Evaluating integration of letter fragments through contrast and spatially targeted masking.

Four experiments were conducted to gain a better understanding of the visual mechanisms related to how integration of partial shape cues provides for recognition of the full shape. In each experiment, letters formed as outline contours were displayed as a sequence of adjacent segments (fragments), each visible during a 17-ms time frame. The first experiment varied the contrast of the fragments. There were substantial individual differences in contrast sensitivity, so stimulus displays in the masking experiments that followed were calibrated to the sensitivity of each participant. Masks were displayed either as patterns that filled the entire screen (full field) or as successive strips that were sliced from the pattern, each strip lying across the location of the letter fragment that had been shown a moment before. Contrast of masks were varied to be lighter or darker than the letter fragments. Full-field masks, whether light or dark, provided relatively little impairment of recognition, as was the case for mask strips that were lighter than the letter fragments. However, dark strip masks proved to be very effective, with the degree of recognition impairment becoming larger as mask contrast was increased. A final experiment found the strip masks to be most effective when they overlapped the location where the letter fragments had been shown a moment before. They became progressively less effective with increased spatial separation from that location. Results are discussed with extensive reference to potential brain mechanisms for integrating shape cues.

Regression with race-modifiers: towards equity and interpretability.

The pervasive effects of structural racism and racial discrimination are well-established and offer strong evidence that the effects of many important variables on health and life outcomes vary by race. Alarmingly, standard practices for statistical regression analysis introduce racial biases into the estimation and presentation of these race-modified effects. We introduce abundance-based constraints (ABCs) to eliminate these racial biases. ABCs offer a remarkable invariance property: estimates and inference for main effects are nearly unchanged by the inclusion of race-modifiers. Thus, quantitative researchers can estimate race-specific effects "for free"-without sacrificing parameter interpretability, equitability, or statistical efficiency. The benefits extend to prominent statistical learning techniques, especially regularization and selection. We leverage these tools to estimate the joint effects of environmental, social, and other factors on 4th end-of-grade readings scores for students in North Carolina (n = 27, 638) and identify race-modified effects for racial (residential) isolation, PM2.5 exposure, and mother's age at birth.

Semiparametric count data regression for self-reported mental health.

''For how many days during the past 30 days was your mental health not good?" The responses to this question measure self-reported mental health and can be linked to important covariates in the National Health and Nutrition Examination Survey (NHANES). However, these count variables present major distributional challenges: The data are overdispersed, zero-inflated, bounded by 30, and heaped in 5- and 7-day increments. To address these challenges-which are especially common for health questionnaire data-we design a semiparametric estimation and inference framework for count data regression. The data-generating process is defined by simultaneously transforming and rounding (star) a latent Gaussian regression model. The transformation is estimated nonparametrically and the rounding operator ensures the correct support for the discrete and bounded data. Maximum likelihood estimators are computed using an expectation-maximization (EM) algorithm that is compatible with any continuous data model estimable by least squares. star regression includes asymptotic hypothesis testing and confidence intervals, variable selection via information criteria, and customized diagnostics. Simulation studies validate the utility of this framework. Using star regression, we identify key factors associated with self-reported mental health and demonstrate substantial improvements in goodness-of-fit compared to existing count data regression models.

Subset selection for linear mixed models.

Linear mixed models (LMMs) are instrumental for regression analysis with structured dependence, such as grouped, clustered, or multilevel data. However, selection among the covariates-while accounting for this structured dependence-remains a challenge. We introduce a Bayesian decision analysis for subset selection with LMMs. Using a Mahalanobis loss function that incorporates the structured dependence, we derive optimal linear coefficients for (i) any given subset of variables and (ii) all subsets of variables that satisfy a cardinality constraint. Crucially, these estimates inherit shrinkage or regularization and uncertainty quantification from the underlying Bayesian model, and apply for any well-specified Bayesian LMM. More broadly, our decision analysis strategy deemphasizes the role of a single "best" subset, which is often unstable and limited in its information content, and instead favors a collection of near-optimal subsets. This collection is summarized by key member subsets and variable-specific importance metrics. Customized subset search and out-of-sample approximation algorithms are provided for more scalable computing. These tools are applied to simulated data and a longitudinal physical activity dataset, and demonstrate excellent prediction, estimation, and selection ability.

Fast, Optimal, and Targeted Predictions using Parametrized Decision Analysis.

Prediction is critical for decision-making under uncertainty and lends validity to statistical inference. With targeted prediction, the goal is to optimize predictions for specific decision tasks of interest, which we represent via functionals. Although classical decision analysis extracts predictions from a Bayesian model, these predictions are often difficult to interpret and slow to compute. Instead, we design a class of parametrized actions for Bayesian decision analysis that produce optimal, scalable, and simple targeted predictions. For a wide variety of action parametrizations and loss functions-including linear actions with sparsity constraints for targeted variable selection-we derive a convenient representation of the optimal targeted prediction that yields efficient and interpretable solutions. Customized out-of-sample predictive metrics are developed to evaluate and compare among targeted predictors. Through careful use of the posterior predictive distribution, we introduce a procedure that identifies a set of near-optimal, or acceptable targeted predictors, which provide unique insights into the features and level of complexity needed for accurate targeted prediction. Simulations demonstrate excellent prediction, estimation, and variable selection capabilities. Targeted predictions are constructed for physical activity data from the National Health and Nutrition Examination Survey (NHANES) to better predict and understand the characteristics of intraday physical activity.

Bayesian subset selection and variable importance for interpretable prediction and classification.

Subset selection is a valuable tool for interpretable learning, scientific discovery, and data compression. However, classical subset selection is often avoided due to selection instability, lack of regularization, and difficulties with post-selection inference. We address these challenges from a Bayesian perspective. Given any Bayesian predictive model ℳ, we extract a family of near-optimal subsets of variables for linear prediction or classification. This strategy deemphasizes the role of a single "best" subset and instead advances the broader perspective that often many subsets are highly competitive. The acceptable family of subsets offers a new pathway for model interpretation and is neatly summarized by key members such as the smallest acceptable subset, along with new (co-) variable importance metrics based on whether variables (co-) appear in all, some, or no acceptable subsets. More broadly, we apply Bayesian decision analysis to derive the optimal linear coefficients for any subset of variables. These coefficients inherit both regularization and predictive uncertainty quantification via ℳ. For both simulated and real data, the proposed approach exhibits better prediction, interval estimation, and variable selection than competing Bayesian and frequentist selection methods. These tools are applied to a large education dataset with highly correlated covariates. Our analysis provides unique insights into the combination of environmental, socioeconomic, and demographic factors that predict educational outcomes, and identifies over 200 distinct subsets of variables that offer near-optimal out-of-sample predictive accuracy.

Sex-dependent correlates of arterial stiffness in Tanzanian adults.

OBJECTIVE: Arterial stiffness is a known indicator for cardiovascular disease. However, the factors that lead to arterial stiffening have primarily been studied in participants from high-income countries. Here, we examine clinical and lifestyle metrics in relation to arterial stiffness in Tanzanian adults. METHODS: We performed pulse wave velocity (PWV), the gold standard measure of arterial stiffness, on 808 Tanzanian adults (ages 18-65) enrolled in a longitudinal cohort studying trends in blood pressure. RESULTS: As expected, PWV was strongly associated with age, blood pressure and sex. We controlled for these factors in our statistical analysis. Lifestyle metrics were compared across multiple PWV quantiles. We found that determinants of PWV varied by sex: in female participants, PWV was associated with common obesity metrics and menopause, while in male participants, PWV was associated with HIV status and duration of anti-retroviral therapy (ART). Further clinical and lifestyle factors such as marriage status and type of occupation were also significantly associated with PWV and moderated by sex. CONCLUSION: Together, our data demonstrate the importance of studying sex-specific causal pathways for arterial stiffness and of including under-represented populations in these studies.

Bayesian variable selection for understanding mixtures in environmental exposures.

Social and environmental stressors are crucial factors in child development. However, there exists a multitude of measurable social and environmental factors-the effects of which may be cumulative, interactive, or null. Using a comprehensive cohort of children in North Carolina, we study the impact of social and environmental variables on 4th end-of-grade exam scores in reading and mathematics. To identify the essential factors that predict these educational outcomes, we design new tools for Bayesian linear variable selection using decision analysis. We extract a predictive optimal subset of explanatory variables by coupling a loss function with a novel model-based penalization scheme, which leads to coherent Bayesian decision analysis and empirically improves variable selection, estimation, and prediction on simulated data. The Bayesian linear model propagates uncertainty quantification to all predictive evaluations, which is important for interpretable and robust model comparisons. These predictive comparisons are conducted out-of-sample with a customized approximation algorithm that avoids computationally intensive model refitting. We apply our variable selection techniques to identify the joint collection of social and environmental stressors-and their interactions-that offer clear and quantifiable improvements in prediction of reading and mathematics exam scores.

Integer-valued functional data analysis for measles forecasting.

Measles presents a unique and imminent challenge for epidemiologists and public health officials: the disease is highly contagious, yet vaccination rates are declining precipitously in many localities. Consequently, the risk of a measles outbreak continues to rise. To improve preparedness, we study historical measles data both prevaccine and postvaccine, and design new methodology to forecast measles counts with uncertainty quantification. We propose to model the disease counts as an integer-valued functional time series: measles counts are a function of time-of-year and time-ordered by year. The counts are modeled using a negative-binomial distribution conditional on a real-valued latent process, which accounts for the overdispersion observed in the data. The latent process is decomposed using an unknown basis expansion, which is learned from the data, with dynamic basis coefficients. The resulting framework provides enhanced capability to model complex seasonality, which varies dynamically from year-to-year, and offers improved multimonth-ahead point forecasts and substantially tighter forecast intervals (with correct coverage) compared to existing forecasting models. Importantly, the fully Bayesian approach provides well-calibrated and precise uncertainty quantification for epi-relevant features, such as the future value and time of the peak measles count in a given year. An R package is available online.

Mechanical heterogeneities in the subendothelial matrix develop with age and decrease with exercise.

Arterial stiffening occurs with age and is associated with lack of exercise. Notably both age and lack of exercise are major cardiovascular risk factors. While it is well established that bulk arterial stiffness increases with age, more recent data suggest that the intima, the innermost arterial layer, also stiffens during aging. Micro-scale mechanical characterization of individual layers is important because cells primarily sense the matrix that they are in contact with and not necessarily the bulk stiffness of the vessel wall. To investigate the relationship between age, exercise, and subendothelial matrix stiffening, atomic force microscopy was utilized here to indent the subendothelial matrix of the thoracic aorta from young, aged-sedentary, and aged-exercised mice, and elastic modulus values were compared to conventional pulse wave velocity measurements. The subendothelial matrix elastic modulus was elevated in aged-sedentary mice compared to young or aged-exercised mice, and the macro-scale stiffness of the artery was found to linearly correlate with the subendothelial matrix elastic modulus. Notably, we also found that with age, there exists an increase in the point-to-point variations in modulus across the subendothelial matrix, indicating non-uniform stiffening. Importantly, this heterogeneity is reversible with exercise. Given that vessel stiffening is known to cause aberrant endothelial cell behavior, and the spatial heterogeneities we find exist on a length scale much smaller than the size of a cell, these data suggest that further investigation in the heterogeneity of the subendothelial matrix elastic modulus is necessary to fully understand the effects of physiological matrix stiffening on cell function.

Probing the biophysical properties of primary breast tumor-derived fibroblasts.

As cancer progresses, cells must adapt to a new and stiffer environment, which can ultimately alter how normal cells within the tumor behave. In turn, these cells are known to further aid tumor progression. Therefore, there is potentially a unique avenue to better understand metastatic potential through single-cell biophysical assays performed on patient-derived cells. Here, we perform biophysical characterization of primary human fibroblastic cells obtained from mammary carcinoma and normal contralateral tissue. Through a series of tissue dissociation, differential centrifugation and trypsinization steps, we isolate an adherent fibroblastic population viable for biomechanical testing. 2D TFM and 3D migration measurements in a collagen matrix show that fibroblasts obtained from patient tumors generate more traction forces and display improved migration potential than their counterparts from normal tissue. Moreover, through the use of an embedded spheroid model, we confirmed the extracellular matrix (ECM) remodeling behavior of primary cells isolated from carcinoma. Overall, correlating biophysical characterization of normal- and carcinoma-derived samples from individual patient along with patient outcome may become a powerful approach to further our comprehension of metastasis and ultimately design drug targets on a patient-specific basis.