Forward Electrophysiological Modeling and Inverse Problem for Uterine Contractions during Pregnancy

Uterine contractile dysfunction during pregnancy is a significant healthcare challenge that imposes heavy medical and financial burdens on both human beings and society. In the U.S., about 12% of babies are born prematurely each year, which is a leading cause of neonatal mortality and increases the possibility of having subsequent health problems. Post-term birth, in which a baby is born after 42 weeks of gestation, can cause risks for both the newborn and the mother. Currently, there is a limited understanding of how the uterus transitions from quiescence to excitation, which hampers our ability to detect labor and treat major obstetric syndromes associated with contractile dysfunction. Therefore, it is critical to develop objective methods to investigate the underlying contractile mechanism using a non-invasive sensing technique. This dissertation focuses on the multiscale forward electromagnetic modeling of uterine contractile activities and the inverse estimation of underlying source currents from abdominal magnetic field measurements. We develop a realistic multiscale forward electromagnetic model of uterine contractions in the pregnant uterus, taking into account current electrophysiological and anatomical knowledge of the uterus. Previous models focused on generating contractile forces at the organ level or on ionic concentration changes at the cellular level. Our approach is to characterize the electromagnetic fields of uterine contractions jointly at the cellular, tissue, and organ levels. At the cellular level, focusing on both plateau-type and bursting-type action potentials, we introduce a generalized version of the FitzHugh-Nagumo equations and analyze its response behavior based on bifurcation theory. To represent the anisotropy of the myometrium, we introduce a random conductivity tensor model for the fiber orientations at the tissue level. Specifically, we divide the uterus into contiguous regions, each of which is assigned a random fiber angle. We also derive analytical expressions for the spiking frequency and propagation velocity of the bursting potential. At the organ level, we propose a realistic four-compartment volume conductor, in which the uterus is modeled based on the magnetic resonance imaging scans of a near-term woman and the abdomen is curved to match the device used to take the magnetomyography measurements. To mimic the effect of the sensing direction, we incorporate a sensor array model on the surface of abdomen. We illustrate our approach using numerical examples and compute the magnetic field using the finite element method. Our results show that fiber orientation and initiation location are the key factors affecting the magnetic field pattern, and that our multiscale forward model flexibly characterizes the limited-propagation local contractions at term. These results are potentially important as a tool for interpreting the non-invasive measurements of uterine contractions. We also consider the inverse problem of uterine contractions during pregnancy. Our aim is to estimate the myometrial source currents that generate the external magnetomyography measurements. Existing works approach this problem using synthetic electromyography data. Our approach instead proceeds in two stages: develop a linear approximation model and conduct the estimation. In the first stage, we derive a linear approximation model of the sensor-oriented magnetic field measurements with respect to source current dipoles in the myometrium, based on a lead-field matrix. In particular, this lead-field matrix is analytically computed from distributed current dipoles in the myometrium according to quasi-static Maxwell\u27s equations, using the finite element method. In the second stage, we solve a constrained least-squares problem to estimate the source currents, from which we predict the intrauterine pressure. We demonstrate our approach through numerical examples with synthetic data that are generated using our multiscale forward model. In the simulations, we assume that the excitation is located at the fundus of the uterus. We also illustrate our approach using real data sets, one of which has simultaneous contractile pressure measurements. The results show that our method well captures the short-distance propagation of uterine contractile activities during pregnancy, the change of excitation area in subsequent contractions or even in a single contraction, and the timing of uterine contractions. These findings are helpful in understanding the physiological and functional properties of the uterus, potentially enabling the diagnosis of labor and the treatment of obstetric syndromes associated with contractile dysfunction such as preterm birth and post-term birth

An Improved Algorithm for Learning to Perform Exception-Tolerant Abduction

Abstract Inference from an observed or hypothesized condition to a plausible cause or explanation for this condition is known as abduction. For many tasks, the acquisition of the necessary knowledge by machine learning has been widely found to be highly effective. However, the semantics of learned knowledge are weaker than the usual classical semantics, and this necessitates new formulations of many tasks. We focus on a recently introduced formulation of the abductive inference task that is thus adapted to the semantics of machine learning. A key problem is that we cannot expect that our causes or explanations will be perfect, and they must tolerate some error due to the world being more complicated than our formalization allows. This is a version of the qualification problem, and in machine learning, this is known as agnostic learning. In the work by Juba that introduced the task of learning to make abductive inferences, an algorithm is given for producing k-DNF explanations that tolerates such exceptions: if the best possible k-DNF explanation fails to justify the condition with probability , then the algorithm is promised to find a k-DNF explanation that fails to justify the condition with probability at most , where n is the number of propositional attributes used to describe the domain. Here, we present an improved algorithm for this task. When the best k-DNF fails with probability , our algorithm finds a k-DNF that fails with probability at most (i.e., suppressing logarithmic factors in n and ).We examine the empirical advantage of this new algorithm over the previous algorithm in two test domains, one of explaining conditions generated by a “noisy k-DNF rule, and another of explaining conditions that are actually generated by a linear threshold rule. We also apply the algorithm on the real world application Anomaly explanation. In this work, as opposed to anomaly detection, we are interested in finding possible descriptions of what may be causing anomalies in visual data. We use PCA to perform anomaly detection. The task is attaching semantics drawn from the image meta-data to a portion of the anomalous images from some source such as web-came. Such a partial description of the anomalous images in terms of the meta-data is useful both because it may help to explain what causes the identified anomalies, and also because it may help to identify the truly unusual images that defy such simple categorization. We find that it is a good match to apply our approximation algorithm on this task. Our algorithm successfully finds plausible explanations of the anomalies. It yields low error rate when the data set is large(\u3e80,000 inputs) and also works well when the data set is not very large(\u3c 50,000 examples). It finds small 2-DNFs that are easy to interpret and capture a non-negligible

Dual roles of anesthetics in postoperative cognitive dysfunction: Regulation of microglial activation through inflammatory signaling pathways

Postoperative cognitive dysfunction (POCD) is a prevalent clinical entity following surgery and is characterized by declined neurocognitive function. Neuroinflammation mediated by microglia is the essential mechanism of POCD. Anesthetics are thought to be a major contributor to the development of POCD, as they promote microglial activation and induce neuroinflammation. However, this claim remains controversial. Anesthetics can exert both anti- and pro-inflammatory effects by modulating microglial activation, suggesting that anesthetics may play dual roles in the pathogenesis of POCD. Here, we review the mechanisms by which the commonly used anesthetics regulate microglial activation via inflammatory signaling pathways, showing both anti- and pro-inflammatory properties of anesthetics, and indicating how perioperative administration of anesthetics might either relieve or worsen POCD development. The potential for anesthetics to enhance cognitive performance based on their anti-inflammatory properties is further discussed, emphasizing that the beneficial effects of anesthetics vary depending on dose, exposure time, and patients’ characteristics. To minimize the incidence of POCD, we recommend considering these factors to select appropriate anesthetics

Modeling and Analyzing Scorer Preferences in Short-Answer Math Questions

Automated scoring of student responses to open-ended questions, including short-answer questions, has great potential to scale to a large number of responses. Recent approaches for automated scoring rely on supervised learning, i.e., training classifiers or fine-tuning language models on a small number of responses with human-provided score labels. However, since scoring is a subjective process, these human scores are noisy and can be highly variable, depending on the scorer. In this paper, we investigate a collection of models that account for the individual preferences and tendencies of each human scorer in the automated scoring task. We apply these models to a short-answer math response dataset where each response is scored (often differently) by multiple different human scorers. We conduct quantitative experiments to show that our scorer models lead to improved automated scoring accuracy. We also conduct quantitative experiments and case studies to analyze the individual preferences and tendencies of scorers. We found that scorers can be grouped into several obvious clusters, with each cluster having distinct features, and analyzed them in detail.Comment: Accepted to 16th International Conference on Educational Data Mining (EDM 2023

Interpretable Math Word Problem Solution Generation Via Step-by-step Planning

Solutions to math word problems (MWPs) with step-by-step explanations are valuable, especially in education, to help students better comprehend problem-solving strategies. Most existing approaches only focus on obtaining the final correct answer. A few recent approaches leverage intermediate solution steps to improve final answer correctness but often cannot generate coherent steps with a clear solution strategy. Contrary to existing work, we focus on improving the correctness and coherence of the intermediate solutions steps. We propose a step-by-step planning approach for intermediate solution generation, which strategically plans the generation of the next solution step based on the MWP and the previous solution steps. Our approach first plans the next step by predicting the necessary math operation needed to proceed, given history steps, then generates the next step, token-by-token, by prompting a language model with the predicted math operation. Experiments on the GSM8K dataset demonstrate that our approach improves the accuracy and interpretability of the solution on both automatic metrics and human evaluation.Comment: Accepted to The 61st Annual Meeting of the Association for Computational Linguistics (ACL 2023

Anomaly Detection of Underwater Gliders Verified by Deployment Data

This paper utilizes an anomaly detection algorithm to check if underwater gliders are operating normally in the unknown ocean environment. Glider pilots can be warned of the detected glider anomaly in real time, thus taking over the glider appropriately and avoiding further damage to the glider. The adopted algorithm is validated by two valuable sets of data in real glider deployments, the University of South Florida (USF) glider Stella and the Skidaway Institute of Oceanography (SkIO) glider Angus.Comment: 10 pages, 16 figures, accepted by the International Symposium on Underwater Technology (UT23

Real-time Autonomous Glider Navigation Software

Underwater gliders are widely utilized for ocean sampling, surveillance, and other various oceanic applications. In the context of complex ocean environments, gliders may yield poor navigation performance due to strong ocean currents, thus requiring substantial human effort during the manual piloting process. To enhance navigation accuracy, we developed a real-time autonomous glider navigation software, named GENIoS Python, which generates waypoints based on flow predictions to assist human piloting. The software is designed to closely check glider status, provide customizable experiment settings, utilize lightweight computing resources, offer stably communicate with dockservers, robustly run for extended operation time, and quantitatively compare flow estimates, which add to its value as an autonomous tool for underwater glider navigation.Comment: OCEANS 2023 Limeric