The cyanobacterium Cyanothece sp. ATCC 51142 is the model photosynthetic bacterium being used for fundamental studies of solar energy capture process and conversion to the chemical energy [1, 2]. To facilitate processes such as photosynthesis, nitrogen fixation and carbon fixation during the circadian cycle, Cyanothece undergoes dramatic morphological changes [3]. As a part of Grand Challenge in Membrane Biology project, several research groups from different disciplines are working in an orchestrated manner to obtain data such as gene expression levels, proteomics and metabolomics during the circadian cycle. We are creating a whole cell 3D model to provide basis for modeling dynamic cellular processes and correlate Cyanothece ultrastructural changes with these molecular networks datasets. We are using TEM tomography as a tool for constructing this 3D model. The Cyanothece cell is of a spherical shape about 3 um in diameter, and thus consecutive serial sectioning is necessary for obtaining the whole cell model. Individual TEM tomograms from consecutive plastic sections are then aligned into one whole cell volume. Feature segmentation and cell components rendering will provide a comprehensive model of Cyanothece's internal architecture. The main point of interest is the organization of the intricate network of thylakoid membranes, as well as spatial distribution of an assortment of storage granules and other cell components [4].Cyanothece cells grown in alternating 12 hour light and dark intervals were high pressure frozen, freeze substituted and embedded in Epon. Ribbons of serial 200 nm thick sections were mounted on a copper slotted grid, and inspected in Tecnai T-12 (LaB6) with tomography stage. 2x2K CCD camera (Gatan) was used for imaging and analyses. Tilt series were acquired typically from +/-70 degrees, single tilt, taking advantage of a large opening in the slotted grid that didn't interfere with the tilting window if sections ribbon was positioned in the central portion of a grid parallel with the longer oval grid opening dimension. We are aware of the fact that the higher accelerating voltage TEM would enable the 3D reconstruction of a whole bacterial cell in fewer steps. However, with instrumentation present in our facility, and the further focus on much smaller cellular components such as phycobilisomes and ribosomal trafficking along the thylakoid membranes, we have decided to utilize our TEM for this project, regardless of the possible disadvantage of more thin sections required for the whole cell reconstruction. Our findings show that the 120kV instrument provided excellent contrast and resolution for this purpose. Regardless of the method of acquirement of the individual tomography volumes, the most time-consuming and critical component of the visualization process is the features segmentation. To aid the manual segmentation process, a series of image analysis and 1338 CD

Computed tomography has propelled scientific advances in fields from biology to materials science. This technology allows for the elucidation of 3-dimensional internal structure by the attenuation of x-rays through an object at different rotations relative to the beam. By imaging 2-dimensional projections, a 3-dimensional object can be reconstructed through a computational algorithm. Imaging at a greater number of rotation angles allows for improved reconstruction. However, taking more measurements increases the x-ray dose and may cause sample damage. Deep neural networks have been used to transform sparse 2-D projection measurements to a 3-D reconstruction by training on a dataset of known similar objects. However, obtaining high-quality object reconstructions for the training dataset requires high x-ray dose measurements that can destroy or alter the specimen before imaging is complete. This becomes a chicken-and-egg problem: high-quality reconstructions cannot be generated without deep learning, and the deep neural network cannot be learned without the reconstructions. This work develops and validates a selfsupervised probabilistic deep learning technique, the physics-informed variational autoencoder, to solve this problem. A dataset consisting solely of sparse projection measurements from each object is used to jointly reconstruct all objects of the set. This approach has the potential to allow visualization of fragile samples with x-ray computed tomography. We release our code for reproducing our results at: https://github.com/vganapati/CT_PVAE.

Fluid overload is a chronic medical condition that affects over six million Americans with conditions such as congestive heart failure, end-stage renal disease, and lymphedema. Remote management of fluid overload continues to be a leading clinical challenge. Bioimpedance is one technique that can be used to estimate the hydration of tissue and track it over time. However, commercially available bioimpedance measurement systems are bulky, expensive, and rely on Ag/AgCl electrodes that dry out and can irritate the skin. The use of bioimpedance today is therefore limited to clinical and research settings, with measurements performed at daily intervals or over short periods of time rather than continuously and long-term. This paper proposes using wearable calf bioimpedance measurements integrated into a compression sock for long-term fluid overload management. A PCB was developed using standard measurement techniques that measures the calf bioimpedance using a custom analog front-end built around an AD8302 gain-phase detection chip. Data is transmitted wirelessly via Bluetooth Low Energy to an iOS device using a custom iOS app. Bioimpedance data were collected both from the wearable system and a commercial measurement system (ImpediMed SFB7) using RRC networks, Ag/AgCl electrodes, and the textile compression sock. Bioimpedance data collected from the wearable system showed close agreement with data from the SFB7 when using RRC networks and in five healthy human subjects with Ag/AgCl electrodes. However, when using the textile compression sock the wearable system had worse precision than the SFB7 (4% run to run compared to <1% run to run) and there were larger differences between the two systems than when using the RRC networks and the Ag/AgCl electrodes. Wearable system precision and agreement with the SFB7 was improved by pressure or light wetting of the current electrodes on the sock. Future research should focus on reliable elimination of low-frequency artifacts in research grade hardware to enable long-term calf bioimpedance measurements for fluid overload management.