Time-resolved velocity map imaging of methyl elimination from photoexcited anisole

To date, H-atom elimination from heteroaromatic molecules following UV excitation has been extensively studied, with the focus on key biological molecules such as chromophores of DNA bases and amino acids. Extending these studies to look at elimination of other non-hydride photoproducts is essential in creating a more complete picture of the photochemistry of these biomolecules in the gas-phase. To this effect, CH3 elimination in anisole has been studied using time resolved velocity map imaging (TR-VMI) for the first time, providing both time and energy information on the dynamics following photoexcitation at 200 nm. The extra dimension of energy afforded by these measurements has enabled us to address the role of πσ* states in the excited state dynamics of anisole as compared to the hydride counterpart (phenol), providing strong evidence to suggest that only CH3 fragments eliminated with high kinetic energy are due to direct dissociation involving a 1πσ* state. These measurements also suggest that indirect mechanisms such as statistical unimolecular decay could be contributing to the dynamics at much longer times

Ultrasound as a technology of reassurance? How pregnant women and health care professionals articulate ultrasound reassurance and its limitations

The premise that ultrasound technologies provide reassurance for pregnant women is well‐rehearsed. However, there has been little research about how this reassurance is articulated and understood by both expectant mothers and health care professionals. In this article, we draw on two qualitative UK studies to explore the salience of ultrasound reassurance to women's pregnancy experiences whilst highlighting issues around articulation and silence. Specifically, we capture how expectant parents express a general need for reassurance and how visualisation and the conduct of professionals have a crucial role to play in accomplishing a sense of reassurance. We also explore how professionals have ambiguities about the relationship between ultrasound and reassurance, and how they subsequently articulate reassurance to expectant mothers. By bringing two studies together, we take a broad perspectival view of how gaps and silences within the discourse of ultrasound reassurance leave the claims made for ultrasound as a technology of reassurance unchallenged. Finally, we explore the implications this can have for women's experiences of pregnancy and health care professionals’ practices

Classifying Four-Body Convex Central Configurations

We classify the full set of convex central configurations in the Newtonian four-body problem. Particular attention is given to configurations possessing some type of symmetry or defining geometric property. Special cases considered include kite, trapezoidal, co-circular, equidiagonal, orthodiagonal, and bisecting-diagonal configurations. Good coordinates for describing the set are established. We use them to prove that the set of four-body convex central configurations with positive masses is three-dimensional, a graph over a domain $D$ that is the union of elementary regions in $\mathbb{R}^{+^3}$.Comment: 28 pages, 14 figure

Divide-and-Conquer Monte Carlo Fusion

Combining several (sample approximations of) distributions, which we term sub-posteriors, into a single distribution proportional to their product, is a common challenge. For instance, in distributed `big data' problems, or when working under multi-party privacy constraints. Many existing approaches resort to approximating the individual sub-posteriors for practical necessity, then representing the resulting approximate posterior. The quality of the posterior approximation for these approaches is poor when the sub-posteriors fall out-with a narrow range of distributional form. Recently, a Fusion approach has been proposed which finds a direct and exact Monte Carlo approximation of the posterior (as opposed to the sub-posteriors), circumventing the drawbacks of approximate approaches. Unfortunately, existing Fusion approaches have a number of computational limitations, particularly when unifying a large number of sub-posteriors. In this paper, we generalise the theory underpinning existing Fusion approaches, and embed the resulting methodology within a recursive divide-and-conquer sequential Monte Carlo paradigm. This ultimately leads to a competitive Fusion approach, which is robust to increasing numbers of sub-posteriors.Comment: 24 pages, 8 figure

Towards Understanding Photodegradation Pathways in Lignins:The Role of Intramolecular Hydrogen Bonding in Excited States

The photoinduced dynamics of the lignin building blocks syringol, guaiacol, and phenol were studied using time-resolved ion yield spectroscopy and velocity map ion imaging. Following irradiation of syringol and guaiacol with a broad-band femtosecond ultraviolet laser pulse, a coherent superposition of out-of-plane OH torsion and/or OMe torsion/flapping motions is created in the first excited 1ππ* (S1) state, resulting in a vibrational wavepacket, which is probed by virtue of a dramatic nonplanar → planar geometry change upon photoionization from S1 to the ground state of the cation (D0). Any similar quantum beat pattern is absent in phenol. In syringol, the nonplanar geometry in S1 is pronounced enough to reduce the degree of intramolecular H bonding (between OH and OMe groups), enabling H atom elimination from the OH group. For guaiacol, H bonding is preserved after excitation, despite the nonplanar geometry in S1, and prevents O–H bond fission. This behavior affects the propensities for forming undesired phenoxyl radical sites in these three lignin chromophores and provides important insight into their relative “photostabilities” within the larger biopolymer

Bayesian inference for high-dimensional discrete-time epidemic models: spatial dynamics of the UK COVID-19 outbreak

In the event of a disease outbreak emergency, such as COVID-19, the ability to construct detailed stochastic models of infection spread is key to determining crucial policy-relevant metrics such as the reproduction number, true prevalence of infection, and the contribution of population characteristics to transmission. In particular, the interaction between space and human mobility is key to prioritising outbreak control resources to appropriate areas of the country. Model-based epidemiological intelligence must therefore be provided in a timely fashion so that resources can be adapted to a changing disease landscape quickly. The utility of these models is reliant on fast and accurate parameter inference, with the ability to account for large amount of censored data to ensure estimation is unbiased. Yet methods to fit detailed spatial epidemic models to national-level population sizes currently do not exist due to the difficulty of marginalising over the censored data. In this paper we develop a Bayesian data-augmentation method which operates on a stochastic spatial metapopulation SEIR state-transition model, using model-constrained Metropolis-Hastings samplers to improve the efficiency of an MCMC algorithm. Coupling this method with state-of-the-art GPU acceleration enabled us to provide nightly analyses of the UK COVID-19 outbreak, with timely information made available for disease nowcasting and forecasting purposes

Quasi-stationary Monte Carlo and the ScaLE Algorithm

This paper introduces a class of Monte Carlo algorithms which are based upon simulating a Markov process whose quasi-stationary distribution coincides with a distribution of interest. This differs fundamentally from, say, current Markov chain Monte Carlo methods which simulate a Markov chain whose stationary distribution is the target. We show how to approximate distributions of interest by carefully combining sequential Monte Carlo methods with methodology for the exact simulation of diffusions. The methodology introduced here is particularly promising in that it is applicable to the same class of problems as gradient based Markov chain Monte Carlo algorithms but entirely circumvents the need to conduct Metropolis-Hastings type accept/reject steps whilst retaining exactness: the paper gives theoretical guarantees ensuring the algorithm has the correct limiting target distribution. Furthermore, this methodology is highly amenable to big data problems. By employing a modification to existing naıve sub-sampling and control variate techniques it is possible to obtain an algorithm which is still exact but has sub-linear iterative cost as a function of data size