Differential interference contrast (DIC) microscopy is widely used for observing unstained biological samples that are otherwise optically transparent. Combining this optical technique with machine vision could enable the automation of many life science experiments; however, identifying relevant features under DIC is challenging. In particular, precise tracking of cell boundaries in a thick ( ) slice of tissue has not previously been accomplished. We present a novel deconvolution algorithm that achieves the state-of-the-art performance at identifying and tracking these membrane locations. Our proposed algorithm is formulated as a regularized least squares optimization that incorporates a filtering mechanism to handle organic tissue interference and a robust edge-sparsity regularizer that integrates dynamic edge tracking capabilities. As a secondary contribution, this paper also describes new community infrastructure in the form of a MATLAB toolbox for accurately simulating DIC microscopy images of in vitro brain slices. Building on existing DIC optics modeling, our simulation framework additionally contributes an accurate representation of interference from organic tissue, neuronal cell-shapes, and tissue motion due to the action of the pipette. This simulator allows us to better understand the image statistics (to improve algorithms), as well as quantitatively test cell segmentation and tracking algorithms in scenarios, where ground truth data is fully known.

In this work we consider nano-scale communication using bacterial populations as transceivers. We demonstrate using a microfluidic test-bed and a population of genetically engineered Escherichia coli bacteria serving as the communication receiver that a simple modulation like on-off keying (OOK) is indeed achievable, but suffers from very poor data-rates. We explore an alternative communication strategy called time elapse communication (TEC) that uses the time period between signals to encode information. We identify the severe limitations of TEC under practical non-zero error conditions in the target environment, and propose an advanced communication strategy called smart time elapse communication (TEC-SMART) that achieves over a 10× improvement in data-rate over OOK.

Bacterial populations housed in microfluidic environments can serve as transceivers for molecular communication, but the data-rates are extremely low (e.g., 10-5bits per second.). In this work, genetically engineered Escherichia coli bacteria were maintained in a microfluidic device where their response to a chemical stimulus was examined over time. The bacteria serve as a communication receiver where a simple modulation such as on-off keying (OOK) is achievable, although it suffers from very poor data-rates. We explore an alternative communication strategy called time-elapse communication (TEC) that uses the time period between signals to encode information. We identify the limitations of TEC under practical non-zero error conditions and propose an advanced communication strategy called smart time-elapse communication (TEC-SMART) that achieves over a 10x improvement in data-rate over OOK. We derive the capacity of TEC and provide a theoretical maximum data-rate that can be achieved.