Visualization of low Reynolds boundary-driven cavity flows in thin liquid shells

Classic examples of low-Reynolds recirculating cavity flows are typically generated from lid-driven boundary motion at a solid-fluid interface, or alternatively may result from shear flow over cavity openings. Here, we are interested in an original family of boundary-driven cavity flows occurring, in contrast to classic setups, at fluid-fluid interfaces. Particle image velocimetry (PIV) is used to investigate the structure of internal convective flows observed in thin liquid shells. Under the specific configuration investigated, the soap bubble's liquid shell is in fact in motion and exhibits sporadic local "bursts”. These bursts induce transient flow motion within the cavity of order Re ∼ O(1). The combination of PIV and proper orthogonal decomposition (POD) is used to extract dominant flow structures present within bubble cavities. Next, we show that thermally induced Marangoni flows in the liquid shell can lead to forced, (quasi) steady-state, internal recirculating flows. The present findings illustrate a novel example of low-Reynolds boundary-driven cavity flows. Graphical Abstrac

Multi-Environment Model Estimation for Motility Analysis of \u3cem\u3eCaenorhabditis elegans\u3c/em\u3e

The nematode Caenorhabditis elegans is a well-known model organism used to investigate fundamental questions in biology. Motility assays of this small roundworm are designed to study the relationships between genes and behavior. Commonly, motility analysis is used to classify nematode movements and characterize them quantitatively. Over the past years, C. elegans’ motility has been studied across a wide range of environments, including crawling on substrates, swimming in fluids, and locomoting through microfluidic substrates. However, each environment often requires customized image processing tools relying on heuristic parameter tuning. In the present study, we propose a novel Multi Environment Model Estimation (MEME) framework for automated image segmentation that is versatile across various environments. The MEME platform is constructed around the concept of Mixture of Gaussian (MOG) models, where statistical models for both the background environment and the nematode appearance are explicitly learned and used to accurately segment a target nematode. Our method is designed to simplify the burden often imposed on users; here, only a single image which includes a nematode in its environment must be provided for model learning. In addition, our platform enables the extraction of nematode ‘skeletons’ for straightforward motility quantification. We test our algorithm on various locomotive environments and compare performances with an intensity-based thresholding method. Overall, MEME outperforms the threshold-based approach for the overwhelming majority of cases examined. Ultimately, MEME provides researchers with an attractive platform for C. elegans’ segmentation and ‘skeletonizing’ across a wide range of motility assays

Aerosols in Healthy and Emphysematous In Silico Pulmonary Acinar Rat Models

International audienceThere has been relatively little attention given on predicting particle deposition in the respiratory zone of the diseased lungs despite the high prevalence of chronic obstructive pulmonary disease (COPD). Increased alveolar volume and deterioration of alveolar septum, characteristic of emphysema, may alter the amount and location of particle deposition compared to healthy lungs, which is particularly important for toxic or therapeutic aerosols. In an attempt to shed new light on aerosol transport and deposition in emphysematous lungs, we performed numerical simulations in models of healthy and emphysematous acini motivated by recent experimental lobar-level data in rats~\citep{OakesEmp}. Compared to healthy acinar structures, models of emphysematous subacini were created by removing inter-septal alveolar walls and enhancing the alveolar volume in either a homogeneous or heterogeneous fashion. Flow waveforms and particle properties were implemented to match the experimental data. The occurrence of flow separation and recirculation within alveolar cavities was found in proximal generations of the healthy zones, in contrast to the radial-like airflows observed in the diseased regions. In agreement with experimental data, simulations point to particle deposition concentrations that are more heterogeneously distributed in the diseased models compared with the healthy one. Yet, simulations predicted less deposition in the emphysematous models in contrast to some experimental studies, a likely consequence due to the shallower penetration depths and modified flow topologies in disease compared to health. These spatial-temporal particle transport simulations provide new insight on deposition in the emphysematous acini and shed light on experimental observations

True-scale biomimetic multi-generation airway platforms of the human bronchial epithelium for in vitro cytotoxicity screening

Lung exposure to inhaled particulate matter may injure the epithelial tissue and lead to a loss of function in affected regions via inflammation for example. Screening for the critical contaminate concentrations may provide essential information towards damage assessment and epithelial healing. To date, most approaches have typically relied on traditional in vitro well plate assays or alternatively in vivo animal experiments. Yet, such methods manifest some outstanding disadvantages such as the inability to capture physiological flow and aerosol deposition characteristics as well as significant differences in anatomy, immune system and inflammatory responses compared to humans. The advent of organ-on-chip platforms has shown promising results to reconcile many such drawbacks. In an attempt to provide an attractive in vitro gateway to monitor airway health, we discuss here a novel biomimetic platform which emulates the bronchial epithelium of a human upper airway, allowing to study organ-level characteristics in a homeostatic cellular microenvironment. This device reconstitutes a multi-generation pulmonary epithelial airway environment, capturing realistic respiratory transport phenomena and critical cellular barrier functions at an air-liquid interface (ALI), in analogy to the bronchial lumen. As a proof of concept, we demonstrate its feasibility for in vitro based assays by exposing the device to cytotoxic aerosolized particles under respiratory flow conditions. Subsequently, we investigate the cytotoxic effects of these particles including cellular viability, cytokine and mucus secretion as a function of local particle deposition patterns. Ultimately, our bronchial airway models are intended to provide off-the-shelf in vitro kits geared for the end-user interested in a wide range of broader biological assays that may be attractive for cytotoxicity and drug screening. Please click Additional Files below to see the full abstract

Material Properties of \u3cem\u3eCaenorhabditis Elegans\u3c/em\u3e Swimming at Low Reynolds Number

Undulatory locomotion, as seen in the nematode Caenorhabditis elegans, is a common swimming gait of organisms in the low Reynolds number regime, where viscous forces are dominant. Although the nematode’s motility is expected to be a strong function of its material properties, measurements remain scarce. Here, the swimming behavior of C. elegans is investigated in experiments and in a simple model. Experiments reveal that nematodes swim in a periodic fashion and generate traveling waves that decay from head to tail. The model is able to capture the experiments’ main features and is used to estimate the nematode’s Young’s modulus E and tissue viscosity η. For wild-type C. elegans, we find E ≈ 3.77 kPa and η ≈ –860 Pa•s; values of η for live C. elegans are negative because the tissue is generating rather than dissipating energy. Results show that material properties are sensitive to changes in muscle functional properties, and are useful quantitative tools with which to more accurately describe new and existing muscle mutants

Dendritic tree extraction from noisy maximum intensity projection images in C. elegans

Background: Maximum Intensity Projections (MIP) of neuronal dendritic trees obtained from confocal microscopy are frequently used to study the relationship between tree morphology and mechanosensory function in the model organism C. elegans. Extracting dendritic trees from noisy images remains however a strenuous process that has traditionally relied on manual approaches. Here, we focus on automated and reliable 2D segmentations of dendritic trees following a statistical learning framework. Methods: Our dendritic tree extraction (DTE) method uses small amounts of labelled training data on MIPs to learn noise models of texture-based features from the responses of tree structures and image background. Our strategy lies in evaluating statistical models of noise that account for both the variability generated from the imaging process and from the aggregation of information in the MIP images. These noisy models are then used within a probabilistic, or Bayesian framework to provide a coarse 2D dendritic tree segmentation. Finally, some post-processing is applied to refine the segmentations and provide skeletonized trees using a morphological thinning process. Results: Following a Leave-One-Out Cross Validation (LOOCV) method for an MIP database with available “ground truth” images, we demonstrate that our approach provides significant improvements in tree-structure segmentations over traditional intensity-based methods. Improvements for MIPs under various imaging conditions are both qualitative and quantitative, as measured from Receiver Operator Characteristic (ROC) curves and the yield and error rates in the final segmentations. In a final step, we demonstrate our DTE approach on previously unseen MIP samples including the extraction of skeletonized structures, and compare our method to a state-of-the art dendritic tree tracing software. Conclusions: Overall, our DTE method allows for robust dendritic tree segmentations in noisy MIPs, outperforming traditional intensity-based methods. Such approach provides a useable segmentation framework, ultimately delivering a speed-up for dendritic tree identification on the user end and a reliable first step towards further morphological characterizations of tree arborization

Robustness of common hemodynamic indicators with respect to numerical resolution in 38 middle cerebral artery aneurysms

Background: Using computational fluid dynamics (CFD) to compute the hemodynamics in cerebral aneurysms has received much attention in the last decade. The usability of these methods depends on the quality of the computations, highlighted in recent discussions. The purpose of this study is to investigate the convergence of common hemodynamic indicators with respect to numerical resolution. Methods: 38 middle cerebral artery bifurcation aneurysms were studied at two different resolutions (one comparable to most studies, and one finer). Relevant hemodynamic indicators were collected from two of the most cited studies, and were compared at the two refinements. In addition, correlation to rupture was investigated. Results: Most of the hemodynamic indicators were very well resolved at the coarser resolutions, correlating with the finest resolution with a correlation coefficient >0.95. The oscillatory shear index (OSI) had the lowest correlation coefficient of 0.83. A logarithmic Bland-Altman plot revealed noticeable variations in the proportion of the aneurysm under low shear, as well as in spatial and temporal gradients not captured by the correlation alone. Conclusion: Statistically, hemodynamic indicators agree well across the different resolutions studied here. However, there are clear outliers visible in several of the hemodynamic indicators, which suggests that special care should be taken when considering individual assessment