MY RESEARCH
Microbial Motility




The transport of motile microbes in a flow is determined by both the characteristics of the fluid flow and the motility of microbes. Although it has been well-studied in open-media, the relationship between hydrodynamics and motility for microbes in porous media is not well understood. To address this research gap, we used microfluidic devices to investigate the impacts of flow on the transport of motile microbes in a quasi-two-dimensional synthetic porous medium. Our results show that at higher flow speeds bacterial motility is less important to the transport of bacteria, and that reduced motility results in reduced dispersion. Furthermore, bacteria with swimming motility are shown to remain motile at higher flow speeds than bacteria with twitching motility. Finally, we show that bacteria have enhanced dispersion in high porosity environments. This study provides new insight into the relationship between fluid flow and bacterial motility, which will help inform transport models for a variety of applications such as bioremediation and targeted drug delivery.
Particle Tracking Code Comparison



Particle tracking (PT) is a common technique in microscopy, microfluidics, and colloidal transport studies, where time lapse images are recorded at a high frame rate and image analysis is used to reconstruct particle trajectories. The performance of many PT codes has been tested for particles that exhibit Brownian-type motion. However, PT is frequently used to track particles in porous media where heterogeneous velocity fields generate non-Brownian motion of particles.
To understand the capabilities and limitations of PT algorithms in heterogeneous flows, we simulated the advective transport of particles in an open channel and in two confined channels that differ in their packing and grain size distribution. We tested four different PT codes that differ in their feature finding, trajectory linking and trajectory filtering algorithms. For each geometry, we performed a sensitivity analysis with respect to particle density, particle speed and particle intermittency. We used the ground truth trajectories and imagery from each simulation to compare the performance of the different PT codes by calculating the average Euclidean distance between ground truth and tracked trajectories, the probability distributions of trajectory lengths and velocities, and the false positive rate for spot detection. We show that increasing particle density, speed, and intermittency will negatively impact all PT codes, but some codes are consistently impacted less. We also find that our statistics reveal different problems with trajectory linking for each PT code. Furthermore, our statistics can be used to determine the best PT method for advected particles in a particular porous geometry.
Mammogram Classification with Deep Learning




Cross-view Attention is a multi-view attention method developed to retain relevant information between each of the four views of a mammography exam. Although the Cross-View Attention Module (CvAM) was shown to improve upon the baselines of ResNet50 and ResNet50 with the Convolutional Bottleneck Attention Module (CBAM), little effort has been made to compare CvAM with state of the art attention methods used for breast cancer classification. In this paper we present a comparison of CvAM with CBAM, Multi-Scale Attention (MSA), Double Attention (DA), and Multi-Head Attention (MHA). We implemented each attention module within ResNet50, DenseNet121 and EfficientNetB7 to determine which attention module and base network combination performs best.
Publications
Publications
Working Papers
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Revise & Resubmit at Soft Matter – Berghouse et al., Advection-Dominated Transport Dynamics for Pili and Flagella- Mediated Motile Bacteria in Porous Media.
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Under Peer Review at EAAI – Berghouse et al., DeepTrackStat: an End-to-End Deep Learning Framework for Extraction of Motion Statistics from Videos of Particles
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Under Peer Review at AWR – Berghouse et al., STAMNet - A Spatiotemporal Attention Module and Network for Upscaling Reactive Transport Simulations of the Hyporheic Zone
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In Development – Berghouse et al., Investigation of Feedback Cycles and the Impacts of Speed-Based Biomass Decay on Biomass Growth and Chromium Reduction in the Hyporheic Zone
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​Dissertation - Physical and Deep-Learning-Based Explorations of Microbe-Mediated Reactive Transport Processes in Porous Media Across Scales