The cytoskeletal protein vinculin contributes to the mechanical link of the contractile actomyosin cytoskeleton to the extracellular matrix (ECM) through integrin receptors. In addition, vinculin modulates the dynamics of cell adhesions and is associated with decreased cell motility on two-dimensional ECM substrates. The effect of vinculin on cell invasion through dense three-dimensional ECM gels is unknown. Here, we report how vinculin expression affects cell invasion into three-dimensional collagen matrices. Cell motility was investigated in vinculin knockout and vinculin expressing wild-type mouse embryonic fibroblasts. Vinculin knockout cells were 2-fold more motile on two-dimensional collagen-coated substrates compared with wild-type cells, but 3-fold less invasive in 2.4 mg/ml three-dimensional collagen matrices. Vinculin knockout cells were softer and remodeled their cytoskeleton more dynamically, which is consistent with their enhanced two-dimensional motility but does not explain their reduced three-dimensional invasiveness. Importantly, vinculin-expressing cells adhered more strongly to collagen and generated 3-fold higher traction forces compared with vinculin knockout cells. Moreover, vinculin-expressing cells were able to migrate into dense (5.8 mg/ml) threedimensional collagen matrices that were impenetrable for vinculin knockout cells. These findings suggest that vinculin facilitates three-dimensional matrix invasion through up-regulation or enhanced transmission of traction forces that are needed to overcome the steric hindrance of ECMs.Cell migration is an important and fundamental biomechanical process that plays an essential role in inflammatory diseases, embryonic development, wound healing, and metastasis formation. Current concepts of cell migration have been established in two-dimensional models, but they can explain only partially the migratory behavior in three dimensions. For instance, the migratory capability of cells on two-dimensional substrates depends mainly on adhesion strength, adhesion dynamics, and the dynamics of cytoskeletal remodeling (1, 2), whereas the migratory capability of cells in three-dimensional connective tissue depends also on the steric hindrance of the matrix, matrix degradation by proteolytic enzyme secretion, and the generation of protrusive or contractile forces (1, 3-5). The balance of all these parameters-adhesion strength, cytoskeletal remodeling, matrix degradation, and the generation and transmission of contractile forces-is important for the migration speed in three-dimensional extracellular matrix (ECM) 2 (6). Depending on this balance, a broad variety of invasion strategies between different cell types and even within the same cell type are possible (7).The connection between the ECM and the actomyosin cytoskeleton through integrin-type cell-matrix adhesion receptors is facilitated by the mechano-coupling protein vinculin (8, 9). The effect of vinculin on the migration of cells has previously been investigated using two-dimensional ECM substrates, whe...

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The low-frequency impulsive gunshot vocalizations of baleen whales exhibit dispersive propagation in shallow-water channels which is well-modeled by normal mode theory. Typically, underwater acoustic source range estimation requires multiple time-synchronized hydrophone arrays which can be difficult and expensive to achieve. However, single-hydrophone modal dispersion has been used to range baleen whale vocalizations and estimate shallow-water geoacoustic properties. Although convenient when compared to sensor arrays, these algorithms require preliminary signal detection and human labor to estimate the modal dispersion. In this paper, we apply a temporal convolutional network (TCN) to spectrograms from single-hydrophone acoustic data for simultaneous gunshot detection and ranging. The TCN learns ranging and detection jointly using gunshots simulated across multiple environments and ranges along with experimental noise. The synthetic data are informed by only the water column depth, sound speed, and density of the experimental environment, while other parameters span empirically observed bounds. The method is experimentally verified on North Pacific right whale gunshot data collected in the Bering Sea. To do so, 50 dispersive gunshots were manually ranged using the state-of-the-art time-warping inversion method. The TCN detected these gunshots among 50 noise-only examples with high precision and estimated ranges which closely matched those of the physics-based approach.

A convolutional neural network (CNN) was trained to identify multi-modal gunshots (impulse calls) within large acoustic datasets in shallow-water environments. South Atlantic right whale gunshots were used to train the CNN, and North Pacific right whale (NPRW) gunshots, to which the network was naive, were used for testing. The classifier generalizes to new gunshots from the NPRW and is shown to identify calls which can be used to invert for source range and/or environmental parameters. This can save human analysts hours of manually screening large passive acoustic monitoring datasets.

Localizing sources of underwater sound is a well studied field that is utilized by several scientific and naval communities. The scope of localization might differ dramatically, from the necessity to localize targets with a sub-meter accuracy to estimation of the position of an object on a kilometer scale. Advances in data storing capabilities during the last decade now allow multi-year deployments of autonomous passive acoustic monitoring arrays for which past-recovery time synchronization cannot be guaranteed. For localization of transient signals, like marine mammal vocalization, arrival time based localization schemes are currently the prevalent method. Applying arrival time based methods to non-synchronized multi station arrays eventually leads to large localization uncertainties. Here, we present a backpropagation based localization scheme that overcomes the necessity to synchronize between array stations for localization purposes. It utilizes waveguide dispersion measured within distributed arrays for simultaneous source localization and time synchronization. Numerical examples are presented to demonstrate that localization uncertainty significantly improves compared to arrival time based methods.