Aggregation of amyloid β peptides is known to be one of the main processes responsible for Alzheimer’s disease. The resulting dementia is believed to be due in part to the formation of potentially toxic oligomers. However, the study of such intermediates and the understanding of how they form are very challenging because they are heterogeneous and transient in nature. Unfortunately, few techniques can quantify, in real time, the proportion and the size of the different soluble species during the aggregation process. In a previous work (Deleanu et al. Anal. Chem. 2021, 93, 6523−6533), we showed the potential of Taylor dispersion analysis (TDA) in amyloid speciation during the aggregation process of Aβ (1−40) and Aβ (1−42). The current work aims at exploring in detail the aggregation of amyloid Aβ (1− 40):Aβ (1−42) peptide mixtures with different proportions of each peptide (1:0, 3:1, 1:1, 1:3, and 0:1) using TDA and atomic force microscopy (AFM). TDA allowed for monitoring the kinetics of the amyloid assembly and quantifying the transient intermediates. Complementarily, AFM allowed the formation of insoluble fibrils to be visualized. Together, the two techniques enabled us to study the influence of the peptide ratios on the kinetics and the formation of potentially toxic oligomeric species.

Wheat gluten includes two major proteins classes, gliadin (25–60 kg/mol) and glutenin polymers (100 to > 2,000 kg/mol) each comprising several polypeptides routinely identified by size-exclusion chromatography and electrophoresis. Gluten proteins are rich in glutamine (30%) and contain several repeated sequences, linking them to the wide class of intrinsically disordered protein (IDP). Here we showed that an ethanol/water (EtOH/W, 50/50, v/v) extract of an industrial gluten, comprising 1/3 of glutenin polymers and 2/3 of gliadin, underwent liquid-liquid phase separation (LLPS) below 14 °C, leading to two coexisting phases, respectively rich and poor in protein. As the quenching depth increased, proteins of lower and lower molecular weight joined the rich phase, akin to what would have been obtained for a polydisperse polymer sample. Within the rich phase the mass ratio of glutenin over gliadin decreased from 2.5 to 0.5 as the temperature dropped from 14 °C to −0.8 °C. Concomitantly the concentration in glutenin polymers increased up to 143 ± 6 g/L (at 9 °C) and then stopped to evolve, suggesting that the binodal line intersected the gelation line below this temperature. Applying the Flory-Huggins (FH) lattice model for each gluten protein classes, we demonstrated that their partitioning in the coexisting phases followed a same temperature dependency. However, some gliadin species joined the rich phase above their critical temperature. Here, specific interactions with the glutenin polymers through weak forces were exemplified. The study demonstrated the relevance of the Flory-Huggins (FH) lattice model in predicting phase behavior even when applied to complex protein mixtures.

In road environments, real-time knowledge of local weather conditions is an essential prerequisite for addressing the twin challenges of enhancing road safety and avoiding congestions. Currently, the main means of quantifying weather conditions along a road network requires the installation of meteorological stations. Such stations are costly and must be maintained; however, large numbers of cameras are already installed on the roadside. A new artificial intelligence method that uses road traffic cameras and a convolution neural network to detect weather conditions has, therefore, been proposed. It addresses a clearly defined set of constraints relating to the ability to operate in real-time and to classify the full spectrum of meteorological conditions and order them according to their intensity. The method can differentiate between five weather conditions such as normal (no precipitation), heavy rain, light rain, heavy fog and light fog. The deep-learning method’s training and testing phases were conducted using a new database called the Cerema-AWH (Adverse Weather Highway) database. After several optimisation steps, the proposed method obtained an accuracy of 0.99 for classification.

The amount and characteristics of plastic waste in the environment are closely monitored with the regular expansion of their collection to smaller debris sizes and new compartments, such as atmosphere, soil and even food. These data, subject to constant re-evaluation, are necessary to assess the threat that plastic particles pose. In this article, we take stock of the most recent measurements of their size distribution on the ocean surface. We confront the observed size distribution to the commonly invoked degradation modes of plastics and propose a scenario that accounts for the quite unusual features of the size distribution of floating debris. We conclude that delamination alone could be a major mechanism explaining the large number of small microplastics (<150 μm) in the ocean.

We use the impact of drops on a small solid target as a tool to investigate the behavior of viscoelastic fluids under extreme deformation rates. We study two classes of transient networks: semidilute solutions of supramolecular polymers and suspensions of spherical oil droplets reversibly linked by polymers. The two types of samples display very similar linear viscoelastic properties, which can be described with a Maxwell fluid model, but contrasting nonlinear properties due to different network structure. Upon impact, weakly viscoelastic samples exhibit a behavior qualitatively similar to that of Newtonian fluids: A smooth and regular sheet forms, expands, and then retracts. By contrast, for highly viscoelastic fluids, the thickness of the sheet is found to be very irregular, leading to instabilities and eventually formation of holes. We find that material rheological properties rule the onset of instabilities. We first provide a simple image analysis of the expanding sheets to determine the onset of instabilities. We then demonstrate that a Deborah number related to the shortest relaxation time associated to the sample structure following a high shear is the relevant parameter that controls the heterogeneities in the thickness of the sheet, eventually leading to the formation of holes. When the sheet tears-up, data suggest by contrast that the opening dynamics depends also on the expansion rate of the sheet.

Mucus is a biological gel protecting several tissues. Its key properties result from a crucial balance between solid-like and fluid-like behavior, ensured by the nonpermanent nature of the bonds between its macromolecular constituents. Our understanding of the micrometer-scale response of mucus to an applied stress is still rudimentary, although in living organisms, stresses acting on mucus are ubiquitous, from bacterial penetration to coughing and peristalsis. We show that under a modest applied stress, in the mechanical linear regime, the microscopic dynamics of pig gastric mucus transiently accelerate by up to 2 orders of magnitude. A simple model rationalizes this previously unrecognized fluidization mechanism stemming from elastic recoil following bond breaking and generalizes our findings to networks with reversible bonds.Mucus is a biological gel covering the surface of several tissues and ensuring key biological functions, including as a protective barrier against dehydration, pathogen penetration, or gastric acids. Mucus biological functioning requires a finely tuned balance between solid-like and fluid-like mechanical response, ensured by reversible bonds between mucins, the glycoproteins that form the gel. In living organisms, mucus is subject to various kinds of mechanical stresses, e.g., due to osmosis, bacterial penetration, coughing, and gastric peristalsis. However, our knowledge of the effects of stress on mucus is still rudimentary and mostly limited to macroscopic rheological measurements, with no insight into the relevant microscopic mechanisms. Here, we run mechanical tests simultaneously to measurements of the microscopic dynamics of pig gastric mucus. Strikingly, we find that a modest shear stress, within the macroscopic rheological linear regime, dramatically enhances mucus reorganization at the microscopic level, as signaled by a transient acceleration of the microscopic dynamics, by up to 2 orders of magnitude. We rationalize these findings by proposing a simple, yet general, model for the dynamics of physical gels under strain and validate its assumptions through numerical simulations of spring networks. These results shed light on the rearrangement dynamics of mucus at the microscopic scale, with potential implications in phenomena ranging from mucus clearance to bacterial and drug penetration.ASCII and Excel files for all the datasets shown in the figures of the main text and SI Appendix have been deposited in Zenodo (DOI: 10.5281/zenodo.5533877) (74).