Scholarly Works, Statistics

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Machine Learning–Based Prediction and Optimization of Balanced Mixture Design Performance Indices
Tong, Bilin; Huang, Wenjiang; Habbouche, Jhony; Boz, Ilker; Guo, Qing; Diefenderfer, Stacey D.; Flintsch, Gerardo W. (SAGE Publications, 2025-04-26)
The balanced mix design (BMD) concept is an emerging methodology that facilitates the design of engineered asphalt mixtures. This approach is particularly beneficial for mixtures containing conventional and high reclaimed asphalt pavement, for which the traditional volumetric design methods may fail to effectively address the performance characteristics. However, given production variability, these engineered mixtures can still fail to meet the required thresholds. Additionally, identifying the cause of this imbalance is challenging. To maximize the benefits of BMD implementation, this study introduces machine learning (ML) algorithms including linear regression (LR), random forest (RF), extreme gradient boosting (XGB), and support vector regression (SVR) as strategic tools to predict mixtures’ BMD performance indices. 648 specimens fabricated for quality acceptance as part of the 2020 Virginia Accelerated Pavement Testing Program is used for the modeling and analysis. The durability, cracking, and rutting susceptibility of the specimens were evaluated using the Cantabro test, the indirect tensile cracking test (IDT-CT), and the asphalt pavement analyzer (APA) rut test. Key outcomes include: a) ML models, including RF, XGB, and SVR, demonstrated superior performance compared with LR; b) feature importance analysis from ML models identified dominant factors for each BMD test, also highlighting the reheating process; and c) a pseudo in situ deployment was simulated to optimize BMD implementation. The dimensionality reduction analysis—uniform manifold approximation and projection—highlighted the practical challenges associated with concurrently improving multiple performance metrics. Ultimately, the pivotal role of ML in advancing both the design and production phases was emphasized.
Age Differences in the Required Coefficient of Friction During Level Walking Do Not Exist When Experimentally-Controlling Speed and Step Length
Anderson, Dennis E.; Franck, Christopher T.; Madigan, Michael L. (Human Kinetics, 2014-06-30)
The effects of gait speed and step length on the required coefficient of friction (COF) confound the investigation of age-related differences in required COF. The goals of this study were to investigate whether age differences in required COF during self-selected gait persist when experimentally-controlling speed and step length, and to determine the independent effects of speed and step length on required COF. Ten young and 10 older healthy adults performed gait trials under five gait conditions: self-selected, slow and fast speeds without controlling step length, and slow and fast speeds while controlling step length. During self-selected gait, older adults walked with shorter step lengths and exhibited a lower required COF. Older adults also exhibited a lower required COF when walking at a controlled speed without controlling step length. When both age groups walked with the same speed and step length, no age difference in required COF was found. Thus, speed and step length can have a large influence on studies investigating age-related differences in required COF. It was also found that speed and step length have independent and opposite effects on required COF, with step length having a strong positive effect on required COF, and speed having a weaker negative effect.
Comparison of Treadmill Trip-Like Training Versus Tai Chi to Improve Reactive Balance Among Independent Older Adult Residents of Senior Housing: A Pilot Controlled Trial
Aviles, Jessica; Allin, Leigh J.; Alexander, Neil B.; Van Mullekom, Jennifer; Nussbaum, Maury A.; Madigan, Michael L. (Oxford University Press, 2018-12-21)
Background: There is growing interest in using perturbation-based balance training to improve the reactive response to common perturbations (eg, tripping and slipping). The goal of this study was to compare the efficacy of treadmill-based reactive balance training versus Tai Chi performed at, and among independent residents of, older adult senior housing. Methods: Thirty-five residents from five senior housing facilities were allocated to either treadmill-based reactive balance training or Tai Chi training. Both interventions were performed three times per week for 4 weeks, with each session lasting approximately 30 minutes. A battery of balance tests was performed at baseline, and again 1 week, 1 month, 3 months, and 6 months post-training. The battery included six standard clinical tests of balance and mobility, and a test of reactive balance performance. Results: At baseline, no significant between-group differences were found for any balance tests. After training, reactive balance training participants had better reactive balance than Tai Chi participants. Maximum trunk angle was 13.5 smaller among reactive balance training participants 1 week after training (p =. 01), and a reactive balance rating was 24%-31% higher among reactive balance training participants 1 week to 6 months after training (p <. 03). Clinical tests showed minimal differences between groups at any time point after training. Conclusion: Trip-like reactive balance training performed at senior housing facilities resulted in better rapid balance responses compared with Tai Chi training.
Approximate Bayesian Techniques for Statistical Model Selection and Quantifying Model Uncertainty-Application to a Gait Study
Franck, Christopher T.; Arena, Sara L.; Madigan, Michael L. (Springer, 2022-08-20)
Frequently, biomedical researchers need to choose between multiple candidate statistical models. Several techniques exist to facilitate statistical model selection including adjusted R2, hypothesis testing and p-values, and information criteria among others. One particularly useful approach that has been slow to permeate the biomedical literature is the notion of posterior model probabilities. A major advantage of posterior model probabilities is that they quantify uncertainty in model selection by providing a direct, probabilistic comparison among competing models as to which is the “true” model that generated the observed data. Additionally, posterior model probabilities can be used to compute posterior inclusion probabilities which quantify the probability that individual predictors in a model are associated with the outcome in the context of all models considered given the observed data. Posterior model probabilities are typically derived from Bayesian statistical approaches which require specialized training to implement, but in this paper we describe an easy-to-compute version of posterior model probabilities and inclusion probabilities that rely on the readily-available Bayesian information criterion. We illustrate the utility of posterior model probabilities and inclusion probabilities by re-analyzing data from a published gait study investigating factors that predict required coefficient of friction between the shoe sole and floor while walking.
Inertial measurement units worn on the dorsum of the foot and proximal to the ankle can provide valid slip recovery measures
Morris, Michelle A.; Franck, Christopher T.; Madigan, Michael L. (Elsevier, 2024-11-03)
Background: Slips are a leading cause of injury among older adults. Slip recovery measures are often captured using optoelectronic motion capture (OMC) systems that can be costly and typically require a laboratory setting. Inertial measurement unit (IMU) systems show promise as a lower cost, portable, and wearable form of motion capture. Question: Can IMUs worn on the dorsum of the feet and proximal to the ankles be used to capture valid slip recovery measures? Methods: Thirty older adults (ages 65–80; 18 females) were exposed to a laboratory slip while wearing OMC markers, IMUs on the dorsum of the feet, and IMUs proximal to the ankles. To evaluate the concurrent validity of IMU-based slip recovery measures using the OMC-based measures as our standard, we determined whether the IMU-based slip recovery measures differed between falls and recoveries, and evaluated the strength of correlation between IMU-based measures and OMC. We also defined the difference between foot IMU-based and OMC-based slip recovery measures to be the system offset, and compared the system offset variance between participant-placed IMUs and researcher-placed IMUs. Results: All IMU-based and OMC-based slip recovery measures differed between falls and recoveries (p ≤ 0.008), and all IMU-based measures exhibited strong correlation (r ≥ 0.94) with OMC-based measures. The system offset variance was larger when foot IMUs were participant-placed than when researcher-placed for anterior-posterior slip distance (p = 0.032), but not other slip recovery measures (p ≥ 0.054). Significance: IMUs worn on the dorsum of the feet and proximal to the ankle can provide valid slip recovery measures in a laboratory setting. This includes IMUs placed by participants on the dorsum of the feet that might be needed for the long-term monitoring of these measures by participants outside the laboratory setting.
Low responsiveness of machine learning models to critical or deteriorating health conditions
Pias, Tanmoy Sarkar; Afrose, Sharmin; Tuli, Moon Das; Trisha, Ipsita Hamid; Deng, Xinwei; Nemeroff, Charles B.; Yao, Danfeng Daphne (Springer Nature, 2025-03-11)
Background: Machine learning (ML) based mortality prediction models can be immensely useful in intensive care units. Such a model should generate warnings to alert physicians when a patient’s condition rapidly deteriorates, or their vitals are in highly abnormal ranges. Before clinical deployment, it is important to comprehensively assess a model’s ability to recognize critical patient conditions. Methods: We develop multiple medical ML testing approaches, including a gradient ascent method and neural activation map. We systematically assess these machine learning models’ ability to respond to serious medical conditions using additional test cases, some of which are time series. Guided by medical doctors, our evaluation involves multiple machine learning models, resampling techniques, and four datasets for two clinical prediction tasks. Results: We identify serious deficiencies in the models’ responsiveness, with the models being unable to recognize severely impaired medical conditions or rapidly deteriorating health. For in-hospital mortality prediction, the models tested using our synthesized cases fail to recognize 66% of the injuries. In some instances, the models fail to generate adequate mortality risk scores for all test cases. Our study identifies similar kinds of deficiencies in the responsiveness of 5-year breast and lung cancer prediction models. Conclusions: Using generated test cases, we find that statistical machine-learning models trained solely from patient data are grossly insufficient and have many dangerous blind spots. Most of the ML models tested fail to respond adequately to critically ill patients. How to incorporate medical knowledge into clinical machine learning models is an important future research direction.
A framework for developing a real-time lake phytoplankton forecasting system to support water quality management in the face of global change
Carey, Cayelan C.; Calder, Ryan S. D.; Figueiredo, Renato J.; Gramacy, Robert B.; Lofton, Mary E.; Schreiber, Madeline E.; Thomas, R. Quinn (Springer, 2024-09-20)
Phytoplankton blooms create harmful toxins, scums, and taste and odor compounds and thus pose a major risk to drinking water safety. Climate and land use change are increasing the frequency and severity of blooms, motivating the development of new approaches for preemptive, rather than reactive, water management. While several real-time phytoplankton forecasts have been developed to date, none are both automated and quantify uncertainty in their predictions, which is critical for manager use. In response to this need, we outline a framework for developing the first automated, real-time lake phytoplankton forecasting system that quantifies uncertainty, thereby enabling managers to adapt operations and mitigate blooms. Implementation of this system calls for new, integrated ecosystem and statistical models; automated cyberinfrastructure; effective decision support tools; and training for forecasters and decision makers. We provide a research agenda for the creation of this system, as well as recommendations for developing real-time phytoplankton forecasts to support management.
Augmenting a Simulation Campaign for Hybrid Computer Model and Field Data Experiments
Koermer, Scott; Loda, Justin; Noble, Aaron; Gramacy, Robert B. (Taylor & Francis, 2024-05-24)
The Kennedy and O’Hagan (KOH) calibration framework uses coupled Gaussian processes (GPs) to meta-model an expensive simulator (first GP), tune its “knobs” (calibration inputs) to best match observations from a real physical/field experiment and correct for any modeling bias (second GP) when predicting under new field conditions (design inputs). There are well-established methods for placement of design inputs for data-efficient planning of a simulation campaign in isolation, that is, without field data: space-filling, or via criterion like minimum integrated mean-squared prediction error (IMSPE). Analogues within the coupled GP KOH framework are mostly absent from the literature. Here we derive a closed form IMSPE criterion for sequentially acquiring new simulator data for KOH. We illustrate how acquisitions space-fill in design space, but concentrate in calibration space. Closed form IMSPE precipitates a closed-form gradient for efficient numerical optimization. We demonstrate that our KOH-IMSPE strategy leads to a more efficient simulation campaign on benchmark problems, and conclude with a showcase on an application to equilibrium concentrations of rare earth elements for a liquid–liquid extraction reaction.
Semiparametric change points detection using single index spatial random effects model in environmental epidemiology study
Mahmoud, Hamdy F. F.; Kim, Inyoung (Public Library of Science, 2024-12-12)
Environmental health studies are of great interest in research to evaluate the mortality-temperature relationship by adjusting spatially correlated random effects as well as identifying significant change points in temperature. However, this relationship is often not expressed using parametric models, which makes identifying change points an even more challenging problem. This paper proposes a unified semiparametric approach to simultaneously identify the nonlinear mortality-temperature relationship and detect spatially-dependent change points. A unified method is proposed for the model estimation, spatially dependent change points detection, and testing whether they are significant simultaneously by a permutation-based test. We operate under the assumption that change points remain constant, yet acknowledge the uncertainty regarding their precise number. These change points are influenced by the smoothing of an unknown function, which in turn relies on a smoothing variable and spatial random effects. Consequently, the detection of change points may be influenced by spatial effects. In this paper, several simulation studies are conducted to evaluate the performance of our proposed approach. The advantages of this unified approach are demonstrated using epidemiological data on mortality and temperature.
Plasma SOMAmer proteomics of postoperative delirium
Leung, Jacqueline M.; Rojas, Julio C.; Sands, Laura P.; Chan, Brandon; Rajbanshi, Binita; Du, Zhiyuan; Du, Pang (Wiley, 2024-02-12)
Background: Postoperative delirium is prevalent in older adults and has been shown to increase the risk of long-term cognitive decline. Plasma biomarkers to identify the risk for postoperative delirium and the risk of Alzheimer's disease and related dementias are needed. Methods: This biomarker discovery case–control study aimed to identify plasma biomarkers associated with postoperative delirium. Patients aged ≥65 years undergoing major elective noncardiac surgery were recruited. The preoperative plasma proteome was interrogated with SOMAmer-based technology targeting 1433 biomarkers. Results: In 40 patients (20 with vs. 20 without postoperative delirium), a preoperative panel of 12 biomarkers discriminated patients with postoperative delirium with an accuracy of 97.5%. The final model of five biomarkers delivered a leave-one-out cross-validation accuracy of 80%. Represented biological pathways included lysosomal and immune response functions. Conclusion: In older patients who have undergone major surgery, plasma SOMAmer proteomics may provide a relatively non-invasive benchmark to identify biomarkers associated with postoperative delirium.
Identifying Barriers and Bridging Gaps Between Researchers and Decision Makers in Water Quality Modeling
Chowdhury, Mahabub; Carey, Cayelan C.; Figueiredo, Renato; Gramacy, Robert; Hoffman, Kathryn; Lofton, Mary; Patil, Parul; Schreiber, Madeline; Thomas, R. Quinn; Calder, Ryan S. D. (2024-12-12)
Predicting patients with septic shock and sepsis through analyzing whole-blood expression of NK cell-related hub genes using an advanced machine learning framework
Du, Chao; Tan, Stephanie C.; Bu, Heng-Fu; Subramanian, Saravanan; Geng, Hua; Wang, Xiao; Xie, Hehuang; Wu, Xiaowei; Zhou, Tingfa; Liu, Ruijin; Xu, Zhen; Liu, Bing; Tan, Xiao-Di (Frontiers, 2024-11-28)
Background: Sepsis is a life-threatening condition that causes millions of deaths globally each year. The need for biomarkers to predict the progression of sepsis to septic shock remains critical, with rapid, reliable methods still lacking. Transcriptomics data has recently emerged as a valuable resource for disease phenotyping and endotyping, making it a promising tool for predicting disease stages. Therefore, we aimed to establish an advanced machine learning framework to predict sepsis and septic shock using transcriptomics datasets with rapid turnaround methods. Methods: We retrieved four NCBI GEO transcriptomics datasets previously generated from peripheral blood samples of healthy individuals and patients with sepsis and septic shock. The datasets were processed for bioinformatic analysis and supplemented with a series of bench experiments, leading to the identification of a hub gene panel relevant to sepsis and septic shock. The hub gene panel was used to establish a novel prediction model to distinguish sepsis from septic shock through a multistage machine learning pipeline, incorporating linear discriminant analysis, risk score analysis, and ensemble method combined with Least Absolute Shrinkage and Selection Operator analysis. Finally, we validated the prediction model with the hub gene dataset generated by RT-qPCR using peripheral blood samples from newly recruited patients. Results: Our analysis led to identify six hub genes (GZMB, PRF1, KLRD1, SH2D1A, LCK, and CD247) which are related to NK cell cytotoxicity and septic shock, collectively termed 6-HubGss. Using this panel, we created SepxFindeR, a machine learning model that demonstrated high accuracy in predicting sepsis and septic shock and distinguishing septic shock from sepsis in a cross-database context. Remarkably, the SepxFindeR model proved compatible with RT-qPCR datasets based on the 6-HubGss panel, facilitating the identification of newly recruited patients with sepsis and septic shock. Conclusions: Our bioinformatic approach led to the discovery of the 6-HubGss biomarker panel and the development of the SepxFindeR machine learning model, enabling accurate prediction of septic shock and distinction from sepsis with rapid processing capabilities.
Near-term ecological forecasting for climate change action
Dietze, Michael; White, Ethan P.; Abeyta, Antoinette; Boettiger, Carl; Bueno Watts, Nievita; Carey, Cayelan C.; Chaplin-Kramer, Rebecca; Emanuel, Ryan E.; Ernest, S. K. Morgan; Figueiredo, Renato J.; Gerst, Michael D.; Johnson, Leah R.; Kenney, Melissa A.; McLachlan, Jason S.; Paschalidis, Ioannis Ch.; Peters, Jody A.; Rollinson, Christine R.; Simonis, Juniper; Sullivan-Wiley, Kira; Thomas, R. Quinn; Wardle, Glenda M.; Willson, Alyssa M.; Zwart, Jacob (Springer Nature, 2024-11-08)
A substantial increase in predictive capacity is needed to anticipate and mitigate the widespread change in ecosystems and their services in the face of climate and biodiversity crises. In this era of accelerating change, we cannot rely on historical patterns or focus primarily on long-term projections that extend decades into the future. In this Perspective, we discuss the potential of near-term (daily to decadal) iterative ecological forecasting to improve decision-making on actionable time frames. We summarize the current status of ecological forecasting and focus on how to scale up, build on lessons from weather forecasting, and take advantage of recent technological advances. We also highlight the need to focus on equity, workforce development, and broad cross-disciplinary and non-academic partnerships.
Selective Reduction of Socioeconomic Disparities in the Experimental Tobacco Marketplace: Effects of Cigarette and E-cigarette Flavor Restrictions
Freitas-Lemos, Roberta; Tegge, Allison N.; Shevorykin, Alina; Tomlinson, Devin C.; Athamneh, Liqa N.; Stein, Jeffrey S.; Sheffer, Christine E.; Shields, Peter G.; Hatsukami, Dorothy K. (Oxford University Press, 2024-06)
Introduction: Cigarette smoking accounts for >30% of the socioeconomic gap in life expectancy. Flavored restrictions claim to promote equity; however, no previous studies have compared the effect of cigarette and e-cigarette flavor restrictions among individuals who smoke with lower and higher socioeconomic status (SES). Aims and Methods: In a between-group within-subject design, individuals with lower (n = 155) and higher (n = 125) SES completed hypothetical purchasing trials in the experimental tobacco marketplace (ETM). Conditions were presented in a 2 × 2 factorial design (cigarette flavors restricted or unrestricted and e-cigarette flavors restricted or unrestricted) with increasing cigarette prices across trials. Results: Results show (1) SES differences in cigarette, e-cigarette, and NRT purchases under unrestricted policies, with lower SES showing higher cigarette demand and lower e-cigarette and NRT substitution than higher SES, (2) cigarette restrictions decreased cigarette and increased NRT purchases among lower SES, but no significant changes among higher SES, (3) decreased SES differences in cigarette demand under cigarette restrictions, but persistence under e-cigarette restrictions or their combination, (4) persistence of SES differences in e-cigarette purchases when all restrictions were enforced, and (5) waning of SES differences in NRT purchasing under all restrictions. Conclusions: Flavor restrictions differentially affected individuals based on SES. Within-group comparisons demonstrated restrictions significantly impacted lower SES, but not higher SES. Between-group comparisons showed SES differences in cigarette purchasing decreased under cigarette restrictions, but persisted under e-cigarette-restrictions or their combination. Additionally, SES differences in NRT substitution decreased under flavor restrictions. These findings highlight the utility of the ETM to investigate SES disparities. Implications: With increasing trends of socioeconomic differences in smoking prevalence and cessation rates, smoking-related health disparities are expected to continue to widen. Restricting menthol flavor in cigarettes while enhancing the availability and affordability of NRT have the potential to alleviate SES disparities in tobacco use, therefore, positively impacting health equity. However, this effect may depend on flavor availability in other tobacco products.
Local knowledge reconstructs historical resource use
Castello, Leandro; Martins, Eduardo G.; Sorice, Michael G.; Smith, Eric P.; Almedia, Morgana; Bastos, Gastao C.C.; Gardoso, Luis G.; Clauzet, Mariana; Dopona, Alisson P.; Ferreira, Beatrice; Haimovic, Manuel; Jorge, Marcelo; Mendonça, Jocemar; Ávila-da- Silva, Antonio O.; Roman, Ana P.O.; Ramires, Milena; de Miranda, Laura V.; Lopes, Priscila F.M. (Wiley, 2024-03-07)
Information on natural resource exploitation is vital for conservation but scarce in developing nations, which encompass most of the world and often lack the capacity to produce it. A growing approach to generate information about resource use in the context of developing nations relies on surveys of resource users about their recollections (recall) of past harvests. However, the reliability of harvest recalls remains unclear. Here, we show that harvest recalls can be as accurate to data collected by standardized protocols, despite that recalls are variable and affected by the age of the recollecting person and the length of time elapsed since the event. Samples of harvest recalls permit relatively reliable reconstruction of harvests for up to 39 years in the past. Harvest recalls therefore have strong potential to inform data-poor resource systems and curb shifting baselines around the world at a fraction of the cost of conventional approaches.
Learning Common Knowledge Networks Via Exponential Random Graph Models
Liu, Xueying; Hu, Zhihao; Deng, Xinwei; Kuhlman, Chris (ACM, 2023-11-06)
Common knowledge (CK) is a phenomenon where each individual within a group knows the same information and everyone knows that everyone knows the information, infinitely recursively. CK spreads information as a contagion through social networks in ways different from other models like susceptibleinfectious- recovered (SIR) model. In a model of CK on Facebook, the biclique serves as the characterizing graph substructure for generating CK, as all nodes within a biclique share CK through their walls. To understand the effects of network structure on CKbased contagion, it is necessary to control the numbers and sizes of bicliques in networks. Thus, learning how to generate these CK networks (CKNs) is important. Consequently, we develop an exponential random graph model (ERGM) that constructs networks while controlling for bicliques. Our method offers powerful prediction and inference, reduces computational costs significantly, and has proven its merit in contagion dynamics through numerical experiments.
Antibiotic exposure is associated with decreased risk of psychiatric disorders
Kerman, Ilan A.; Glover, Matthew E.; Lin, Yezhe; West, Jennifer L.; Hanlon, Alexandra L.; Kablinger, Anita S.; Clinton, Sarah M. (Frontiers, 2024-01-08)
Objective: This study sought to investigate the relationship between antibiotic exposure and subsequent risk of psychiatric disorders. Methods: This retrospective cohort study used a national database of 69 million patients from 54 large healthcare organizations. We identified a cohort of 20,214 (42.5% male; 57.9 ± 15.1 years old [mean ± SD]) adults without prior neuropsychiatric diagnoses who received antibiotics during hospitalization. Matched controls included 41,555 (39.6% male; 57.3 ± 15.5 years old) hospitalized adults without antibiotic exposure. The two cohorts were balanced for potential confounders, including demographics and variables with potential to affect: the microbiome, mental health, medical comorbidity, and overall health status. Data were stratified by age and by sex, and outcome measures were assessed starting 6 months after hospital discharge. Results: Antibiotic exposure was consistently associated with a significant decrease in the risk of novel mood disorders and anxiety and stressor-related disorders in: men (mood (OR 0.84, 95% CI 0.77, 0.91), anxiety (OR 0.88, 95% CI 0.82, 0.95), women (mood (OR 0.94, 95% CI 0.89,1.00), anxiety (OR 0.93, 95% CI 0.88, 0.98), those who are 26–49 years old (mood (OR 0.87, 95% CI 0.80, 0.94), anxiety (OR 0.90, 95% CI 0.84, 0.97)), and in those ≥50 years old (mood (OR 0.91, 95% CI 0.86, 0.97), anxiety (OR 0.92, 95% CI 0.87, 0.97). Risk of intentional harm and suicidality was decreased in men (OR 0.73, 95% CI 0.55, 0.98) and in those ≥50 years old (OR 0.67, 95% CI 0.49, 0.92). Risk of psychotic disorders was also decreased in subjects ≥50 years old (OR 0.83, 95 CI: 0.69, 0.99). Conclusion: Use of antibiotics in the inpatient setting is associated with protective effects against multiple psychiatric outcomes in an age- and sex-dependent manner.
Effects of establishment fertilization on Landsat-assessed leaf area development of loblolly pine stands
House, Matthew N.; Wynne, Randolph H.; Thomas, Valerie A.; Cook, Rachel L.; Carter, David R.; Van Mullekom, Jennifer H.; Rakestraw, Jim; Schroeder, Todd A. (Elsevier, 2024-03-15)
Loblolly pine (Pinus taeda L.) plantations in the southeastern United States are among the world's most intensively managed forest plantations. Under intensive management, a common practice is fertilizing at establishment. The objective of this study was to investigate the effect of establishment fertilization on leaf area development of loblolly pine plantation stands (n = 3997) over 16 years compared to stands that did not receive nutrient additions at planting. Leaf area index (LAI) is a meaningful biophysical indicator of vigor and an important functional and structural element of a planted stand. The study area was stratified by plant hardiness zone to account for climatic differences and soil type (texture and drainage class), using the Cooperative Research in Forest Fertilization (CRIFF) groupings. LAI was estimated from Landsat imagery to create trajectories of mean stand LAI over 16 years. Establishment fertilization, on average, (1) increased stand LAI beginning at year two, with a peak at years six and seven, and (2) decreased the time required for a stand to reach a winter LAI of 1.5 by almost two years. Fertilization responses varied by climate zone and soil drainage class, where the warmest zones benefited the most, particularly in poorly drained soils. Past year 10, the differences in LAI between fertilized and unfertilized stands were not practically important. Using Landsat data in a cloud-computing environment, we demonstrated the benefits of establishment fertilization to stand LAI development using a large sample over the native range of loblolly pine.
Age-dependent ventilator-induced lung injury: Mathematical modeling, experimental data, and statistical analysis
Hay, Quintessa; Grubb, Christopher; Minucci, Sarah; Valentine, Michael S.; Van Mullekom, Jennifer H.; Heise, Rebecca L.; Reynolds, Angela M. (PLOS, 2024-02-22)
A variety of pulmonary insults can prompt the need for life-saving mechanical ventilation; however, misuse, prolonged use, or an excessive inflammatory response, can result in ventilator-induced lung injury. Past research has observed an increased instance of respiratory distress in older patients and differences in the inflammatory response. To address this, we performed high pressure ventilation on young (2-3 months) and old (20-25 months) mice for 2 hours and collected data for macrophage phenotypes and lung tissue integrity. Large differences in macrophage activation at baseline and airspace enlargement after ventilation were observed in the old mice. The experimental data was used to determine plausible trajectories for a mathematical model of the inflammatory response to lung injury which includes variables for the innate inflammatory cells and mediators, epithelial cells in varying states, and repair mediators. Classification methods were used to identify influential parameters separating the parameter sets associated with the young or old data and separating the response to ventilation, which was measured by changes in the epithelial state variables. Classification methods ranked parameters involved in repair and damage to the epithelial cells and those associated with classically activated macrophages to be influential. Sensitivity results were used to determine candidate in-silico interventions and these interventions were most impact for transients associated with the old data, specifically those with poorer lung health prior to ventilation. Model results identified dynamics involved in M1 macrophages as a focus for further research, potentially driving the age-dependent differences in all macrophage phenotypes. The model also supported the pro-inflammatory response as a potential indicator of age-dependent differences in response to ventilation. This mathematical model can serve as a baseline model for incorporating other pulmonary injuries.
Nonparametric Bayesian Functional Clustering with Applications to Racial Disparities in Breast Cancer
Gao, Wenyu; Kim, Inyoung; Nam, Wonil; Ren, Xiang; Zhou, Wei; Agah, Masoud (Wiley, 2024-01)
As we have easier access to massive data sets, functional analyses have gained more interest. However, such data sets often contain large heterogeneities, noises, and dimensionalities. When generalizing the analyses from vectors to functions, classical methods might not work directly. This paper considers noisy information reduction in functional analyses from two perspectives: functional clustering to group similar observations and thus reduce the sample size and functional variable selection to reduce the dimensionality. The complicated data structures and relations can be easily modeled by a Bayesian hierarchical model due to its flexibility. Hence, this paper proposes a nonparametric Bayesian functional clustering and peak point selection method via weighted Dirichlet process mixture (WDPM) modeling that automatically clusters and provides accurate estimations, together with conditional Laplace prior, which is a conjugate variable selection prior. The proposed method is named WDPM-VS for short, and is able to simultaneously perform the following tasks: (1) Automatic cluster without specifying the number of clusters or cluster centers beforehand; (2) Cluster for heterogeneously behaved functions; (3) Select vibrational peak points; and (4) Reduce noisy information from the two perspectives: sample size and dimensionality. The method will greatly outperform its comparison methods in root mean squared errors. Based on this proposed method, we are able to identify biological factors that can explain the breast cancer racial disparities.

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