Addressing the challenges of modeling the scattering from bottlebrush polymers in solution

Small‐angle scattering measurements of complex macromolecules in solution are used to establish relationships between chemical structure and conformational properties. Interpretation of the scattering data requires an inverse approach where a model is chosen and the simulated scattering intensity from that model is iterated to match the experimental scattering intensity. This raises challenges in the case where the model is an imperfect approximation of the underlying structure, or where there are significant correlations between model parameters. We examine three bottlebrush polymers (consisting of polynorbornene backbone and polystyrene side chains) in a good solvent using a model commonly applied to this class of polymers: the flexible cylinder model. Applying a series of constrained Monte‐Carlo Markov Chain analyses demonstrates the severity of the correlations between key parameters and the presence of multiple close minima in the goodness of fit space. We demonstrate that a shape‐agnostic model can fit the scattering with significantly reduced parameter correlations and less potential for complex, multimodal parameter spaces. We provide recommendations to improve the analysis of complex macromolecules in solution, highlighting the value of Bayesian methods. This approach provides richer information for understanding parameter sensitivity compared to methods which produce a single, best fit

The city dimension of the productivity growth puzzle: The relative role of structural change and within-sector slowdown

© The Author(s) (2018). Published by Oxford University Press. All rights reserved. Across OECD countries productivity growth has slowed, not just in recent years but over the past four decades: the so-called productivity puzzle. This paper examines the differing productivity growth paths of some 85 British cities since the beginning of the 1970s, and explores how far these paths reflect differences across cities in the pace and nature of structural change. We find that while northern cities led productivity growth over 1971-91 southern cities then led after 1991. However, at the same time, the rate of productivity growth slowed across almost all cities between these two periods. We find evidence of considerable structural convergence across cities and a general tendency for the degree of specialisation to fall. This then leads to a decomposition analysis which identifies the relative contribution of between-sector (structural change) and within-sector effects to city productivity growth. The analysis reveals that structural change - and especially the shift from manufacturing to services - has had a negative impact on productivity growth across all cities, but that within-sector productivity developments while positive and outweighing structural change effects, have also declined over the past 45 years, as well as varying across cities. These findings point to the need for further research on the causes of this slowdown in 'within-sector 'productivity growth and why those causes appear to differ from city to city. They also point to the need for a 'place-based' dimension to policies aimed at improving national productivity performance.This research for this article was undertaken as part of a project funded by the ESRC (ES/N006135/1) into Structural Transformation, Adaptability and City Economic Evolutions, as part of its Structural Transformations Programme. We are grateful to the ESRC for its support

Growing apart? Structural transformation and the uneven development of British cities

Structural change is now widely considered to be an important aspect of national economic growth. The issue is not only relevant at the macro-economic level, but also has a direct bearing on the growth of regions and cities. In this article, we examine the relationship between structural transformation and economic (output) growth across British cities over the last half-century. During this time, the British economy has gone through a series of extensive structural transformations, most notably a historical shift from an industrial to a post-industrial structure. But also within the dominant ‘post-industrial’ economy, some service activities have been growing at a faster rate and appear to be more dynamic than others. We show how the structural transformations in the national economy have played out quite differently across British cities, shaping to a considerable extent their divergent growth trajectories over the past five decades. At a broad level, it is possible to distinguish between a number of distinct growth groups of cities, and these also display significant differences in the extent and direction of structural change and reorientation. While differences in structural change have been important in shaping city growth paths, other ‘city-specific’ factors also appear to have exerted an influence, and thus require investigation. Despite the importance of structural change on the growth trajectories of British cities, the most comprehensive analysis was undertaken some 30 years ago (see Hausner, 1987). This article seeks to fill this lacuna in knowledge.This research was undertaken as part of a project funded by the ESRC (ES/N006135/1) into Structural Transformation, Adaptability and City Economic Evolutions, as part of its Urban Transformations Programme

Addressing the challenges of modeling the scattering from bottlebrush polymers in solution

Small‐angle scattering measurements of complex macromolecules in solution are used to establish relationships between chemical structure and conformational properties. Interpretation of the scattering data requires an inverse approach where a model is chosen and the simulated scattering intensity from that model is iterated to match the experimental scattering intensity. This raises challenges in the case where the model is an imperfect approximation of the underlying structure, or where there are significant correlations between model parameters. We examine three bottlebrush polymers (consisting of polynorbornene backbone and polystyrene side chains) in a good solvent using a model commonly applied to this class of polymers: the flexible cylinder model. Applying a series of constrained Monte‐Carlo Markov Chain analyses demonstrates the severity of the correlations between key parameters and the presence of multiple close minima in the goodness of fit space. We demonstrate that a shape‐agnostic model can fit the scattering with significantly reduced parameter correlations and less potential for complex, multimodal parameter spaces. We provide recommendations to improve the analysis of complex macromolecules in solution, highlighting the value of Bayesian methods. This approach provides richer information for understanding parameter sensitivity compared to methods which produce a single, best fit

Concentration Dependence of the Size and Symmetry of a Bottlebrush Polymer in a Good Solvent

Bottlebrush polymers consist of a linear backbone with densely grafted side chains which impact the rigidity of the molecule. The persistence length of the bottlebrush backbone in solution is influenced by both the intrinsic structure of the polymer and the local environment, such as the solvent quality and concentration. Increasing the concentration reduces the overall size of the molecule because of the reduction in backbone stiffness. In this study, we map out the size of a bottlebrush polymer as a function of concentration for a single backbone length. Small-angle neutron scattering measurements are conducted on a polynorbornene-based bottlebrush with polystyrene side chains in a good solvent. The data are fit using a model which provides both the long and short axis radius of gyration (R_(g,2) and R_(g,1), respectively), providing a measure for how the conformation changes as a function of concentration. At low concentrations, a highly anisotropic structure is observed (R_(g,2)/R_(g,1) ≈ 4), becoming more isotropic at higher concentrations (R_(g,2)/R_(g,1) ≈ 1.5). The concentration scaling for both R_(g,2) and the overall Rg is evaluated and compared with predictions in the literature. Coarse-grained molecular dynamics simulations were also conducted to probe the impact of concentration on bottlebrush conformation, showing qualitative agreement with the experimental results

Concentration Dependence of the Size and Symmetry of a Bottlebrush Polymer in a Good Solvent

Bottlebrush polymers consist of a linear backbone with densely grafted side chains which impact the rigidity of the molecule. The persistence length of the bottlebrush backbone in solution is influenced by both the intrinsic structure of the polymer and the local environment, such as the solvent quality and concentration. Increasing the concentration reduces the overall size of the molecule because of the reduction in backbone stiffness. In this study, we map out the size of a bottlebrush polymer as a function of concentration for a single backbone length. Small-angle neutron scattering measurements are conducted on a polynorbornene-based bottlebrush with polystyrene side chains in a good solvent. The data are fit using a model which provides both the long and short axis radius of gyration (R_(g,2) and R_(g,1), respectively), providing a measure for how the conformation changes as a function of concentration. At low concentrations, a highly anisotropic structure is observed (R_(g,2)/R_(g,1) ≈ 4), becoming more isotropic at higher concentrations (R_(g,2)/R_(g,1) ≈ 1.5). The concentration scaling for both R_(g,2) and the overall Rg is evaluated and compared with predictions in the literature. Coarse-grained molecular dynamics simulations were also conducted to probe the impact of concentration on bottlebrush conformation, showing qualitative agreement with the experimental results

Overutilization of magnetic resonance imaging in the diagnosis and treatment of moderate to severe osteoarthritis

Advanced imaging is a major driver of health care expenditures. Magnetic resonance imaging provides advantages over radiography because of its ability to visualize soft tissues within the knee joint. The clinical relevance of these findings in osteoarthritis, however, is not well understood. For example, MRI can detect meniscal tears, but these are frequent findings in patients with osteoarthritis, with no difference in prevalence among those with and without symptoms. In addition to concerns about excessive cost, it is possible that patients may undergo unnecessary procedures due to MRI findings. A randomized placebo-controlled trial showed no benefit of arthroscopy for osteoarthritis. Our goal was to examine how prevalent this practice is at this institution, and to examine the characteristics of physicians who ordered these MRIs. Our hypothesis is that many providers order MRI for evaluation of osteoarthritis before referring to an orthopedic surgeon, and that providers with higher levels of training are less likely to order these unnecessary MRIs

Estimation of Individual Micro Data from Aggregated Open Data

In this paper, we propose a method of estimating individual micro data from aggregated open data based on semi-supervised learning and conditional probability. Firstly, the proposed method collects aggregated open data and support data, which are related to the individual micro data to be estimated. Then, we perform the locality sensitive hashing (LSH) algorithm to find a subset of the support data that is similar to the aggregated open data and then classify them by using the Ensemble classification model, which is learned by semi-supervised learning. Finally, we use conditional probability to estimate the individual micro data by finding the most suitable record for the probability distribution of the individual micro data among the classification results. To evaluate the performance of the proposed method, we estimated the individual building data where the fire occurred using the aggregated fire open data. According to the experimental results, the micro data estimation performance of the proposed method is 59.41% on average in terms of accuracy.Comment: 7 page

A multi-step nucleation process determines the kinetics of prion-like domain phase separation

The nucleation mechanisms of biological protein phase separation are poorly understood. Here, the authors perform time-resolved SAXS experiments with the low-complexity domain (LCD) of hnRNPA1 and uncover multiple kinetic regimes on the micro- to millisecond timescale. Initially, individual proteins collapse. Nucleation then occurs via two steps distinguished by their protein cluster size distributions