Quantitative BioSciences

Abstract: In mammals, the endometrium undergoes dynamic changes in response to estrogen and progesterone to prepare for blastocyst implantation. Two distinct types of endometrial epithelial cells, the luminal (LE) and glandular (GE) epithelial cells play different functional roles during this physiological process. Previously, we have reported that Notch signaling plays multiple roles in embryo implantation, decidualization, and postpartum repair. Here, using the uterine epithelial-specific Ltf-iCre, we showed that Notch1 signaling over-activation in the endometrial epithelium caused dysfunction of the epithelium during the estrous cycle, resulting in hyper-proliferation. During pregnancy, it further led to dysregulation of estrogen and progesterone signaling, resulting in infertility in these animals. Using 3D organoids, we showed that over-activation of Notch1 signaling increased the proliferative potential of both LE and GE cells and reduced the difference in transcription profiles between them, suggesting disrupted differentiation of the uterine epithelium. In addition, we demonstrated that both canonical and non-canonical Notch signaling contributed to the hyper-proliferation of GE cells, but only the non-canonical pathway was involved with estrogen sensitivity in the GE cells. These findings provided insights into the effects of Notch1 signaling on the proliferation, differentiation, and function of the uterine epithelium. This study demonstrated the important roles of Notch1 signaling in regulating hormone response and differentiation of endometrial epithelial cells and provides an opportunity for future studies in estrogen-dependent diseases, such as endometriosis.

Abstract: I use a behavioral economic lens to examine how people facing financial insecurity decide to seek financial help. Help can broadly be categorized as being either formal (provided by an organization or professional) or informal (provided by an individual acting in a personal capacity). I focus on one particularly vital type of financial help from each of these categories: from formal help, I examine social welfare program benefits; from informal help, I examine financial loans or gifts from friends and family. I begin by presenting a general framework that outlines how a person may decide to seek any type of financial help. In the framework, a person progresses through three stages as they make this choice: identifying whether that type of help is available to them, assessing how attractive it would be if they could get it without taking action, and estimating how costly the actions required to seek the help would be. I then apply the framework separately to the formal and informal help types, each time reviewing prior literature on factors that affect people’s (un)willingness to seek that help. Finally, I discuss policy implications, arguing that an understanding of both formal and informal help-seeking processes is crucial for informing policies intended to help financially vulnerable populations.

Abstract: Functional forms of biophysically-realistic neuron models are constrained by neurobiological and anatomical considerations, such as cell morphologies and the presence of known ion channels. Despite these constraints, neuron models still contain unknown static parameters which must be inferred from experiment. This inference task is most readily cast into the framework of state-space models, which systematically takes into account partial observability and measurement noise. Inferring only dynamical state variables such as membrane voltages is a well-studied problem, and has been approached with a wide range of techniques beginning with the well-known Kalman filter. Inferring both states and fixed parameters, on the other hand, is less straightforward. Here, we develop a method for joint parameter and state inference that combines traditional state space modeling with chaotic synchronization and optimal control. Our methods are tailored particularly to situations with considerable measurement noise, sparse observability, very nonlinear or chaotic dynamics, and highly uninformed priors. We illustrate our approach both in a canonical chaotic model and in a phenomenological neuron model, showing that many unknown parameters can be uncovered reliably and accurately from short and noisy observed time traces. Our method holds promise for estimation in larger-scale systems, given ongoing improvements in calcium reporters and genetically-encoded voltage indicators.

Abstract: Abstract. Climate change is modifying river temperature regimes across the world. To apply management interventions in an effective and efficient fashion, it is critical to both understand the underlying processes causing stream warming and identify the streams most and least sensitive to environmental change. Empirical stream thermal sensitivity, defined as the change in water temperature with a single degree change in air temperature, is a useful tool to characterize historical stream temperature conditions and to predict how streams might respond to future climate warming. We measured air and stream temperature across the Snoqualmie and Wenatchee basins, Washington during the years 2014–2021. We used ordinary least squares regression to calculate seasonal summary metrics of thermal sensitivity and time-varying coefficient models to derive continuous estimates of thermal sensitivity for each site. We then applied classification approaches to determine unique thermal sensitivity regimes and, further, to establish a link between environmental covariates and thermal sensitivity regime. We found a diversity of thermal sensitivity responses across our basins that differed in both timing and magnitude of sensitivity. We also found that covariates describing underlying geology and snowmelt were the most important in differentiating clusters. Our findings can be used to inform strategies for river basin restoration and conservation in the context of climate change, such as identifying climate insensitive areas of the basin that should be preserved and protected.