A fast and efficient deep learning procedure for tracking droplet motion in dense microfluidic emulsions

We present a deep learning-based object detection and object tracking algorithm to study droplet motion in dense microfluidic emulsions. The deep learning procedure is shown to correctly predict the droplets’ shape and track their motion at competitive rates as compared to standard clustering algorithms, even in the presence of significant deformations. The deep learning technique and tool developed in this work could be used for the general study of the dynamics of biological agents in fluid systems, such as moving cells and self-propelled microorganisms in complex biological flows. This article is part of the theme issue ‘Progress in mesoscale methods for fluid dynamics simulation’.

Go with the Flow: Modelling Unique Biological Flows in Engineered In Vitro Platforms

Interest in recapitulating in vivo phenomena in vitro using organ-on-a-chip technology has grown rapidly and with it, attention to the types of fluid flow experienced in the body has followed...

Hotspots of boundary accumulation: dynamics and statistics of micro-swimmers in flowing films

Biological flows over surfaces and interfaces can result in accumulation hotspots or depleted voids of microorganisms in natural environments. Apprehending the mechanisms that lead to such distributions is essential for understanding biofilm initiation. Using a systematic framework, we resolve the dynamics and statistics of swimming microbes within flowing films, considering the impact of confinement through steric and hydrodynamic interactions, flow and motility, along with Brownian and run–tumble fluctuations. Micro-swimmers can be peeled off the solid wall above a critical flow strength. However, the interplay of flow and fluctuations causes organisms to migrate back towards the wall above a secondary critical value. Hence, faster flows may not always be the most efficacious strategy to discourage biofilm initiation. Moreover, we find run–tumble dynamics commonly used by flagellated microbes to be an intrinsically more successful strategy to escape from boundaries than equivalent levels of enhanced Brownian noise in ciliated organisms.

Numerical Prediction of Microbubble Attachment in Biological Flows

Biofilm infections pose a major threat to human health and are difficult to detect. Microbubbles provide an effective and inexpensive method of detection for biofilm-based infections and other diseases such as cancer. The approach studied here examines the potential of targeted microbubbles, with specific antibodies covalently linked to their surfaces for use as ultrasound contrast agents and drug delivery vehicle. This work presents a novel numerical model for estimating the forces on microbubble conjugates in the vascular system. A full computational fluid dynamics simulation of biological fluid flow and the resulting forces on attached microbubbles is presented as well as comparisons with simplified analytical models. Both the computational and analytical predictions are compared with experimental measurements from Takalkar et al. and Schmidt et al., and these comparisons indicate stable microbubble attachment can be anticipated when the total hydrodynamic force on the microbubble is less than 100 pN. Through the examination of typical biological flows, microbubble attachment can be expected up to an average fluid velocity of 0.025 cm/s near the microbubble (i.e., a particle Reynolds number on the order of .001). The Stokes drag law was shown to predict the drag force (the dominant force) on the microbubble within an order of magnitude of the force predicted by the numerical model. Finally, it was found that the lift force on a microbubble was small relative to the drag force, and that the Saffman equation prediction differed from the numerical model by more than an order of magnitude for the biological flows examined. KEYWORDS: Microbubble Attachment; Ultrasound Contrast Agent; Hydrodynamic Force; Computational Fluid Dynamics