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Bacteria experience substantial physical forces in their natural environment including forces caused by osmotic pressure, growth in constrained spaces, and fluid shear. The cell envelope is the primary load-carrying structure of bacteria, but the mechanical properties of the cell envelope are poorly understood; reports of Young’s modulus of the cell envelope of E. coli are widely range from 2 MPa to 18 MPa. We have developed a microfluidic system to apply mechanical loads to hundreds of bacteria at once and demonstrated the utility of the approach for evaluating whole-cell stiffness. Here we extend this technique to determine Young’s modulus of the cell envelope of E. coli and of the pathogens V. cholerae and S. aureus. An optimization-based inverse finite element analysis was used to determine the cell envelope Young’s modulus from observed deformations. The Young’s modulus of the cell envelope was 2.06±0.04 MPa for E. coli, 0.84±0.02 MPa for E. coli treated with a chemical known to reduce cell stiffness, 0.12±0.03 MPa for V. cholerae, and 1.52±0.06 MPa for S. aureus (mean ± SD). The microfluidic approach allows examining hundreds of cells at once and is readily applied to Gram-negative and Gram-positive organisms as well as rod-shaped and cocci cells, allowing further examination of the structural causes of differences in cell envelope Young's modulus among bacteria species and strains. 

In-context learning is the ability of a pretrained model to adapt to novel and diverse downstream tasks by conditioning on prompt examples, without optimizing any parameters. While large language models have demonstrated this ability, how in-context learning could be performed over graphs is unexplored. In this paper, we develop Pretraining Over Diverse In-Context Graph Systems (PRODIGY), the first pretraining framework that enables in-context learning over graphs. The key idea of our framework is to formulate in-context learning over graphs with a novel prompt graph representation, which connects prompt examples and queries. We then propose a graph neural network architecture over the prompt graph and a corresponding family of in-context pretraining objectives. With PRODIGY, the pre- trained model can directly perform novel downstream classification tasks on unseen graphs via in-context learning. We provide empirical evidence of the effectiveness of our framework by showcasing its strong in-context learning performance on tasks involving citation networks and knowledge graphs. Our approach outperforms the in-context learning accuracy of contrastive pretraining baselines with hard-coded adaptation by 18% on average across all setups. Moreover, it also outperforms standard finetuning with limited data by 33% on average with in-context learning. 

A novel castor oil/water/ethanol Pickering emulsion, stabilized by magnetic nanoparticles (NPs), was developed to allow on-demand demulsification by an external magnetic field for the extraction of ethanol from aqueous solution using the castor oil. The emulsion was stabilized by Fe3O4-coated cellulose nanocrystals (CNC@Fe3O4) and lignin-coated Fe3O4 NPs (lignin@Fe3O4). The stability of the emulsions was investigated at various castor oil to ethanol-water ratios (50/50 and 70/30), various NP concentrations, and ethanol concentrations in the aqueous phase. The magnetically controlled demulsification ability of the emulsions was investigated by using a permanent magnet. The results showed that the 70/30 emulsions were more stable than the 50/50 emulsions for all the ethanol concentrations. Moreover, increasing the NP concentration increased the emulsion stability and hence, 1 w/v% NPs concentration provided the more stable systems. However, all the emulsions were successfully broken by the permanent magnet. Yet, the presence of ethanol improves the ability of the external magnetic field to demulsify these dispersions. Furthermore, the used hybrid NPs were recovered and recycled for three cycles. The recycled NPs were characterized with X-ray diffraction (XRD) and vibrating sample magnetometry (VSM) indicating that they retained their saturation magnetization and crystalline structure, demonstrating their lack of degradation over multiple recycling cycles. This study facilitates the exploration of innovative two-phase Pickering emulsions comprising three distinct liquid components and their utilization in liquid-liquid extraction processes. 

The logarithmic law of the wall does not capture the mean flow when a boundary layer is subjected to a strong pressure gradient. In such a boundary layer, the mean flow is affected by the spatio-temporal history of the imposed pressure gradient; and accounting for history effects remains a challenge. This work aims to develop a universal mean flow scaling for boundary layers subjected to arbitrary adverse or/and favourable pressure gradients. We derive from the Navier–Stokes equation a velocity transformation that accounts for the history effects and maps the mean flow to the canonical law of the wall. The transformation is tested against channel flows with a suddenly imposed adverse or favourable pressure gradient, boundary layer flows subjected to an adverse pressure gradient, and Couette–Poiseuille flows with a streamwise pressure gradient. It is found that the transformed velocity profiles follow closely the equilibrium law of the wall.

 

Mechanosensitive mechanisms are often used to sense damage to tissue structure, stimulating matrix synthesis and repair. While this kind of mechanoregulatory process is well recognized in eukaryotic systems, it is not known whether such a process occurs in bacteria. InVibrio cholerae, antibiotic-induced damage to the load-bearing cell wall promotes increased signaling by the two-component system VxrAB, which stimulates cell wall synthesis. Here we show that changes in mechanical stress within the cell envelope are sufficient to stimulate VxrAB signaling in the absence of antibiotics. We applied mechanical forces to individual bacteria using three distinct loading modalities: extrusion loading within a microfluidic device, direct compression and hydrostatic pressure. In all cases, VxrAB signaling, as indicated by a fluorescent protein reporter, was increased in cells submitted to greater magnitudes of mechanical loading, hence diverse forms of mechanical stimuli activate VxrAB signaling. Reduction in cell envelope stiffness following removal of the endopeptidase ShyA led to large increases in cell envelope deformation and substantially increased VxrAB response, further supporting the responsiveness of VxrAB. Our findings demonstrate a mechanosensitive gene regulatory system in bacteria and suggest that mechanical signals may contribute to the regulation of cell wall homeostasis.