A high-dimensional mediation model for a neuroimaging mediator: Integrating clinical, neuroimaging, and neurocognitive data to mitigate late effects in pediatric cancer.

Pediatric cancer treatment, especially for brain tumors, can have profound and complicated late effects. With the survival rates increasing because of improved detection and treatment, a more comprehensive understanding of the impact of current treatments on neurocognitive function and brain structure is critically needed. A frontline medulloblastoma clinical trial (SJMB03) has collected data, including treatment, clinical, neuroimaging, and cognitive variables. Advanced methods for modeling and integrating these data are critically needed to understand the mediation pathway from the treatment through brain structure to neurocognitive outcomes. We propose an integrative Bayesian mediation analysis approach to model jointly a treatment exposure, a high-dimensional structural neuroimaging mediator, and a neurocognitive outcome and to uncover the mediation pathway. The high-dimensional imaging-related coefficients are modeled via a binary Ising-Gaussian Markov random field prior (BI-GMRF), addressing the sparsity, spatial dependency, and smoothness and increasing the power to detect brain regions with mediation effects. Numerical simulations demonstrate the estimation accuracy, power, and robustness. For the SJMB03 study, the BI-GMRF method has identified white matter microstructure that is damaged by cancer-directed treatment and impacts late neurocognitive outcomes. The results provide guidance on improving treatment planning to minimize long-term cognitive sequela for pediatric brain tumor patients.

Revealing Opinions for COVID-19 Questions Using a Context Retriever, Opinion Aggregator, and Question-Answering Model: Model Development Study.

BACKGROUND: COVID-19 has challenged global public health because it is highly contagious and can be lethal. Numerous ongoing and recently published studies about the disease have emerged. However, the research regarding COVID-19 is largely ongoing and inconclusive. OBJECTIVE: A potential way to accelerate COVID-19 research is to use existing information gleaned from research into other viruses that belong to the coronavirus family. Our objective is to develop a natural language processing method for answering factoid questions related to COVID-19 using published articles as knowledge sources. METHODS: Given a question, first, a BM25-based context retriever model is implemented to select the most relevant passages from previously published articles. Second, for each selected context passage, an answer is obtained using a pretrained bidirectional encoder representations from transformers (BERT) question-answering model. Third, an opinion aggregator, which is a combination of a biterm topic model and k-means clustering, is applied to the task of aggregating all answers into several opinions. RESULTS: We applied the proposed pipeline to extract answers, opinions, and the most frequent words related to six questions from the COVID-19 Open Research Dataset Challenge. By showing the longitudinal distributions of the opinions, we uncovered the trends of opinions and popular words in the articles published in the five time periods assessed: before 1990, 1990-1999, 2000-2009, 2010-2018, and since 2019. The changes in opinions and popular words agree with several distinct characteristics and challenges of COVID-19, including a higher risk for senior people and people with pre-existing medical conditions; high contagion and rapid transmission; and a more urgent need for screening and testing. The opinions and popular words also provide additional insights for the COVID-19-related questions. CONCLUSIONS: Compared with other methods of literature retrieval and answer generation, opinion aggregation using our method leads to more interpretable, robust, and comprehensive question-specific literature reviews. The results demonstrate the usefulness of the proposed method in answering COVID-19-related questions with main opinions and capturing the trends of research about COVID-19 and other relevant strains of coronavirus in recent years.

Noninferiority testing for matched-pair ordinal data with misclassification.

New treatments that are noninferior or equivalent to-but not necessarily superior to-the reference treatment may still be beneficial to patients because they have fewer side effects, are more convenient, take less time, or cost less. The noninferiority test is widely used in medical research to provide guidance in such situation. In addition, categorical variables are frequently encountered in medical research, such as in studies involving patient-reported outcomes. In this paper, we develop a noninferiority testing procedure for correlated ordinal categorical variables based on a paired design with a latent normal distribution approach. Misclassification is frequently encountered in the collection of ordinal categorical data; therefore, we further extend the procedure to account for misclassification using information in the partially validated data. Simulation studies are conducted to investigate the accuracy of the estimates, the type I error rates, and the power of the proposed procedure. Finally, we analyze one substantive example to demonstrate the utility of the proposed approach.

Bayesian longitudinal low-rank regression models for imaging genetic data from longitudinal studies.

To perform a joint analysis of multivariate neuroimaging phenotypes and candidate genetic markers obtained from longitudinal studies, we develop a Bayesian longitudinal low-rank regression (L2R2) model. The L2R2 model integrates three key methodologies: a low-rank matrix for approximating the high-dimensional regression coefficient matrices corresponding to the genetic main effects and their interactions with time, penalized splines for characterizing the overall time effect, and a sparse factor analysis model coupled with random effects for capturing within-subject spatio-temporal correlations of longitudinal phenotypes. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. Simulations show that the L2R2 model outperforms several other competing methods. We apply the L2R2 model to investigate the effect of single nucleotide polymorphisms (SNPs) on the top 10 and top 40 previously reported Alzheimer disease-associated genes. We also identify associations between the interactions of these SNPs with patient age and the tissue volumes of 93 regions of interest from patients' brain images obtained from the Alzheimer's Disease Neuroimaging Initiative.

Multiple SNP Set Analysis for Genome-Wide Association Studies Through Bayesian Latent Variable Selection.

The power of genome-wide association studies (GWAS) for mapping complex traits with single-SNP analysis (where SNP is single-nucleotide polymorphism) may be undermined by modest SNP effect sizes, unobserved causal SNPs, correlation among adjacent SNPs, and SNP-SNP interactions. Alternative approaches for testing the association between a single SNP set and individual phenotypes have been shown to be promising for improving the power of GWAS. We propose a Bayesian latent variable selection (BLVS) method to simultaneously model the joint association mapping between a large number of SNP sets and complex traits. Compared with single SNP set analysis, such joint association mapping not only accounts for the correlation among SNP sets but also is capable of detecting causal SNP sets that are marginally uncorrelated with traits. The spike-and-slab prior assigned to the effects of SNP sets can greatly reduce the dimension of effective SNP sets, while speeding up computation. An efficient Markov chain Monte Carlo algorithm is developed. Simulations demonstrate that BLVS outperforms several competing variable selection methods in some important scenarios.

Bayesian Factor Analysis as a Variable-Selection Problem: Alternative Priors and Consequences.

Factor analysis is a popular statistical technique for multivariate data analysis. Developments in the structural equation modeling framework have enabled the use of hybrid confirmatory/exploratory approaches in which factor-loading structures can be explored relatively flexibly within a confirmatory factor analysis (CFA) framework. Recently, Muthén & Asparouhov proposed a Bayesian structural equation modeling (BSEM) approach to explore the presence of cross loadings in CFA models. We show that the issue of determining factor-loading patterns may be formulated as a Bayesian variable selection problem in which Muthén and Asparouhov's approach can be regarded as a BSEM approach with ridge regression prior (BSEM-RP). We propose another Bayesian approach, denoted herein as the Bayesian structural equation modeling with spike-and-slab prior (BSEM-SSP), which serves as a one-stage alternative to the BSEM-RP. We review the theoretical advantages and disadvantages of both approaches and compare their empirical performance relative to two modification indices-based approaches and exploratory factor analysis with target rotation. A teacher stress scale data set is used to demonstrate our approach.

High-Dimensional Mediation Analysis with Network Mediators: Applications to Pediatric Acute Lymphoblastic Leukemia.

Acute lymphoblastic leukemia (ALL) is the most common childhood cancer, with survivors frequently experiencing long-term neurocognitive morbidities. Here, we utilize the TOTXVI clinical trial data to elucidate the mechanisms underlying treatment-related neurocognitive side effects in pediatric ALL patients by incorporating brain connectivity network data. To enable such analysis, we propose a high-dimensional mediation analysis method with a novel network mediation structural shrinkage (NMSS) prior, which is particularly suited for analyzing high-dimensional brain structural connectivity network data that serve as mediators. Our method is capable of addressing the structural dependencies of brain connectivity networks including sparsity, effective degrees of nodes, and modularity, yielding accurate estimates of the high-dimensional coefficients and mediation effects. We demonstrate the effectiveness and superiority of the proposed NMSS method through simulation studies and apply it to the TOTXVI data, revealing significant mediation effects of brain connectivity on visual processing speed directed by IT intensity. The findings shed light on the potential of targeted interventions to mitigate neurocognitive deficits in pediatric ALL survivors.

[Study of arsenic concentration and distribution in shell sand of the shell ridge islands by hydride generation atomic fluorescence spectrometry].

The present paper determined the As concentration in shell sand of the shell ridge islands by hydride generation atomic fluorescence spectrometry, studied the distribution of As in shell sand of the shell ridge islands, analysed the correlations of As with other nutrient elements, and discussed the probably influencing factors affecting the As concentration and distribution in shell sand. The results showed that the range of the arsenic concentration in shell sand is between 0.78 and 8.76 mg x kg(-1), the average concentration is 3.11 mg x kg(-1), and this indicated that the As contamination of the shell ridge island is in clean level. The As concentration of the shell sand has a increasing trend followed by the increase with profile depth or the decrease with the particle size, and the difference in As concentrations in shell sand of different particle sizes reached the significant level (p < or = 0.05). The As concentration in shell sand has a very significant positive correlation with the concentrations of Cu, Zn and Mn as well as the TP and TK, whereas the correlations between As and TN or Fe are not significant. The pollutant of As in the shell sand mainly comes from the absorption and fixation by shell sand from the environment but not the accumulation of the shell organism during their growing up.

Alzheimer's Disease Classification Through Imaging Genetic Data With IGnet.

The application of deep learning techniques to the detection and automated classification of Alzheimer's disease (AD) has recently gained considerable attention. The rapid progress in neuroimaging and sequencing techniques has enabled the generation of large-scale imaging genetic data for AD research. In this study, we developed a deep learning approach, IGnet, for automated AD classification using both magnetic resonance imaging (MRI) data and genetic sequencing data. The proposed approach integrates computer vision (CV) and natural language processing (NLP) techniques, with a deep three-dimensional convolutional network (3D CNN) being used to handle the three-dimensional MRI input and a Transformer encoder being used to manage the genetic sequence input. The proposed approach has been applied to the Alzheimer's Disease Neuroimaging Initiative (ADNI) data set. Using baseline MRI scans and selected single-nucleotide polymorphisms on chromosome 19, it achieved a classification accuracy of 83.78% and an area under the receiver operating characteristic curve (AUC-ROC) of 0.924 with the test set. The results demonstrate the great potential of using multi-disciplinary AI approaches to integrate imaging genetic data for the automated classification of AD.

A Bayesian Vector Autoregressive Model with Nonignorable Missingness in Dependent Variables and Covariates: Development, Evaluation, and Application to Family Processes.

Intensive longitudinal designs involving repeated assessments of constructs often face the problems of nonignorable attrition and selected omission of responses on particular occasions. However, time series models, such as vector autoregressive (VAR) models, are often fit to these data without consideration of nonignorable missingness. We introduce a Bayesian model that simultaneously represents the over-time dependencies in multivariate, multiple-subject time series data via a VAR model, and possible ignorable and nonignorable missingness in the data. We provide software code for implementing this model with application to an empirical data set. Moreover, simulation results comparing the joint approach with two-step multiple imputation procedures are included to shed light on the relative strengths and weaknesses of these approaches in practical data analytic scenarios.

Zero-Inflated Regime-Switching Stochastic Differential Equation Models for Highly Unbalanced Multivariate, Multi-Subject Time-Series Data.

In the study of human dynamics, the behavior under study is often operationalized by tallying the frequencies and intensities of a collection of lower-order processes. For instance, the higher-order construct of negative affect may be indicated by the occurrence of crying, frowning, and other verbal and nonverbal expressions of distress, fear, anger, and other negative feelings. However, because of idiosyncratic differences in how negative affect is expressed, some of the lower-order processes may be characterized by sparse occurrences in some individuals. To aid the recovery of the true dynamics of a system in cases where there may be an inflation of such "zero responses," we propose adding a regime (unobserved phase) of "non-occurrence" to a bivariate Ornstein-Uhlenbeck (OU) model to account for the high instances of non-occurrence in some individuals while simultaneously allowing for multivariate dynamic representation of the processes of interest under nonzero responses. The transition between the occurrence (i.e., active) and non-occurrence (i.e., inactive) regimes is represented using a novel latent Markovian transition model with dependencies on latent variables and person-specific covariates to account for inter-individual heterogeneity of the processes. Bayesian estimation and inference are based on Markov chain Monte Carlo algorithms implemented using the JAGS software. We demonstrate the utility of the proposed zero-inflated regime-switching OU model to a study of young children's self-regulation at 36 and 48 months.

Changes of some anti-oxidative physiological indices under soil water deficits among 10 wheat (Triticum aestivum L.) genotypes at tillering stage.

Drought is one of the major ecological factors limiting crop production and food quality globally, especially in the arid and semi-arid areas of the world. Wheat is the staple food for more than 35% of world population and wheat cultivation is mainly restricted to such zones with scarcity of water, so wheat anti-drought physiology study is of importance to wheat production, food safety and quality and biotechnological breeding for the sake of coping with abiotic and biotic conditions. The current study is to investigate changes of anti-oxidative physiological indices of 10 wheat genotypes at tillering stage. The main results and conclusion of tillering stage in terms of activities of POD, SOD, CAT and MDA content as followed: (1) 10 wheat genotypes can be generally grouped into three kinds (A-C, respectively) according to their changing trend of the measured indices; (2) A group performed better drought resistance under the condition of treatment level 1 (appropriate level), whose activities of anti-oxidative enzymes (POD, SOD, CAT) were higher and MDA lower; (3) B group exhibited stronger anti-drought under treatment level 2 (light-stress level), whose activities of anti-oxidative enzymes were higher and MDA lower; (4) C group expressed anti-drought to some extent under treatment level 3 (serious-stress), whose activities of anti-oxidative enzymes were stronger, MDA lower; (5) these results demonstrated that different wheat genotypes have different physiological mechanisms to adapt themselves to changing drought stress, whose molecular basis is discrete gene expression profiling (transcriptom). The study in this respect is the key to wheat anti-drought and biological-saving water in worldwide arid and semi-arid areas; (6) POD, SOD, and CAT activities and MDA content of different wheat genotypes had quite different changing trend at different stages and under different soil water stress conditions, which was linked with their origin of cultivation and individual soil water threshold, which will provide better reference to selecting proper plant species for eco-environmental construction and crops for sustainable agriculture in arid and semi-arid areas.

On evolution and perspectives of bio-watersaving.

As shortage in water resources is a fact, bio-watersaving becomes one hot topic at present. The concept of bio-watersaving has been developed from agronomic watersaving to physiological watersaving then to gene watersaving. The definition of bio-watersaving is yielding more agricultural productions under the same water condition by exploiting the physiological and genetic potential of organisms themselves. There are two aspects in bio-watersaving: one is managing crop system and watersaving irrigation according to the drought characteristics and physiological water need of plants; the second is breeding new varieties with good drought resistance and high water use efficiency (WUE) and high yield and good quality traits, through exploiting new drought resistance genes and high WUE genes with the aid of biotechnology. Gene watersaving is the base for physiological watersaving, so gene watersaving has the biggest potential to be exploited in future, and will play an important role in high use efficiency of water and soil resources, and agricultural sustainable development in China and the globe.

Mutual physiological genetic mechanism of plant high water use efficiency and nutrition use efficiency.

Water deficiency and lower fertilizer utilization efficiency are major constraints of productivity and yield stability. Improvements of crop water use efficiency (WUE) and nutrient use efficiency (NUE) is becoming an important objective in crop breeding. With the introduction of new physiological and biological approaches, we can better understand the mutual genetics mechanism of high use efficiency of water and nutrient. Much work has been done in past decades mainly including the interactions between different fertilizers and water influences on root characteristics and crop growth. Fertilizer quantity and form were regulated in order to improve crop WUE. The crop WUE and NUE shared the same increment tendency during evolution process; some genes associated with WUE and NUE have been precisely located and marked on the same chromosomes, some genes related to WUE and NUE have been cloned and transferred into wheat and rice and other plants, they can enhance water and nutrient use efficiency. The proteins transporting nutrient and water were identified such as some water channel proteins. The advance on the mechanism of higher water and nutrient use efficiency in crop was reviewed in this article, and it could provide some useful information for further research on WUE and NUE in crop.

A comparison of Bayesian and frequentist model selection methods for factor analysis models.

We compare the performances of well-known frequentist model fit indices (MFIs) and several Bayesian model selection criteria (MCC) as tools for cross-loading selection in factor analysis under low to moderate sample sizes, cross-loading sizes, and possible violations of distributional assumptions. The Bayesian criteria considered include the Bayes factor (BF), Bayesian Information Criterion (BIC), Deviance Information Criterion (DIC), a Bayesian leave-one-out with Pareto smoothed importance sampling (LOO-PSIS), and a Bayesian variable selection method using the spike-and-slab prior (SSP; Lu, Chow, & Loken, 2016). Simulation results indicate that of the Bayesian measures considered, the BF and the BIC showed the best balance between true positive rates and false positive rates, followed closely by the SSP. The LOO-PSIS and the DIC showed the highest true positive rates among all the measures considered, but with elevated false positive rates. In comparison, likelihood ratio tests (LRTs) are still the preferred frequentist model comparison tool, except for their higher false positive detection rates compared to the BF, BIC and SSP under violations of distributional assumptions. The root mean squared error of approximation (RMSEA) and the Tucker-Lewis index (TLI) at the conventional cut-off of approximate fit impose much more stringent "penalties" on model complexity under conditions with low cross-loading size, low sample size, and high model complexity compared with the LRTs and all other Bayesian MCC. Nevertheless, they provided a reasonable alternative to the LRTs in cases where the models cannot be readily constructed as nested within each other. (PsycINFO Database Record

Bayesian Analysis of Ambulatory Blood Pressure Dynamics with Application to Irregularly Spaced Sparse Data.

Ambulatory cardiovascular (CV) measurements provide valuable insights into individuals' health conditions in "real-life," everyday settings. Current methods of modeling ambulatory CV data do not consider the dynamic characteristics of the full data set and their relationships with covariates such as caffeine use and stress. We propose a stochastic differential equation (SDE) in the form of a dual nonlinear Ornstein-Uhlenbeck (OU) model with person-specific covariates to capture the morning surge and nighttime dipping dynamics of ambulatory CV data. To circumvent the data analytic constraint that empirical measurements are typically collected at irregular and much larger time intervals than those evaluated in simulation studies of SDEs, we adopt a Bayesian approach with a regularized Brownian Bridge sampler (RBBS) and an efficient multiresolution (MR) algorithm to fit the proposed SDE. The MR algorithm can produce more efficient MCMC samples that is crucial for valid parameter estimation and inference. Using this model and algorithm to data from the Duke Behavioral Investigation of Hypertension Study, results indicate that age, caffeine intake, gender and race have effects on distinct dynamic characteristics of the participants' CV trajectories.

A Bayesian modeling approach for generalized semiparametric structural equation models.

In behavioral, biomedical, and psychological studies, structural equation models (SEMs) have been widely used for assessing relationships between latent variables. Regression-type structural models based on parametric functions are often used for such purposes. In many applications, however, parametric SEMs are not adequate to capture subtle patterns in the functions over the entire range of the predictor variable. A different but equally important limitation of traditional parametric SEMs is that they are not designed to handle mixed data types-continuous, count, ordered, and unordered categorical. This paper develops a generalized semiparametric SEM that is able to handle mixed data types and to simultaneously model different functional relationships among latent variables. A structural equation of the proposed SEM is formulated using a series of unspecified smooth functions. The Bayesian P-splines approach and Markov chain Monte Carlo methods are developed to estimate the smooth functions and the unknown parameters. Moreover, we examine the relative benefits of semiparametric modeling over parametric modeling using a Bayesian model-comparison statistic, called the complete deviance information criterion (DIC). The performance of the developed methodology is evaluated using a simulation study. To illustrate the method, we used a data set derived from the National Longitudinal Survey of Youth.

[Quality level assessment of lowly efficient Tamarix chinensis secondary shrubs in Laizhou Bay of Yellow River Delta].

Taking the Tamarix chinensis secondary shrubs in Laizhou Bay of Yellow River Delta as test objects, and by using synthetic factor method, this paper studied the main factors causing the lowly efficiency of T. chinensis secondary shrubs as well as the main parameters for the classification of lowly efficient T. chinensis secondary shrubs. A total of 24 indices including shrubs growth and soil physical and chemical properties were selected to determine the main affecting factors and parameters in evaluating and classifying the lowly efficient shrubs. There were no obvious correlations between the indices reflecting the shrubs growth and soil quality, and thus, only using shrub growth index to reflect the lowly efficiency level of T. chinensis was not enough, and it would be necessary to combine with soil quality factors to make a comprehensive evaluation. The principal factors reflecting the quality level of lowly efficient T. chinensis shrubs included soil salt content and moisture content, stand age, single tree's aboveground stem, leaf biomass, and basal diameter, followed by soil density, porosity, and soil nutrient status. The lowly efficient T. chinensis shrubs in the Bay could be classified into five types, namely, shrub with growth potential, slightly low quality shrub, moderately lowly efficient shrub, moderately low quality and lowly efficient shrub, and seriously low quality and lowly efficient shrub. The main features, low efficiency causes, and management measures of these shrubs were discussed based on the mean cluster value.

[Roles of soil dissolved organic carbon in carbon cycling of terrestrial ecosystems: a review].

Soil dissolved organic carbon (DOC) is an active fraction of soil organic carbon pool, playing an important role in the carbon cycling of terrestrial ecosystems. In view of the importance of the carbon cycling, this paper summarized the roles of soil DOC in the soil carbon sequestration and greenhouse gases emission, and in considering of our present ecological and environmental problems such as soil acidification and climate warming, discussed the effects of soil properties, environmental factors, and human activities on the soil DOC as well as the response mechanisms of the DOC. This review could be helpful to the further understanding of the importance of soil DOC in the carbon cycling of terrestrial ecosystems and the reduction of greenhouse gases emission.