crossover), one of the most fascinating dynamic electronic structure phenomena occurring in coordination compounds of third row transition metal ions, mostly of iron(II), iron(III) and cobalt(II) with critical ligand field strengths competing with the spin pairing energy, has attracted increasing attention by many research groups. One of the reasons is the promising potential for practical applications. In this chapter we intend to cover essential recent work, primarily accomplished within the European research network on “Thermal and Optical Switching of Molecular Spin States (TOSS)”. New spin crossover compounds and their thermal spin transition behaviour, also under applied pressure, novel effects observed by irradiation and magnetic field, will be discussed. Progress in theoretical treatments of spin crossover phenomena, particularly cooperativity, will be briefly outlined. The chapter concludes with a summary of research highlights published by the partner laboratories of the TMR network TOSS.

Generalized methods and solvers for noise removal from piecewise constant signals. I. Background theory

Removing noise from piecewise constant (PWC) signals is a challenging signal processing problem arising in many practical contexts. For example, in exploration geosciences, noisy drill hole records need to be separated into stratigraphic zones, and in biophysics, jumps between molecular dwell states have to be extracted from noisy fluorescence microscopy signals. Many PWC denoising methods exist, including total variation regularization, mean shift clustering, stepwise jump placement, running medians, convex clustering shrinkage and bilateral filtering; conventional linear signal processing methods are fundamentally unsuited. This paper (part I, the first of two) shows that most of these methods are associated with a special case of a generalized functional, minimized to achieve PWC denoising. The minimizer can be obtained by diverse solver algorithms, including stepwise jump placement, convex programming, finite differences, iterated running medians, least angle regression, regularization path following and coordinate descent. In the second paper, part II, we introduce novel PWC denoising methods, and comparisons between these methods performed on synthetic and real signals, showing that the new understanding of the problem gained in part I leads to new methods that have a useful role to play.

Steps and bumps:precision extraction of discrete states of molecular machines

We report statistical time-series analysis tools providing improvements in the rapid, precision extraction of discrete state dynamics from time traces of experimental observations of molecular machines. By building physical knowledge and statistical innovations into analysis tools, we provide techniques for estimating discrete state transitions buried in highly correlated molecular noise. We demonstrate the effectiveness of our approach on simulated and real examples of steplike rotation of the bacterial flagellar motor and the F1-ATPase enzyme. We show that our method can clearly identify molecular steps, periodicities and cascaded processes that are too weak for existing algorithms to detect, and can do so much faster than existing algorithms. Our techniques represent a step in the direction toward automated analysis of high-sample-rate, molecular-machine dynamics. Modular, open-source software that implements these techniques is provided.