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Foraminifera play an important role in oceanographic and paleoceanographic research. The test morphology and chemistry within species, as well as the presence or absence of certain species, are affected by environmental conditions. Classification of different species of foraminifera is a crucial yet tedious task for researchers. Deep-learning approaches can help with morphological studies and aid in species classification; however, they require large-scale datasets that are challenging to obtain and annotate because of the extremely small size and delicate handling of these microorganisms. In this work, we expand on an existing mathematical model for foraminifera shell growth to generate 3D synthetic models to aid in these studies. We define parameter spaces for the model which are intended to approximate seven randomly chosen foraminifera taxa. Along with providing an open-source code base to support other researchers in generating models and studying growth patterns, we further extend the synthetic data generation to include a rendering component that mimics two existing robotic imaging systems. We provide two use cases for our synthetic dataset. First, we show how orientation can affect the automated classification of different species and how incorporating aleatoric uncertainty indicators can help select the next views of the samples to significantly improve classification accuracy from 82% to 89%. Next, we show how a sparse set of synthetic 2D images can be used to extract 3D morphology of foraminifera using Neural Radiance Fields (NeRFs). 

Blood coagulation is a highly regulated injury response that features polymerization of fibrin fibers to prevent the passage of blood from a damaged vascular endothelium. A growing body of research seeks to monitor coagulation in microfluidic systems but fails to capture coagulation as a response to disruption of the vascular endothelium. Here we present a device that allows compression injury of a defined segment of a microfluidic vascular endothelium and the assessment of coagulation at the injury site. This pressure injury-on-a-chip (PINCH) device allows visualization of coagulation as the accumulation of fluorescent fibrin at injury sites. Quantification of fluorescent fibrin levels upstream of and at injury sites confirm that pre-treating vascular endothelium with fluid shear stress helps capture coagulation as an injury response. We leverage the PINCH devices to demonstrate the limited coagulation response of type A hemophiliacs and evaluate the performance of hemostatic microparticles and fibrinolytic nanoparticles. Our findings and the straightforward fabrication of the PINCH devices make it a promising choice for additional screening of hemostatic therapeutics. 

Environmental factors, such as drought stress, significantly impact maize growth and productivity worldwide. To improve yield and quality, effective strategies for early detection and mitigation of drought stress in maize are essential. This paper presents a detailed analysis of three imaging trials conducted to detect drought stress in maize plants using an existing, custom-developed, low-cost, high-throughput phenotyping platform. A pipeline is proposed for early detection of water stress in maize plants using a Vision Transformer classifier and analysis of distributions of near-infrared (NIR) reflectance from the plants. A classification accuracy of 85% was achieved in one of our trials, using hold-out trials for testing. Suitable regions on the plant that are more sensitive to drought stress were explored, and it was shown that the region surrounding the youngest expanding leaf (YEL) and the stem can be used as a more consistent alternative to analysis involving just the YEL. Experiments in search of an ideal window size showed that small bounding boxes surrounding the YEL and the stem area of the plant perform better in separating drought-stressed and well-watered plants than larger window sizes enclosing most of the plant. The results presented in this work show good separation between well-watered and drought-stressed categories for two out of the three imaging trials, both in terms of classification accuracy from data-driven features as well as through analysis of histograms of NIR reflectance. 

Sleep staging has a very important role in diagnosing patients with sleep disorders. In general, this task is very time-consuming for physicians to perform. Deep learning shows great potential to automate this process and remove physician bias from decision making. In this study, we aim to identify recent trends on performance improvement and the causes for these trends. Recent papers on sleep stage classification and interpretability are investigated to explore different modeling and data manipulation techniques, their efficiency, and recent advances. We identify an improvement in performance up to 12% on standard datasets over the last 5 years. The improvements in performance do not appear to be necessarily correlated to the size of the models, but instead seem to be caused by incorporating new architectural components, such as the use of transformers and contrastive learning. 

The study of plant electrophysiology offers promising techniques to track plant health and stress in vivo for both agricultural and environmental monitoring applications. Use of superficial electrodes on the plant body to record surface potentials may provide new phenotyping insights. Bacterial nanocellulose (BNC) is a flexible, optically translucent, and water-vapor-permeable material with low manufacturing costs, making it an ideal substrate for non-invasive and non-destructive plant electrodes. This work presents BNC electrodes with screen-printed carbon (graphite) ink-based conductive traces and pads. It investigates the potential of these electrodes for plant surface electrophysiology measurements in comparison to commercially available standard wet gel and needle electrodes. The electrochemically active surface area and impedance of the BNC electrodes varied based on the annealing temperature and time over the ranges of 50 °C to 90 °C and 5 to 60 min, respectively. The water vapor transfer rate and optical transmittance of the BNC substrate were measured to estimate the level of occlusion caused by these surface electrodes on the plant tissue. The total reduction in chlorophyll content under the electrodes was measured after the electrodes were placed on maize leaves for up to 300 h, showing that the BNC caused only a 16% reduction. Maize leaf transpiration was reduced by only 20% under the BNC electrodes after 72 h compared to a 60% reduction under wet gel electrodes in 48 h. On three different model plants, BNC–carbon ink surface electrodes and standard invasive needle electrodes were shown to have a comparable signal quality, with a correlation coefficient of >0.9, when measuring surface biopotentials induced by acute environmental stressors. These are strong indications of the superior performance of the BNC substrate with screen-printed graphite ink as an electrode material for plant surface biopotential recordings. 

Microphysiological systems (MPS) incorporate physiologically relevant microanatomy, mechanics, and cells to mimic tissue function. Reproducible and standardized in vitro models of tissue barriers, such as the blood-tissue interface (BTI), are critical for next-generation MPS applications in research and industry. Many models of the BTI are limited by the need for semipermeable membranes, use of homogenous cell populations, or 2D culture. These factors limit the relevant endothelial-epithelial contact and 3D transport, which would best mimic the BTI. Current models are also difficult to assemble, requiring precise alignment and layering of components. The work reported herein details the engineering of a BTI-on-a-chip (BTI Chip) that addresses current disadvantages by demonstrating a single layer, membrane-free design. Laminar flow profiles, photocurable hydrogel scaffolds, and human cell lines were used to construct a BTI Chip that juxtaposes an endothelium in direct contact with a 3D engineered tissue. A biomaterial composite, gelatin methacryloyl and 8-arm polyethylene glycol thiol, was used for in situ fabrication of a tissue structure within a Y-shaped microfluidic device. To produce the BTI, a laminar flow profile was achieved by flowing a photocurable precursor solution alongside phosphate buffered saline. Immediately after stopping flow, the scaffold underwent polymerization through a rapid exposure to UV light (<300 mJ/cm2). After scaffold formation, blood vessel endothelial cells were introduced and allowed to adhere directly to the 3D tissue scaffold, without barriers or phase guides. Fabrication of the BTI Chip was demonstrated in both an epithelial tissue model and blood-brain barrier (BBB) model. In the epithelial model, scaffolds were seeded with human dermal fibroblasts. For the BBB models, scaffolds were seeded with the immortalized glial cell line, SVGP12. The BTI Chip microanatomy was analyzed post facto by immunohistochemistry, showing the uniform production of a patent endothelium juxtaposed with a 3D engineered tissue. Fluorescent tracer molecules were used to characterize the permeability of the BTI Chip. The BTI Chips were challenged with an efflux pump inhibitor, cyclosporine A, to assess physiological function and endothelial cell activation. Operation of physiologically relevant BTI Chips and a novel means for high-throughput MPS generation was demonstrated, enabling future development for drug candidate screening and fundamental biological investigations.