Structural sensitivity of an asphalt pavement to asphalt binder content and mix design method

The goal of this study was to analyze the structural sensitivity of a flexible pavement, whose asphalt layers underwent variations in its mechanical properties due to the asphalt binder content and the mix design method Marshall and Superpave. A variation of ±0.5% within the optimum asphalt binder contents was used (service tolerance) considering possible permissible variations in the asphalt binder content during the asphalt mixture manufacturing process. The values of resilient modulus and indirect tensile strength (Brazilian test) of the resulting asphalt mixtures were applied to the reference pavement structure analyzed by the me-PADS software. The results show that the variations in the asphalt binder content and the asphalt mixtures design method influence the mechanical properties and corresponding structural responses of the pavement investigated: the asphalt layers designed by the Marshall method presented greater sensitivity to the variation in asphalt binder content, which may constitute a technical differential of asphalt mixtures designed by the Superpave method.

Coherence transfer in two-pulse double quantum (DQ) and five-pulse double- quantum modulation (DQM) sequences in EPR: Orientation selectivity, structural sensitivity and distance measurements

Double-quantum (DQ) coherence transfers in two-pulse DQ and five-pulse DQM (double quantum modulation) EPR pulse sequences, utilized for orientation selectivity and distance measurements in biological systems using nitroxide biradicals, are investigated. Analytical expressions, along with numerical algorithms, for EPR signals are given in full details. Please see manuscript .pdf for full abstract.

A base measure of precision for protein stability predictors: structural sensitivity

Background Prediction of the change in fold stability (ΔΔG) of a protein upon mutation is of major importance to protein engineering and screening of disease-causing variants. Many prediction methods can use 3D structural information to predict ΔΔG. While the performance of these methods has been extensively studied, a new problem has arisen due to the abundance of crystal structures: How precise are these methods in terms of structure input used, which structure should be used, and how much does it matter? Thus, there is a need to quantify the structural sensitivity of protein stability prediction methods. Results We computed the structural sensitivity of six widely-used prediction methods by use of saturated computational mutagenesis on a diverse set of 87 structures of 25 proteins. Our results show that structural sensitivity varies massively and surprisingly falls into two very distinct groups, with methods that take detailed account of the local environment showing a sensitivity of ~ 0.6 to 0.8 kcal/mol, whereas machine-learning methods display much lower sensitivity (~ 0.1 kcal/mol). We also observe that the precision correlates with the accuracy for mutation-type-balanced data sets but not generally reported accuracy of the methods, indicating the importance of mutation-type balance in both contexts. Conclusions The structural sensitivity of stability prediction methods varies greatly and is caused mainly by the models and less by the actual protein structural differences. As a new recommended standard, we therefore suggest that ΔΔG values are evaluated on three protein structures when available and the associated standard deviation reported, to emphasize not just the accuracy but also the precision of the method in a specific study. Our observation that machine-learning methods deemphasize structure may indicate that folded wild-type structures alone, without the folded mutant and unfolded structures, only add modest value for assessing protein stability effects, and that side-chain-sensitive methods overstate the significance of the folded wild-type structure.

Superchiral near fields detect virus structure

Optical spectroscopy can be used to quickly characterise the structural properties of individual molecules. However, it cannot be applied to biological assemblies because light is generally blind to the spatial distribution of the component molecules. This insensitivity arises from the mismatch in length scales between the assemblies (a few tens of nm) and the wavelength of light required to excite chromophores (≥150 nm). Consequently, with conventional spectroscopy, ordered assemblies, such as the icosahedral capsids of viruses, appear to be indistinguishable isotropic spherical objects. This limits potential routes to rapid high-throughput portable detection appropriate for point-of-care diagnostics. Here, we demonstrate that chiral electromagnetic (EM) near fields, which have both enhanced chiral asymmetry (referred to as superchirality) and subwavelength spatial localisation (∼10 nm), can detect the icosahedral structure of virus capsids. Thus, they can detect both the presence and relative orientation of a bound virus capsid. To illustrate the potential uses of the exquisite structural sensitivity of subwavelength superchiral fields, we have used them to successfully detect virus particles in the complex milieu of blood serum.

Identifying the sources of structural sensitivity in partially specified biological models

Biological systems are characterised by a high degree of uncertainty and complexity, which implies that exact mathematical equations to describe biological processes cannot generally be justified. Moreover, models can exhibit sensitivity to the precise formulations of their component functions—a property known as structural sensitivity. Structural sensitivity can be revealed and quantified by considering partially specified models with uncertain functions, but this goes beyond well-established, parameter-based sensitivity analysis, and currently presents a mathematical challenge. Here we build upon previous work in this direction by addressing the crucial question of identifying the processes which act as the major sources of model uncertainty and those which are less influential. To achieve this goal, we introduce two related concepts: (1) the gradient of structural sensitivity, accounting for errors made in specifying unknown functions, and (2) the partial degree of sensitivity with respect to each function, a global measure of the uncertainty due to possible variation of the given function while the others are kept fixed. We propose an iterative framework of experiments and analysis to inform a heuristic reduction of structural sensitivity in a model. To demonstrate the framework introduced, we investigate the sources of structural sensitivity in a tritrophic food chain model.