Scholarly Works, Statistics

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Semiparametric Integrated and Additive Spatio-Temporal Single-Index Models
Mahmoud, Hamdy F. F.; Kim, Inyoung (MDPI, 2023-11-13)
In this paper, we introduce two semiparametric single-index models for spatially and temporally correlated data. Our first model has spatially and temporally correlated random effects that are additive to the nonparametric function, which we refer to as the “semiparametric spatio-temporal single-index model (ST-SIM)”. The second model integrates the spatially correlated effects into the nonparametric function, and the time random effects are additive to the single-index function. We refer to our second model as the “semiparametric integrated spatio-temporal single-index model (IST-SIM)”. Two algorithms based on a Markov chain expectation maximization are introduced to simultaneously estimate the model parameters, spatial effects, and time effects of the two models. We compare the performance of our models using several simulation studies. The proposed models are then applied to mortality data from six major cities in South Korea. Our results suggest that IST-SIM (1) is more flexible than ST-SIM because the former can estimate various nonparametric functions for different locations, while ST-SIM enforces the mortality functions having the same shape over locations; (2) provides better estimation and prediction, and (3) does not need restrictions for the single-index coefficients to fix the identifiability problem.
National assessment of obstetrics and gynecology and family medicine residents' experiences with CenteringPregnancy group prenatal care
Place, Jean Marie; Van De Griend, Kristin; Zhang, Mengxi; Schreiner, Melanie; Munroe, Tanya; Crockett, Amy; Ji, Wenyan; Hanlon, Alexandra L. (2023-11-21)
Objective To examine family medicine (FM) and obstetrician-gynecologist (OB/GYN) residents’ experiences with CenteringPregnancy (CP) group prenatal care (GPNC) as a correlate to perceived likelihood of implementing CP in future practice, as well as knowledge, level of support, and perceived barriers to implementation. Methods We conducted a repeated cross-sectional study annually from 2017 to 2019 with FM and OB/GYN residents from residency programs in the United States licensed to operate CP. We applied adjusted logistic regression models to identify predictors of intentions to engage with CP in future practice. Results Of 212 FM and 176 OB/GYN residents included in analysis, 67.01% of respondents intended to participate as a facilitator in CP in future practice and 51.80% of respondents were willing to talk to decision makers about establishing CP. Both FM and OB/GYN residents who spent more than 15 h engaged with CP and who expressed support towards CP were more likely to participate as a facilitator. FM residents who received residency-based training on CP and who were more familiar with CP reported higher intention to participate as a facilitator, while OB/GYN residents who had higher levels of engagement with CP were more likely to report an intention to participate as a facilitator. Conclusion Engagement with and support towards CP during residency are key factors in residents’ intention to practice CP in the future. To encourage future adoption of CP among residents, consider maximizing resident engagement with the model in hours of exposure and level of engagement, including hosting residency-based trainings on CP for FM residents.
Alternative approaches for creating a wealth index: the case of Mozambique
Xie, Kexin; Marathe, Achla; Deng, Xinwei; Ruiz-Castillo, Paula; Imputiua, Saimado; Elobolobo, Eldo; Mutepa, Victor; Sale, Mussa; Nicolas, Patricia; Montana, Julia; Jamisse, Edgar; Munguambe, Humberto; Materrula, Felisbela; Casellas, Aina; Rabinovich, Regina; Saute, Francisco; Chaccour, Carlos J.; Sacoor, Charfudin; Rist, Cassidy (BMJ, 2023-08)
Introduction: The wealth index is widely used as a proxy for a household's socioeconomic position (SEP) and living standard. This work constructs a wealth index for the Mopeia district in Mozambique using data collected in year 2021 under the BOHEMIA (Broad One Health Endectocide-based Malaria Intervention in Africa) project. Methods: We evaluate the performance of three alternative approaches against the Demographic and Health Survey (DHS) method based wealth index: feature selection principal components analysis (PCA), sparse PCA and robust PCA. The internal coherence between four wealth indices is investigated through statistical testing. Validation and an evaluation of the stability of the wealth index are performed with additional household income data from the BOHEMIA Health Economics Survey and the 2018 Malaria Indicator Survey data in Mozambique. Results: The Spearman's rank correlation between wealth index ventiles from four methods is over 0.98, indicating a high consistency in results across methods. Wealth rankings and households' income show a strong concordance with the area under the curve value of ∼0.7 in the receiver operating characteristic analysis. The agreement between the alternative wealth indices and the DHS wealth index demonstrates the stability in rankings from the alternative methods. Conclusions: This study creates a wealth index for Mopeia, Mozambique, and shows that DHS method based wealth index is an appropriate proxy for the SEP in low-income regions. However, this research recommends feature selection PCA over the DHS method since it uses fewer asset indicators and constructs a high-quality wealth index.
Assessing Ecosystem State Space Models: Identifiability and Estimation
Smith, John W.; Johnson, Leah R.; Thomas, R. Quinn (Springer, 2023-03)
Hierarchical probability models are being used more often than non-hierarchical deterministic process models in environmental prediction and forecasting, and Bayesian approaches to fitting such models are becoming increasingly popular. In particular, models describing ecosystem dynamics with multiple states that are autoregressive at each step in time can be treated as statistical state space models (SSMs). In this paper, we examine this subset of ecosystem models, embed a process-based ecosystem model into an SSM, and give closed form Gibbs sampling updates for latent states and process precision parameters when process and observation errors are normally distributed. Here, we use simulated data from an example model (DALECev) and study the effects changing the temporal resolution of observations on the states (observation data gaps), the temporal resolution of the state process (model time step), and the level of aggregation of observations on fluxes (measurements of transfer rates on the state process). We show that parameter estimates become unreliable as temporal gaps between observed state data increase. To improve parameter estimates, we introduce a method of tuning the time resolution of the latent states while still using higher-frequency driver information and show that this helps to improve estimates. Further, we show that data cloning is a suitable method for assessing parameter identifiability in this class of models. Overall, our study helps inform the application of state space models to ecological forecasting applications where (1) data are not available for all states and transfers at the operational time step for the ecosystem model and (2) process uncertainty estimation is desired.
Differences in Sleep Quality and Sleepiness among Veterinary Medical Students at Multiple Institutions before and after the Pandemic Induced Transition to Online Learning
Nappier, Michael T.; Alvarez, Elizabeth E.; Bartl-Wilson, Lara; Boynton, Elizabeth P.; Hanlon, Alexandra L.; Lozano, Alicia J.; Ng, Zenithson; Ogunmayowa, Oluwatosin; Shoop, Tiffany; Welborn, Nancy D.; Wuerz, Julia (University of Toronto Press)
Poor sleep health has been previously documented in veterinary medical students. However, it is not known how universal or widespread this problem is. This study evaluated Pittsburgh Sleep Quality Index (PSQI) and Epworth Sleepiness Scale (ESS) scores to measure sleep health among students at seven colleges of veterinary medicine in the United States (US). Inadvertently, the transition to online only learning due to the global COVID-19 pandemic was also captured. Veterinary students were found to have universally poor sleep quality and high daytime sleepiness. The transition to online only learning appeared to have little impact on sleep quality, but improved daytime sleepiness scores were observed. The findings suggest poor sleep health is common among veterinary medical students at multiple institutions in the US and that further investigation is necessary.
BG2: Bayesian variable selection in generalized linear mixed models with nonlocal priors for non-Gaussian GWAS data
Xu, Shuangshuang; Williams, Jacob; Ferreira, Marco A. R. (2023-09-15)
Background Genome-wide association studies (GWASes) aim to identify single nucleotide polymorphisms (SNPs) associated with a given phenotype. A common approach for the analysis of GWAS is single marker analysis (SMA) based on linear mixed models (LMMs). However, LMM-based SMA usually yields a large number of false discoveries and cannot be directly applied to non-Gaussian phenotypes such as count data. Results We present a novel Bayesian method to find SNPs associated with non-Gaussian phenotypes. To that end, we use generalized linear mixed models (GLMMs) and, thus, call our method Bayesian GLMMs for GWAS (BG2). To deal with the high dimensionality of GWAS analysis, we propose novel nonlocal priors specifically tailored for GLMMs. In addition, we develop related fast approximate Bayesian computations. BG2 uses a two-step procedure: first, BG2 screens for candidate SNPs; second, BG2 performs model selection that considers all screened candidate SNPs as possible regressors. A simulation study shows favorable performance of BG2 when compared to GLMM-based SMA. We illustrate the usefulness and flexibility of BG2 with three case studies on cocaine dependence (binary data), alcohol consumption (count data), and number of root-like structures in a model plant (count data).
Design of adaptive EWMA control charts using the conditional false alarm rate
Aytaçoğlu, Burcu; Driscoll, Anne R.; Woodall, William H. (Wiley, 2023-04)
Dynamic control limits can be useful in designing control charts, especially when sample sizes, risk scores, or other covariate values change over time. Computer simulation can be used to control the conditional false alarm rate and thus the in-control run length properties. We show that this approach can be useful in designing adaptive exponentially weighted moving average (AEWMA) control charts for which the control chart smoothing parameter at a given time point depends on the observed value at that time point. We use AEWMA charts as examples, but the approach can be applied to the adaptive cumulative sum (CUSUM) chart and other types of adaptive charts.
Review: Is design data collection still relevant in the big data era? With extensions to machine learning
Freeman, Laura J. (Wiley, 2023-06)
Quantile Importance Sampling
Datta, Jyotishka; Polson, Nicholas G. (2023-05-04)
Long term temporal trends in synoptic-scale weather conditions favoring significant tornado occurrence over the central United States
Elkhouly, Mohamed; Zick, Stephanie E.; Ferreira, Marco A. R. (PLOS, 2023-02-22)
We perform a statistical climatological study of the synoptic- to meso-scale weather conditions favoring significant tornado occurrence to empirically investigate the existence of long term temporal trends. To identify environments that favor tornadoes, we apply an empirical orthogonal function (EOF) analysis to temperature, relative humidity, and winds from the Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) dataset. We consider MERRA-2 data and tornado data from 1980 to 2017 over four adjacent study regions that span the Central, Midwestern, and Southeastern United States. To identify which EOFs are related to significant tornado occurrence, we fit two separate groups of logistic regression models. The first group (LEOF models) estimates the probability of occurrence of a significant tornado day (EF2-EF5) within each region. The second group (IEOF models) classifies the intensity of tornadic days either as strong (EF3-EF5) or weak (EF1-EF2). When compared to approaches using proxies such as convective available potential energy, our EOF approach is advantageous for two main reasons: first, the EOF approach allows for the discovery of important synoptic- to mesoscale variables previously not considered in the tornado science literature; second, proxy-based analyses may not capture important aspects of three-dimensional atmospheric conditions represented by the EOFs. Indeed, one of our main novel findings is the importance of a stratospheric forcing mode on occurrence of significant tornadoes. Other important novel findings are the existence of long-term temporal trends in the stratospheric forcing mode, in a dry line mode, and in an ageostrophic circulation mode related to the jet stream configuration. A relative risk analysis also indicates that changes in stratospheric forcings are partially or completely offsetting increased tornado risk associated with the dry line mode, except in the eastern Midwest region where tornado risk is increasing.
BOHEMIA a cluster randomized trial to assess the impact of an endectocide-based one health approach to malaria in Mozambique: baseline demographics and key malaria indicators
Ruiz-Castillo, Paula; Imputiua, Saimado; Xie, Kexin; Elobolobo, Eldo; Nicolas, Patricia; Montaña, Julia; Jamisse, Edgar; Munguambe, Humberto; Materrula, Felisbela; Casellas, Aina; Deng, Xinwei; Marathe, Achla; Rabinovich, Regina; Saute, Francisco; Chaccour, Carlos; Sacoor, Charfudin (2023-06-04)
Background Many geographical areas of sub-Saharan Africa, especially in rural settings, lack complete and up-to-date demographic data, posing a challenge for implementation and evaluation of public health interventions and carrying out large-scale health research. A demographic survey was completed in Mopeia district, located in the Zambezia province in Mozambique, to inform the Broad One Health Endectocide-based Malaria Intervention in Africa (BOHEMIA) cluster randomized clinical trial, which tested ivermectin mass drug administration to humans and/or livestock as a potential novel strategy to decrease malaria transmission. Methods The demographic survey was a prospective descriptive study, which collected data of all the households in the district that accepted to participate. Households were mapped through geolocation and identified with a unique identification number. Basic demographic data of the household members was collected and each person received a permanent identification number for the study. Results 25,550 households were mapped and underwent the demographic survey, and 131,818 individuals were registered in the district. The average household size was 5 members and 76.9% of households identified a male household head. Housing conditions are often substandard with low access to improved water systems and electricity. The reported coverage of malaria interventions was 71.1% for indoor residual spraying and 54.1% for universal coverage of long-lasting insecticidal nets. The median age of the population was 15 years old. There were 910 deaths in the previous 12 months reported, and 43.9% were of children less than 5 years of age. Conclusions The study showed that the district had good coverage of vector control tools against malaria but sub-optimal living conditions and poor access to basic services. The majority of households are led by males and Mopeia Sede/Cuacua is the most populated locality in the district. The population of Mopeia is young (< 15 years) and there is a high childhood mortality. The results of this survey were crucial as they provided the household and population profiles and allowed the design and implementation of the cluster randomized clinical trial. Trial registration NCT04966702.
Quantifying the Effect of Socio-Economic Predictors and the Built Environment on Mental Health Events in Little Rock, AR
Ek, Alfieri; Drawve, Grant; Robinson, Samantha; Datta, Jyotishka (MDPI, 2023-05-18)
Law enforcement agencies continue to grow in the use of spatial analysis to assist in identifying patterns of outcomes. Despite the critical nature of proper resource allocation for mental health incidents, there has been little progress in statistical modeling of the geo-spatial nature of mental health events in Little Rock, Arkansas. In this article, we provide insights into the spatial nature of mental health data from Little Rock, Arkansas between 2015 and 2018, under a supervised spatial modeling framework. We provide evidence of spatial clustering and identify the important features influencing such heterogeneity via a spatially informed hierarchy of generalized linear, tree-based, and spatial regression models, viz. the Poisson regression model, the random forest model, the spatial Durbin error model, and the Manski model. The insights obtained from these different models are presented here along with their relative predictive performances. The inferential tools developed here can be used in a broad variety of spatial modeling contexts and have the potential to aid both law enforcement agencies and the city in properly allocating resources. We were able to identify several built-environment and socio-demographic measures related to mental health calls while noting that the results indicated that there are unmeasured factors that contribute to the number of events.
Herbivore suppression of waterlettuce in Florida, USA
Foley, Jeremiah R.; Williams, Jacob; Pokorny, Eileen; Tipping, Philip W. (Academic Press, 2023-04)
Waterlettuce, Pistia stratiotes L. (Araceae: Pistieae) is an invasive free-floating aquatic weed found throughout the world that has been targeted for control using various methods including classical and conservation bio-logical control and, herbicides. In Florida, herbicides are the primary strategy employed by land managers, often without regard to the impact of herbivorous arthropods including Samea multiplicalis Guenee (Lepidoptera: Crambidae), Elophila [=Synclita] obliteralis Walker (Lepidoptera: Crambidae), Argyractis [=Petrophila] dru-malis (Dyer) (Lepidoptera: Crambidae), Draeculacephala inscripta VanDuzee (Hemiptera: Cicadellidae), Rho-palosiphum nymphaeae L. (Hemiptera: Aphididae), Orthogalumna terebrantis Wallwork (Acarina: Galumnidae), and Neohydronomus affinis Hustache (Coleoptera: Curculionoidea). A series of field experiments from 2009 to 2012 were conducted at three sites in Florida to quantify the levels of suppression by these species, using an insecticide-check approach to produce restricted and unrestricted herbivory conditions. Four of the species (E. obliteralis, S. multiplicalis, O. terebrantis, and N. affinis) were found at every field site. At the end of the experiment, plots exposed to unrestricted herbivory contained 63.1 % less biomass and covered 32.0 % less surface area compared to plots with restricted herbivory. These results indicate that naturally occurring and introduced species are suppressing the growth of waterlettuce populations in the field in Florida. Future research will examine the synergistic potential of actively managing herbicides and herbivorous arthropods to suppress waterlettuce.
BGWAS: Bayesian variable selection in linear mixed models with nonlocal priors for genome-wide association studies
Williams, Jacob; Xu, Shuangshuang; Ferreira, Marco A. R. (2023-05-11)
Background Genome-wide association studies (GWAS) seek to identify single nucleotide polymorphisms (SNPs) that cause observed phenotypes. However, with highly correlated SNPs, correlated observations, and the number of SNPs being two orders of magnitude larger than the number of observations, GWAS procedures often suffer from high false positive rates. Results We propose BGWAS, a novel Bayesian variable selection method based on nonlocal priors for linear mixed models specifically tailored for genome-wide association studies. Our proposed method BGWAS uses a novel nonlocal prior for linear mixed models (LMMs). BGWAS has two steps: screening and model selection. The screening step scans through all the SNPs fitting one LMM for each SNP and then uses Bayesian false discovery control to select a set of candidate SNPs. After that, a model selection step searches through the space of LMMs that may have any number of SNPs from the candidate set. A simulation study shows that, when compared to popular GWAS procedures, BGWAS greatly reduces false positives while maintaining the same ability to detect true positive SNPs. We show the utility and flexibility of BGWAS with two case studies: a case study on salt stress in plants, and a case study on alcohol use disorder. Conclusions BGWAS maintains and in some cases increases the recall of true SNPs while drastically lowering the number of false positives compared to popular SMA procedures.
A tribute to Howard Rachlin and his two-parameter discounting model: Reliable and flexible model fitting
Franck, Christopher T.; Traxler, Haily K.; Kaplan, Brent A.; Koffarnus, Mikhail N.; Rzeszutek, Mark J. (Wiley, 2023-01)
Delay discounting reflects the rate at which a reward loses its subjective value as a function of delay to that reward. Many models have been proposed to measure delay discounting, and many comparisons have been made among these models. We highlight the two-parameter delay discounting model popularized by Howard Rachlin by demonstrating two key practical features of the Rachlin model. The first feature is flexibility; the Rachlin model fits empirical discounting data closely. Second, when compared with other available two-parameter discounting models, the Rachlin model has the advantage that unique best estimates for parameters are easy to obtain across a wide variety of potential discounting patterns. We focus this work on this second feature in the context of maximum likelihood, showing the relative ease with which the Rachlin model can be utilized compared with the extreme care that must be used with other models for discounting data, focusing on two illustrative cases that pass checks for data validity. Both of these features are demonstrated via a reanalysis of discounting data the authors have previously used for model selection purposes.
Long-term recovery from opioid use disorder: recovery subgroups, transition states and their association with substance use, treatment and quality of life
Craft, William H.; Shin, Hwasoo; Tegge, Allison N.; Keith, Diana R.; Athamneh, Liqa N.; Stein, Jeffrey S.; Ferreira, Marco A. R.; Chilcoat, Howard D.; Le Moigne, Anne; DeVeaugh-Geiss, Angela; Bickel, Warren K. (Wiley, 2022-12)
Background and AimsLimited information exists regarding individual subgroups of recovery from opioid use disorder (OUD) following treatment and how these subgroups may relate to recovery trajectories. We used multi-dimensional criteria to identify OUD recovery subgroups and longitudinal transitions across subgroups. Design, Setting and ParticipantsIn a national longitudinal observational study in the United States, individuals who previously participated in a clinical trial for subcutaneous buprenorphine injections for treatment of OUD were enrolled and followed for an average of 4.2 years after participation in the clinical trial. MeasurementsWe identified recovery subgroups based on psychosocial outcomes including depression, opioid withdrawal and pain. We compared opioid use, treatment utilization and quality of life among these subgroups. FindingsThree dimensions of the recovery process were identified: depression, opioid withdrawal and pain. Using these three dimensions, participants were classified into four recovery subgroups: high-functioning (minimal depression, mild withdrawal and no/mild pain), pain/physical health (minimal depression, mild withdrawal and moderate pain), depression (moderate depression, mild withdrawal and mild/moderate pain) and low-functioning (moderate/severe withdrawal, moderate depression and moderate/severe pain). Significant differences among subgroups were observed for DSM-5 criteria (P < 0.001) and remission status (P < 0.001), as well as with opioid use (P < 0.001), treatment utilization (P < 0.001) and quality of life domains (physical health, psychological, environment and social relationships; Ps < 0.001, Cohen's fs >= 0.62). Recovery subgroup assignments were dynamic, with individuals transitioning across subgroups during the observational period. Moreover, the initial recovery subgroup assignment was minimally predictive of long-term outcomes. ConclusionsThere appear to be four distinct subgroups among individuals in recovery from OUD. Recovery subgroup assignments are dynamic and predictive of contemporaneous, but not long-term, substance use, substance use treatment utilization or quality of life outcomes.
Mapping current and future thermal limits to suitability for malaria transmission by the invasive mosquito Anopheles stephensi
Ryan, Sadie J.; Lippi, Catherine A.; Villena, Oswaldo C.; Singh, Aspen; Murdock, Courtney C.; Johnson, Leah R. (2023-03-21)
Background Anopheles stephensi is a malaria-transmitting mosquito that has recently expanded from its primary range in Asia and the Middle East, to locations in Africa. This species is a competent vector of both Plasmodium falciparum and Plasmodium vivax malaria. Perhaps most alarming, the characteristics of An. stephensi, such as container breeding and anthropophily, make it particularly adept at exploiting built environments in areas with no prior history of malaria risk. Methods In this paper, global maps of thermal transmission suitability and people at risk (PAR) for malaria transmission by An. stephensi were created, under current and future climate. Temperature-dependent transmission suitability thresholds derived from recently published species-specific thermal curves were used to threshold gridded, monthly mean temperatures under current and future climatic conditions. These temperature driven transmission models were coupled with gridded population data for 2020 and 2050, under climate-matched scenarios for future outcomes, to compare with baseline predictions for 2020 populations. Results Using the Global Burden of Disease regions approach revealed that heterogenous regional increases and decreases in risk did not mask the overall pattern of massive increases of PAR for malaria transmission suitability with An. stephensi presence. General patterns of poleward expansion for thermal suitability were seen for both P. falciparum and P. vivax transmission potential. Conclusions Understanding the potential suitability for An. stephensi transmission in a changing climate provides a key tool for planning, given an ongoing invasion and expansion of the vector. Anticipating the potential impact of onward expansion to transmission suitable areas, and the size of population at risk under future climate scenarios, and where they occur, can serve as a large-scale call for attention, planning, and monitoring.
Rate-dependent effects of narrative interventions in a longitudinal study of individuals who use alcohol
Craft, William H.; Dwyer, Candice L.; Tomlinson, Devin C.; Yeh, Yu-Hua; Tegge, Allison N.; Bickel, Warren K. (Wiley, 2023-02-21)
Background: Delay discounting (DD), the decrease in reward valuation as a function of delay to receipt, is a key process undergirding alcohol use. Narrative interventions, including episodic future thinking (EFT), have decreased delay discounting and demand for alcohol. Rate dependence, the relationship between a baseline rate and change in that rate after an intervention, has been evidenced as a marker of ef- ficacious substance use treatment, but whether narrative interventions have rate dependent effects needs to be better understood. We investigated the effects of narrative interventions on delay discounting and hypothetical demand for alcohol in this longitudinal, online study. Methods: Individuals (n = 696) reporting high-or low risk alcohol use were recruited for a longitudinal 3 week survey via Amazon Mechanical Turk. Delay discounting and alcohol demand breakpoint were assessed at baseline. Individuals returned at weeks 2 and 3 and were randomized into the EFT or scarcity narrative interventions and again completed the delay discounting tasks and alcohol breakpoint task. Oldham's correlation was used to explore the rate-dependent effects of narrative interventions. Study attrition as a function of delay discounting was assessed. Results: Episodic future thinking significantly decreased, while scarcity significantly increased delay discounting relative to baseline. No effects of EFT or scarcity on the alcohol demand breakpoint were observed. Significant rate-dependent effects were observed for both narrative intervention types. Higher delay discounting rates were associated with a greater likelihood of attrition from the study. Conclusion: The evidence of a rate-dependent effect of EFT on delay discounting rates offers a more nuanced, mechanistic understanding of this novel therapeutic intervention and can allow more precise treatment targeting by demonstrating who is likely to receive the most benefit from it.
A validation study for a bat-inspired sonar sensing simulator
Zhu, Hongxiao; Gupta, Anupam Kumar; Wu, Xiaowei; Goldsworthy, Michael; Wang, Ruihao; Mikkilineni, Mohitha; Mueller, Rolf (PLoS, 2023-01-20)
Many species of bats rely on echoes to forage and navigate in densely vegetated environments. Foliage echoes in some cases can help bats gather information about the environment, whereas in others may generate clutter that can mask prey echoes during foraging. It is therefore important to study foliage echoes and their role in bat's sensory ecology. In our prior work, a foliage echo simulator has been developed; simulated echoes has been compared with field recordings using a biomimetic sonar head. In this work, we improve the existing simulator by allowing more flexible experimental setups and enabling a closer match with the experiments. Specifically, we add additional features into the simulator including separate directivity patterns for emitter and receiver, the ability to place emitter and receiver at distinct locations, and multiple options to orient the foliage to mimic natural conditions like strong wind. To study how accurately the simulator can replicate the real echo-generating process, we compare simulated echoes with experimental echoes measured by ensonifying a single leaf across four different species of trees. We further extend the prior work on estimating foliage parameters to estimating a map of the environment.
Mapping Genetic Variation in Arabidopsis in Response to Plant Growth-Promoting Bacterium Azoarcus olearius DQS-4T
Plucani do Amaral, Fernanda; Wang, Juexin; Williams, Jacob; Tuleski, Thalita R.; Joshi, Trupti; Ferreira, Marco A. R.; Stacey, Gary (MDPI, 2023-01-28)
Plant growth-promoting bacteria (PGPB) can enhance plant health by facilitating nutrient uptake, nitrogen fixation, protection from pathogens, stress tolerance and/or boosting plant productivity. The genetic determinants that drive the plant–bacteria association remain understudied. To identify genetic loci highly correlated with traits responsive to PGPB, we performed a genome-wide association study (GWAS) using an Arabidopsis thaliana population treated with Azoarcus olearius DQS-4^T. Phenotypically, the 305 Arabidopsis accessions tested responded differently to bacterial treatment by improving, inhibiting, or not affecting root system or shoot traits. GWA mapping analysis identified several predicted loci associated with primary root length or root fresh weight. Two statistical analyses were performed to narrow down potential gene candidates followed by haplotype block analysis, resulting in the identification of 11 loci associated with the responsiveness of Arabidopsis root fresh weight to bacterial inoculation. Our results showed considerable variation in the ability of plants to respond to inoculation by A. olearius DQS-4^T while revealing considerable complexity regarding statistically associated loci with the growth traits measured. This investigation is a promising starting point for sustainable breeding strategies for future cropping practices that may employ beneficial microbes and/or modifications of the root microbiome.

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