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"Ramirez, Michael"
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AI-powered simulation-based inference of a genuinely spatial-stochastic gene regulation model of early mouse embryogenesis
Understanding how multicellular organisms reliably orchestrate cell-fate decisions is a central challenge in developmental biology, particularly in early mammalian development, where tissue-level differentiation arises from seemingly cell-autonomous mechanisms. In this study, we present a multi-scale, spatial-stochastic simulation framework for mouse embryogenesis, focusing on inner cell mass (ICM) differentiation into epiblast (EPI) and primitive endoderm (PRE) at the blastocyst stage. Our framework models key regulatory and tissue-scale interactions in a biophysically realistic fashion, capturing the inherent stochasticity of intracellular gene expression and intercellular signaling, while efficiently simulating these processes by advancing event-driven simulation techniques. Leveraging the power of Simulation-Based Inference (SBI) through the AI-driven Sequential Neural Posterior Estimation (SNPE) algorithm, we conduct a large-scale Bayesian inferential analysis to identify parameter sets that faithfully reproduce experimentally observed features of ICM specification. Our results reveal mechanistic insights into how the combined action of autocrine and paracrine FGF4 signaling coordinates stochastic gene expression at the cellular scale to achieve robust and reproducible ICM patterning at the tissue scale. We further demonstrate that the ICM exhibits a specific time window of sensitivity to exogenous FGF4, enabling lineage proportions to be adjusted based on timing and dosage, thereby extending current experimental findings and providing quantitative predictions for both mutant and wild-type ICM systems. Notably, FGF4 signaling not only ensures correct EPI-PRE lineage proportions but also enhances ICM resilience to perturbations, reducing fate-proportioning errors by 10-20% compared to a purely cell-autonomous system. Additionally, we uncover a surprising role for variability in intracellular initial conditions, showing that high gene-expression heterogeneity can improve both the accuracy and precision of cell-fate proportioning, which remains robust when fewer than 25% of the ICM population experiences perturbed initial conditions. Our work offers a comprehensive, spatial-stochastic description of the biochemical processes driving ICM differentiation and identifies the necessary conditions for its robust unfolding. It also provides a framework for future exploration of similar spatial-stochastic systems in developmental biology.
Journal Article
Diverse drug-resistance mechanisms can emerge from drug-tolerant cancer persister cells
Cancer therapy has traditionally focused on eliminating fast-growing populations of cells. Yet, an increasing body of evidence suggests that small subpopulations of cancer cells can evade strong selective drug pressure by entering a ‘persister’ state of negligible growth. This drug-tolerant state has been hypothesized to be part of an initial strategy towards eventual acquisition of bona fide drug-resistance mechanisms. However, the diversity of drug-resistance mechanisms that can expand from a persister bottleneck is unknown. Here we compare persister-derived, erlotinib-resistant colonies that arose from a single, EGFR-addicted lung cancer cell. We find, using a combination of large-scale drug screening and whole-exome sequencing, that our erlotinib-resistant colonies acquired diverse resistance mechanisms, including the most commonly observed clinical resistance mechanisms. Thus, the drug-tolerant persister state does not limit—and may even provide a latent reservoir of cells for—the emergence of heterogeneous drug-resistance mechanisms.
Cancer cells that survive initial drug treatment can persist in the presence of drugs. Here, the authors generate persister cells that are resistant to the EGFR tyrosine kinase inhibitor erlotinib and show by single cell analysis that multiple mechanism give rise to the drug-resistant persister state.
Journal Article
Biomarkers of Immersion in Virtual Reality Based on Features Extracted from the EEG Signals: A Machine Learning Approach
Virtual reality (VR) enables the development of virtual training frameworks suitable for various domains, especially when real-world conditions may be hazardous or impossible to replicate because of unique additional resources (e.g., equipment, infrastructure, people, locations). Although VR technology has significantly advanced in recent years, methods for evaluating immersion (i.e., the extent to which the user is engaged with the sensory information from the virtual environment or is invested in the intended task) continue to rely on self-reported questionnaires, which are often administered after using the virtual scenario. Having an objective method to measure immersion is particularly important when using VR for training, education, and applications that promote the development, fine-tuning, or maintenance of skills. The level of immersion may impact performance and the translation of knowledge and skills to the real-world. This is particularly important in tasks where motor skills are combined with complex decision making, such as surgical procedures. Efforts to better measure immersion have included the use of physiological measurements including heart rate and skin response, but so far they do not offer robust metrics that provide the sensitivity to discriminate different states (idle, easy, and hard), which is critical when using VR for training to determine how successful the training is in engaging the user’s senses and challenging their cognitive capabilities. In this study, electroencephalography (EEG) data were collected from 14 participants who completed VR jigsaw puzzles with two different levels of task difficulty. Machine learning was able to accurately classify the EEG data collected during three different states, obtaining accuracy rates of 86% and 97% for differentiating easy versus hard difficulty states and baseline vs. VR states. Building on these results may enable the identification of robust biomarkers of immersion in VR, enabling real-time recognition of the level of immersion that can be used to design more effective and translative VR-based training. This method has the potential to adjust aspects of VR related to task difficulty to ensure that participants are immersed in VR.
Journal Article
Ecosystem carbon density and allocation across a chronosequence of longleaf pine forests
Forests can partially offset greenhouse gas emissions and contribute to climate change mitigation, mainly through increases in live biomass. We quantified carbon (C) density in 20 managed longleaf pine (Pinus palustris Mill.) forests ranging in age from 5 to 118 years located across the southeastern United States and estimated above- and belowground trajectories. Ecosystem C stock (all pools including soil C) and aboveground live tree C increased nonlinearly with stand age and the modeled asymptotic maxima were 168 Mg C/ha and 80 Mg C/ha, respectively. Accumulation of ecosystem C with stand age was driven mainly by increases in aboveground live tree C, which ranged from <1 Mg C/ha to 74 Mg C/ha and comprised <1% to 39% of ecosystem C. Live root C (sum of below-stump C, ground penetrating radar measurement of lateral root C, and live fine root C) increased with stand age and represented 4-22% of ecosystem C. Soil C was related to site index, but not to stand age, and made up 39-92% of ecosystem C. Live understory C, forest floor C, downed dead wood C, and standing dead wood C were small fractions of ecosystem C in these frequently burned stands. Stand age and site index accounted for 76% of the variation in ecosystem C among stands. The mean root-to-shoot ratio calculated as the average across all stands (excluding the grass-stage stand) was 0.54 (standard deviation of 0.19) and higher than reports for other conifers. Long-term accumulation of live tree C, combined with the larger role of belowground accumulation of lateral root C than in other forest types, indicates a role of longleaf pine forests in providing disturbance-resistant C storage that can balance the more rapid C accumulation and C removal associated with more intensively managed forests. Although other managed southern pine systems sequester more C over the short-term, we suggest that longleaf pine forests can play a meaningful role in regional forest C management.
Journal Article
Comparing AI versus optimization workflows for simulation-based inference of spatial-stochastic systems
Model parameter inference is a universal problem across science. This challenge is particularly pronounced in developmental biology, where faithful mechanistic descriptions require spatial-stochastic models with numerous parameters, yet quantitative empirical data often lack sufficient granularity due to experimental limitations. Parameterizing such complex models therefore necessitates methods that elaborate on classical Bayesian inference by incorporating notions of optimality and goal-orientation through low-dimensional objective functions that quantitatively encapsulate target system behavior. In this study, we contrast two such inference workflows and apply them to biophysically inspired spatial-stochastic models. Technically, both workflows employ simulation-based inference (SBI) methods: the first leverages a modern deep-learning technique known as sequential neural posterior estimation, while the second relies on a classical optimization technique called simulated annealing. We evaluate these workflows by inferring the parameters of two complementary models for the inner cell mass (ICM) lineage differentiation in the blastocyst-stage mouse embryo. This developmental biology system serves as a paradigmatic example of a highly robust and reproducible cell-fate proportioning process that self-organizes under strongly stochastic conditions, such as intrinsic biochemical noise and cell–cell signaling delays. Our results reveal that while both methods provide consistent model parameter estimates, the modern SBI workflow yields significantly richer inferred distributions at an equivalent computational cost. We identify the computational scenarios that favor the modern SBI method over its classical counterpart, and propose a plausible strategy to exploit the complementary strengths of both workflows for enhanced parameter space exploration.
Journal Article
Soil moisture and vapor pressure deficit controls of longleaf pine physiology: results from a throughfall reduction study
Key messageLongleaf pine demonstrated general resistance to reduced soil moisture and increased VPD, but results highlight the soil and atmospheric conditions that could trigger declines in longleaf pine function and productivity.Low soil moisture and high atmospheric vapor pressure deficit (VPD) independently limit tree function and forest productivity. However, questions remain about how large, established trees respond to dry soil and high VPD over longer time periods. We carried out a 3-year throughfall reduction experiment in a young (12–14-year-old) longleaf pine plantation in west Georgia (USA). We hypothesized that throughfall reduction would reduce soil moisture, leaf-scale stomatal conductance (gs), and net photosynthesis (Pnet), but increase intrinsic water-use efficiency (iWUE). We also hypothesized that throughfall reduction would reduce canopy conductance (Gs) at a reference VPD of 1 kPa and Gs sensitivity to VPD. In addition, we used Gs data collected across both treatments to identify breakpoints in the relative control of soil moisture and VPD on Gs. Throughfall reduction decreased soil moisture and caused small reductions in gs ( – 21%) and Pnet ( – 13%), but no change in iWUE. As expected, reduced throughfall decreased Gs and Gs sensitivity to VPD by 20 and 8%, respectively. Despite this, throughfall reduction had very little effect on tree growth or forest productivity. Importantly, Gs sensitivity to VPD was similar at intermediate soil moisture, but highest and lowest at soil moistures above field capacity and below the permanent wilting point, respectively. Consequently, we could identify thresholds in the relative control of soil moisture and VPD over Gs. These results demonstrate the general resistance of longleaf pine plantations to reduced soil moisture and increased VPD but highlight the soil and atmospheric conditions that could trigger declines in longleaf pine function and productivity.
Journal Article
A Machine Learning Approach to Classifying EEG Data Collected with or without Haptic Feedback during a Simulated Drilling Task
Artificial Intelligence (AI), computer simulations, and virtual reality (VR) are increasingly becoming accessible tools that can be leveraged to implement training protocols and educational resources. Typical assessment tools related to sensory and neural processing associated with task performance in virtual environments often rely on self-reported surveys, unlike electroencephalography (EEG), which is often used to compare the effects of different types of sensory feedback (e.g., auditory, visual, and haptic) in simulation environments in an objective manner. However, it can be challenging to know which aspects of the EEG signal represent the impact of different types of sensory feedback on neural processing. Machine learning approaches offer a promising direction for identifying EEG signal features that differentiate the impact of different types of sensory feedback during simulation training. For the current study, machine learning techniques were applied to differentiate neural circuitry associated with haptic and non-haptic feedback in a simulated drilling task. Nine EEG channels were selected and analyzed, extracting different time-domain, frequency-domain, and nonlinear features, where 360 features were tested (40 features per channel). A feature selection stage identified the most relevant features, including the Hurst exponent of 13–21 Hz, kurtosis of 21–30 Hz, power spectral density of 21–30 Hz, variance of 21–30 Hz, and spectral entropy of 13–21 Hz. Using those five features, trials with haptic feedback were correctly identified from those without haptic feedback with an accuracy exceeding 90%, increasing to 99% when using 10 features. These results show promise for the future application of machine learning approaches to predict the impact of haptic feedback on neural processing during VR protocols involving drilling tasks, which can inform future applications of VR and simulation for occupational skill acquisition.
Journal Article
Multimodal artificial intelligence and online learning in youth mental health: a scoping review
Youth mental health-related problems and disorders have garnered increased attention due to global prevalence estimates that have, in some cases, increased following the COVID-19 pandemic. Various methodologies have been proposed to leverage artificial intelligence (AI) for detecting mental health problems in the general population; however, research specifically focused on AI methods for youth remains limited. Shortcomings in modern AI include limited training data modalities (i.e., types of input data used for model training), reliance on offline training, and the use of static models. This scoping review provides an overview of evidence that uses AI methods applied to youth mental health (YMH) and provides an assessment of the current state of research that integrates multimodal AI (i.e., models that incorporate multiple data modalities) and/or online learning (i.e., incremental or continual model training from streaming data) for the diagnosis, monitoring, and treatment of YMH-related problems. The findings indicate that research in AI applied to YMH is limited in the areas of multimodal AI and online learning. The number of studies in this field is steadily growing. Studies incorporating online learning demonstrate that this approach enhances model performance and adaptability, which is crucial for developing translational models capable of addressing real-world challenges effectively. Despite these advances, key challenges remain, including the availability and long-term validity of multimodal data, the lack of participant-related information in certain databases and studies, the ethical and logistical difficulties of collecting data from minors, and the computational costs of training robust AI models.
Journal Article