Distributed Inference for Spatial Extremes Modeling in High Dimensions.

Extreme environmental events frequently exhibit spatial and temporal dependence. These data are often modeled using max stable processes (MSPs) that are computationally prohibitive to fit for as few as a dozen observations. Supposed computationally-efficient approaches like the composite likelihood remain computationally burdensome with a few hundred observations. In this paper, we propose a spatial partitioning approach based on local modeling of subsets of the spatial domain that delivers computationally and statistically efficient inference. Marginal and dependence parameters of the MSP are estimated locally on subsets of observations using censored pairwise composite likelihood, and combined using a modified generalized method of moments procedure. The proposed distributed approach is extended to estimate inverted MSP models, and to estimate spatially varying coefficient models to deliver computationally efficient modeling of spatial variation in marginal parameters. We demonstrate consistency and asymptotic normality of estimators, and show empirically that our approach leads to statistically efficient estimation of model parameters. We illustrate the flexibility and practicability of our approach through simulations and the analysis of streamflow data from the U.S. Geological Survey.

A statistical approach to system suitability testing for mass spectrometry imaging.

RATIONALE: Mass spectrometry imaging (MSI) elevates the power of conventional mass spectrometry (MS) to multidimensional space, elucidating both chemical composition and localization. However, the field lacks any robust quality control (QC) and/or system suitability testing (SST) protocols to monitor inconsistencies during data acquisition, both of which are integral to ensure the validity of experimental results. To satisfy this demand in the community, we propose an adaptable QC/SST approach with five analyte options amendable to various ionization MSI platforms (e.g., desorption electrospray ionization, matrix-assisted laser desorption/ionization [MALDI], MALDI-2, and infrared matrix-assisted laser desorption electrospray ionization [IR-MALDESI]). METHODS: A novel QC mix was sprayed across glass slides to collect QC/SST regions-of-interest (ROIs). Data were collected under optimal conditions and on a compromised instrument to construct and refine the principal component analysis (PCA) model in R. Metrics, including mass measurement accuracy and spectral accuracy, were evaluated, yielding an individual suitability score for each compound. The average of these scores is utilized to inform if troubleshooting is necessary. RESULTS: The PCA-based SST model was applied to data collected when the instrument was compromised. The resultant SST scores were used to determine a statistically significant threshold, which was defined as 0.93 for IR-MALDESI-MSI analyses. This minimizes the type-I error rate, where the QC/SST would report the platform to be in working condition when cleaning is actually necessary. Further, data scored after a partial cleaning demonstrate the importance of QC and frequent full instrument cleaning. CONCLUSIONS: This study is the starting point for addressing an important issue and will undergo future development to improve the efficiency of the protocol. Ultimately, this work is the first of its kind and proposes this approach as a proof of concept to develop and implement universal QC/SST protocols for a variety of MSI platforms.

Early Lead Exposure Associated with Molar Hypomineralization.

Purpose: The purpose of this study was to determine the association between prenatal and early life exposure to lead and the presence of molar hypomineralization (MH) in a group of Mexican children. Methods: A subset of participants of the Early Life Exposure in Mexico to Environmental Toxicants (ELEMENTS) cohort study was examined for the presence of molar hypomineralization using European Academy of Pedi- atric Dentistry (EAPD) criteria. Prenatal lead exposure was assessed by K-ray fluorescence measurements of patella and tibia lead and by maternal blood lead levels by trimester and averaged over trimesters. Postnatal exposure was assessed by levels of maternal blood lead at delivery and child blood lead at 12 and 24 months. Results: A subset of 506 subjects from the ELEMENT cohorts (nine to 18 years old) were examined for MH; 87 subjects (17.2 percent) had MH. Maternal blood lead levels in the third trimester (odds ratio [OR] equals 1.08; 95 percent confidence interval [95% CI] equals 1.02 to 1.15) and averaged over three trimesters (OR equals 1.10; 95% CI equals 1.02 to 1.19) were significantly associated with MH status. None of the maternal bone lead or the child's blood lead parameters was significantly associated with the presence of MH (P>0.05). Conclusions: This study documents a significant association between prenatal lead exposure especially in late pregnancy and the odds of molar hypomineralization.

Sleep Classification With Artificial Synthetic Imaging Data Using Convolutional Neural Networks.

OBJECTIVE: We propose a new analytic framework, "Artificial Synthetic Imaging Data (ASID) Workflow," for sleep classification from a wearable device comprising: 1) the creation of ASID from data collected by a non-invasive wearable device that permits real-time multi-modal physiological monitoring on heart rate (HR), 3-axis accelerometer, electrodermal activity, and skin temperature, denoted as "Temporal E4 Data" (TED) and 2) the use of an image classification supervised learning algorithm, convolutional neural network (CNN), to classify periods of sleep. METHODS: We investigate ASID Workflow under 6 settings (3 data resolutions × 2 HR scenarios). Competing machine/deep learning classification algorithms, including logistic regression, support vector machine, random forest, k-nearest neighbors, and Long Short-Term Memory, are applied to TED as comparisons, termed "Competing Workflow." RESULTS: The ASID Workflow achieves excellent performance with mean weighted accuracy across settings of 94.7%, and is superior to the Competing Workflow with high and low resolution data regardless of the inclusion of HR modality. This superiority is maximized for low resolution data without HR. Additionally, CNN has a relatively low subject-wise test computational cost compared with competing algorithms. CONCLUSION: We demonstrate the utility of creating ASID from multi-modal physiological data and applying a preexisting image classification algorithm to achieve better classification accuracy. We shed light on the influence of data resolution and HR modality on the Workflow's performance. SIGNIFICANCE: Applying CNN to ASID allows us to capture both temporal and spatial dependency among physiological variables and modalities by using 2D images' topological structure that competing algorithms fail to utilize.

Interrogating the Metabolomic Profile of Amyotrophic Lateral Sclerosis in the Post-Mortem Human Brain by Infrared Matrix-Assisted Laser Desorption Electrospray Ionization (IR-MALDESI) Mass Spectrometry Imaging (MSI).

Amyotrophic lateral sclerosis (ALS) is an idiopathic, fatal neurodegenerative disease characterized by progressive loss of motor function with an average survival time of 2-5 years after diagnosis. Due to the lack of signature biomarkers and heterogenous disease phenotypes, a definitive diagnosis of ALS can be challenging. Comprehensive investigation of this disease is imperative to discovering unique features to expedite the diagnostic process and improve diagnostic accuracy. Here, we present untargeted metabolomics by mass spectrometry imaging (MSI) for comparing sporadic ALS (sALS) and C9orf72 positive (C9Pos) post-mortem frontal cortex human brain tissues against a control cohort. The spatial distribution and relative abundance of metabolites were measured by infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) MSI for association to biological pathways. Proteomic studies on the same patients were completed via LC-MS/MS in a previous study, and results were integrated with imaging metabolomics results to enhance the breadth of molecular coverage. Utilizing METASPACE annotation platform and MSiPeakfinder, nearly 300 metabolites were identified across the sixteen samples, where 25 were identified as dysregulated between disease cohorts. The dysregulated metabolites were further examined for their relevance to alanine, aspartate, and glutamate metabolism, glutathione metabolism, and arginine and proline metabolism. The dysregulated pathways discussed are consistent with reports from other ALS studies. To our knowledge, this work is the first of its kind, reporting on the investigation of ALS post-mortem human brain tissue analyzed by multiomic MSI.

Genome-wide association studies of metabolites in Finnish men identify disease-relevant loci.

Few studies have explored the impact of rare variants (minor allele frequency < 1%) on highly heritable plasma metabolites identified in metabolomic screens. The Finnish population provides an ideal opportunity for such explorations, given the multiple bottlenecks and expansions that have shaped its history, and the enrichment for many otherwise rare alleles that has resulted. Here, we report genetic associations for 1391 plasma metabolites in 6136 men from the late-settlement region of Finland. We identify 303 novel association signals, more than one third at variants rare or enriched in Finns. Many of these signals identify genes not previously implicated in metabolite genome-wide association studies and suggest mechanisms for diseases and disease-related traits.

A Distributed and Integrated Method of Moments for High-Dimensional Correlated Data Analysis.

This paper is motivated by a regression analysis of electroencephalography (EEG) neuroimaging data with high-dimensional correlated responses with multi-level nested correlations. We develop a divide-and-conquer procedure implemented in a fully distributed and parallelized computational scheme for statistical estimation and inference of regression parameters. Despite significant efforts in the literature, the computational bottleneck associated with high-dimensional likelihoods prevents the scalability of existing methods. The proposed method addresses this challenge by dividing responses into subvectors to be analyzed separately and in parallel on a distributed platform using pairwise composite likelihood. Theoretical challenges related to combining results from dependent data are overcome in a statistically efficient way using a meta-estimator derived from Hansen's generalized method of moments. We provide a rigorous theoretical framework for efficient estimation, inference, and goodness-of-fit tests. We develop an R package for ease of implementation. We illustrate our method's performance with simulations and the analysis of the EEG data, and find that iron deficiency is significantly associated with two auditory recognition memory related potentials in the left parietal-occipital region of the brain.

Integrative Analysis of Gene-Specific DNA Methylation and Untargeted Metabolomics Data from the ELEMENT Cohort.

Epigenetic modifications, such as DNA methylation, influence gene expression and cardiometabolic phenotypes that are manifest in developmental periods in later life, including adolescence. Untargeted metabolomics analysis provide a comprehensive snapshot of physiological processes and metabolism and have been related to DNA methylation in adults, offering insights into the regulatory networks that influence cellular processes. We analyzed the cross-sectional correlation of blood leukocyte DNA methylation with 3758 serum metabolite features (574 of which are identifiable) in 238 children (ages 8-14 years) from the Early Life Exposures in Mexico to Environmental Toxicants (ELEMENT) study. Associations between these features and percent DNA methylation in adolescent blood leukocytes at LINE-1 repetitive elements and genes that regulate early life growth (IGF2, H19, HSD11B2) were assessed by mixed effects models, adjusting for sex, age, and puberty status. After false discovery rate correction (FDR q < 0.05), 76 metabolites were significantly associated with LINE-1 DNA methylation, 27 with HSD11B2, 103 with H19, and 4 with IGF2. The ten identifiable metabolites included dicarboxylic fatty acids (five associated with LINE-1 or H19 methylation at q < 0.05) and 1-octadecanoyl-rac-glycerol (q < 0.0001 for association with H19 and q = 0.04 for association with LINE-1). We then assessed the association between these ten known metabolites and adiposity 3 years later. Two metabolites, dicarboxylic fatty acid 17:3 and 5-oxo-7-octenoic acid, were inversely associated with measures of adiposity (P < .05) assessed approximately 3 years later in adolescence. In stratified analyses, sex-specific and puberty-stage specific (Tanner stage = 2 to 5 vs Tanner stage = 1) associations were observed. Most notably, hundreds of statistically significant associations were observed between H19 and LINE-1 DNA methylation and metabolites among children who had initiated puberty. Understanding relationships between subclinical molecular biomarkers (DNA methylation and metabolites) may increase our understanding of genes and biological pathways contributing to metabolic changes that underlie the development of adiposity during adolescence.

Mercury exposure in relation to sleep duration, timing, and fragmentation among adolescents in Mexico City.

INTRODUCTION: Mercury intoxication is known to be associated with adverse symptoms of fatigue and sleep disturbances, but whether low-level mercury exposure could affect sleep remains unclear. In particular, children may be especially vulnerable to both mercury exposures and to poor sleep. We sought to examine associations between mercury levels and sleep disturbances in Mexican youth. METHODS: The study sample comprised 372 youth from the Early Life Exposures to Environmental Toxicants (ELEMENT) cohort, a birth cohort from Mexico City. Sleep (via 7-day actigraphy) and concurrent urine mercury were assessed during a 2015 follow-up visit. Mercury was also assessed in mid-childhood hair, blood, and urine during an earlier study visit, and was considered a secondary analysis. We used linear regression and varying coefficient models to examine non-linear associations between Hg exposure biomarkers and sleep duration, timing, and fragmentation. Unstratified and sex-stratified analyses were adjusted for age and maternal education. RESULTS: During the 2015 visit, participants were 13.3 ± 1.9 years, and 48% were male. There was not a cross-sectional association between urine Hg and sleep characteristics. In secondary analysis using earlier biomarkers of Hg, lower and higher blood Hg exposure was associated with longer sleep duration among girls only. In both boys and girls, Hg biomarker levels in 2008 were associated with later adolescent sleep midpoint (for Hg urine in girls, and for blood Hg in boys). For girls, each unit log Hg was associated with 0.2 h later midpoint (95% CI 0 to 0.4), and for boys each unit log Hg was associated with a 0.4 h later sleep midpoint (95% CI 0.1 to 0.8). CONCLUSIONS: There were mostly null associations between Hg exposure and sleep characteristics among Mexican children. Yet, in both boys and girls, higher Hg exposure in mid-childhood (measured in urine and blood, respectively) was related to later sleep timing in adolescence.

Metabolomic Determinants of Metabolic Risk in Mexican Adolescents.

OBJECTIVE: The goal of this study was to identify metabolites associated with metabolic risk, separately by sex, in Mexican adolescents. METHODS: Untargeted metabolomic profiling was carried out on fasting serum of 238 youth aged 8 to 14 years, and metabolites associated with a metabolic syndrome risk z-score (MetRisk z-score) were identified separately for boys and girls, using the simulation and extrapolation algorithm. Associations of each metabolite with MetRisk z-score were examined using linear regression models that accounted for maternal education, child's age, and pubertal status. RESULTS: Of the 938 features identified in metabolomics analysis, 7 named compounds (of 27 identified metabolites) were associated with MetRisk z-score in girls, and 3 named compounds (of 14 identified) were associated with MetRisk z-score in boys. In girls, diacylglycerol (DG) 16:0/16:0, 1,3-dielaidin, myo-inositol, and urate corresponded with higher MetRisk z-score, whereas N-acetylglycine, thymine, and dodecenedioic acid were associated with lower MetRisk z-score. For example, each z-score increment in DG 16:0/16:0 corresponded with 0.60 (95% CI: 0.47-0.74) units higher MetRisk z-score. In boys, positive associations of DG 16:0/16:0, tyrosine, and 5'-methylthioadenosine with MetRisk z-score were found. CONCLUSIONS: Metabolites on lipid, amino acid, and carbohydrate metabolism pathways are associated with metabolic risk in girls. Compounds on lipid and DNA pathways correspond with metabolic risk in boys.