Near-term fetal and neonatal parathyroid gland function has been studied in the Rhesus monkey. Fetal serum ionized calcium (Ca⁺⁺) levels are significantly greater than simultaneously obtained maternal levels. Fetal serum parathyroid hormone (PTH) was undetectable both in the basal state and in association with EDTA-induced fetal hypocalcemia. Induced maternal hypocalcemia was associated with increased maternal serum PTH levels and no change in fetal basal serum Ca⁺⁺ or PTH levels. Only a minimal decrease in simian neonatal serum Ca⁺⁺ occurred over the first 48 h of life. Normal adult levels of serum PTH were present as early as 6 h of neonatal life. Induced hypocalcemia at 12 h of age resulted in a significant increase in serum PTH levels.

No abstract

Current in-situ X-ray diffraction (XRD) techniques generate data over human analytical capabilities – leading to the loss of novel insights. Automated techniques require human intervention, and lack the performance and adaptability needed for material exploration. With the critical need for high-throughput automated XRD pattern analysis of novel materials, we developed a generalized deep learning model to classify a diverse set of materials’ crystal systems and space groups. In our approach, we generated training data with a holistic representation of patterns that emerge from varying experimental conditions and crystal properties. We then used an expedited learning technique to refine our model’s expertise to experimental conditions. Additionally, we used evaluation data to interpret our model’s decision-making and optimized model architecture to elicit classification based on Bragg’s Law. We evaluated our models on experimental data, novel materials, and altered cubic crystals, where we observed state-of-the-art performance and even greater advances in space group classification.

We present a simple yet highly generalizable method for explaining interacting parts within a neural network's reasoning process. In this work, we consider local, global, and higher-order statistical interactions. Generally speaking, local interactions occur between features within individual datapoints, while global interactions come in the form of universal features across the whole dataset. With deep learning, combined with some heuristics for tractability, we achieve state of the art measurement of global statistical interaction effects, including at higher orders (3-way interactions or more). We generalize this to the multidimensional setting to explain local interactions in multi-object detection and relational reasoning using the COCO annotated-image and Sort-Of-CLEVR toy datasets respectively. Here, we submit a new task for testing feature vector interactions, conduct a human study, propose a novel metric for relational reasoning, and use our interaction interpretations to innovate a more effective Relation Network. Finally, we apply these techniques on a real-world biomedical dataset to discover the higher-order interactions underlying Parkinson's disease clinical progression. Code for all experiments, fully reproducible, is available at: https://github.com/slerman12/ExplainingInteractions.Preprint. Under review.