Time-varying mediation analysis for incomplete data with application to DNA methylation study for PTSD

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DNA methylation (DNAm) has been shown to mediate causal effects from traumatic experiences to post-traumatic stress disorder (PTSD). However, the scientific question about whether the mediation effect changes over time remains unclear. In this paper, we develop time-varying structural equation models to identify cytosine-phosphate-guanine (CpG) sites where DNAm mediates the effect of trauma exposure on PTSD, and to capture dynamic changes in mediation effects. The proposed methodology is motivated by the Detroit Neighborhood Health Study (DNHS) with high-dimensional and longitudinal DNAm measurements. To handle the non-monotone missing DNAm in the dataset, we propose a novel Longitudinal Multiple Imputation (LMI) method utilizing dependency among repeated measurements, and employ the generalized method of moments to integrate the multiple imputations. Simulations confirm that the proposed method outperforms existing approaches in various longitudinal settings. In DNHS data analysis, our method identifies several CpG sites where DNAm exhibits dynamic mediation effects. Some of the corresponding genes have been shown to be associated with PTSD in the existing literature, and our findings on their time-varying effects could deepen the understanding of the mediation role of DNAm on the causal path from trauma exposure to PTSD risk.

This work is devoted to development of instruments for analysis of ionospheric parameters and detection of anomalies that occur during ionospheric disturbances. An algorithm is proposed to determine the parameters of a multicomponent model of ionospheric data. It is based on a combination of a wavelet transform and autoregressive-integrated moving average models. Methods for the model diagnosis are described. The multicomponent model allows description of quiet variations in ionospheric parameters, prediction of the variations, and detection of anomalies during disturbances. An algorithm based on wavelets and threshold functions is used for detection and detailed analysis of the anomalies. Data from the Institute of Cosmophysical Research and Radio Wave Propagation, Far East Branch, Russian Academy of Sciences, on the ionospheric foF2 critical frequency above Kamchatka were used during the experiments. Anomalies that occur in the ionosphere during increased solar and seismic activity above Kamchatka have been revealed on the basis of the simulation and data analysis.

BackgroundDNA methylation offers an excellent example for elucidating how epigenetic information affects gene expression. β values and M values are commonly used to quantify DNA methylation. Statistical methods applicable to DNA methylation data analysis span a number of approaches such as Wilcoxon rank sum test, t-test, Kolmogorov–Smirnov test, permutation test, empirical Bayes method, and bump hunting method. Nonetheless, selection of an optimal statistical method can be challenging when different methods generate inconsistent results from the same data set.ResultsWe compared six statistical approaches relevant to DNA methylation microarray analysis in terms of false discovery rate control, statistical power, and stability through simulation studies and real data examples. Observable differences were noticed between β values and M values only when methylation levels were correlated across CpG loci. For small sample size (n=3 or 6 in each group), both the empirical Bayes and bump hunting methods showed appropriate FDR control and the highest power when methylation levels across CpG loci were independent. Only the bump hunting method showed appropriate FDR control and the highest power when methylation levels across CpG sites were correlated. For medium (n=12 in each group) and large sample sizes (n=24 in each group), all methods compared had similar power, except for the permutation test whenever the proportion of differentially methylated loci was low. For all sample sizes, the bump hunting method had the lowest stability in terms of standard deviation of total discoveries whenever the proportion of differentially methylated loci was large. The apparent test power comparisons based on raw p-values from DNA methylation studies on ovarian cancer and rheumatoid arthritis provided results as consistent as those obtained in the simulation studies. Overall, these results provide guidance for optimal statistical methods selection under different scenarios.ConclusionsFor DNA methylation studies with small sample size, the bump hunting method and the empirical Bayes method are recommended when DNA methylation levels across CpG loci are independent, while only the bump hunting method is recommended when DNA methylation levels are correlated across CpG loci. All methods are acceptable for medium or large sample sizes.

Aims.We aim to probe the magnetic field geometry and particle acceleration mechanism in the relativistic jets of supermassive black holes.Methods.We conducted a polarimetry campaign from radio to X-ray wavelengths of the high-synchrotron-peak (HSP) blazar Mrk 421, including Imaging X-ray Polarimetry Explorer (IXPE) measurements from 2022 December 6–8. During the IXPE observation, we also monitored Mrk 421 usingSwift-XRT and obtained a single observation withXMM-Newtonto improve the X-ray spectral analysis. The time-averaged X-ray polarization was determined consistently using the event-by-event Stokes parameter analysis, spectropolarimetric fit, and maximum likelihood methods. We examined the polarization variability over both time and energy, the former via analysis of IXPE data obtained over a time span of 7 months.Results.We detected X-ray polarization of Mrk 421 with a degree of ΠX = 14 ± 1% and an electric-vector position angleψX = 107 ± 3° in the 2–8 keV band. From the time variability analysis, we find a significant episodic variation inψX. During the 7 months from the first IXPE pointing of Mrk 421 in 2022 May,ψXvaried in the range 0° to 180°, while ΠXremained relatively constant within ∼10–15%. Furthermore, a swing inψXin 2022 June was accompanied by simultaneous spectral variations. The results of the multiwavelength polarimetry show that ΠXwas generally ∼2–3 times greater than Π at longer wavelengths, whileψfluctuated. Additionally, based on radio, infrared, and optical polarimetry, we find that the rotation ofψoccurred in the opposite direction with respect to the rotation ofψXand over longer timescales at similar epochs.Conclusions.The polarization behavior observed across multiple wavelengths is consistent with previous IXPE findings for HSP blazars. This result favors the energy-stratified shock model developed to explain variable emission in relativistic jets. We considered two versions of the model, one with linear and the other with radial stratification geometry, to explain the rotation ofψX. The accompanying spectral variation during theψXrotation can be explained by a fluctuation in the physical conditions, for example in the energy distribution of relativistic electrons. The opposite rotation direction ofψbetween the X-ray and longer wavelength polarization accentuates the conclusion that the X-ray emitting region is spatially separated from that at longer wavelengths. Moreover, we identify a highly polarized knot of radio emission moving down the parsec-scale jet during the episode ofψXrotation, although it is unclear whether there is any connection between the two events.

Molecular transport measurements through isolated nanopores can greatly inform our understanding of how such systems can select for molecular size and shape. In this work, we present a detailed analysis of experimental gas permeation data through single layer graphene membranes under batch depletion conditions parametric in starting pressure for He, H2, Ne, and CO2 between 100 and 670 kPa. We show mathematically that the observed intersections of the membrane deflection curves parametric in starting pressure are indicative of a time dependent membrane permeance (pressure normalized molecular flow). Analyzing these time dependent permeance data for He, Ne, H2, and CO2 shows remarkably that the latter three gases exhibit discretized permeance values that are temporally repeated. Such quantized fluctuations (called "gating" for liquid phase nanopore and ion channel systems) are a hallmark of isolated nanopores, since small, but rapid changes in the transport pathway necessarily influence a single detectable flux. We analyze the fluctuations using a Hidden Markov model to fit to discrete states and estimate the activation barrier for switching at 1.0 eV. This barrier is and the relative fluxes are consistent with a chemical bond rearrangement of an 8-10 atom vacancy pore. Furthermore, we use the relations between the states given by the Markov network for few pores to determine that three pores, each exhibiting two state switching, are responsible for the observed fluctuations; and we compare simulated control data sets with and without the Markov network for comparison and to establish confidence in our evaluation of the limited experimental data set.

DNA methylation of CpGs located in two types of repetitive elements—LINE1 (L1) and Alu—is used to assess “global” changes in DNA methylation in studies of human disease and environmental exposure. L1 and Alu contribute close to 30% of all base pairs in the human genome and transposition of repetitive elements is repressed through DNA methylation. Few studies have investigated whether repetitive element DNA methylation is associated with DNA methylation at other genomic regions, or the biological and technical factors that influence potential associations. Here, we assess L1 and Alu DNA methylation by Pyrosequencing of consensus sequences and using subsets of probes included in the Illumina Infinium HumanMethylation27 BeadChip array. We show that evolutionary age and assay method affect the assessment of repetitive element DNA methylation. Additionally, we compare Pyrosequencing results for repetitive elements to average DNA methylation of CpG islands, as assessed by array probes classified into strong, weak and non-islands. We demonstrate that each of these dispersed sequences exhibits different patterns of tissue-specific DNA methylation. Correlation of DNA methylation suggests an association between L1 and weak CpG island DNA methylation in some of the tissues examined. We caution, however, that L1, Alu and CpG island DNA methylation are distinct measures of dispersed DNA methylation and one should not be used in lieu of another. Analysis of DNA methylation data is complex and assays may be influenced by environment and pathology in different or complementary ways.

Posttraumatic stress disorder (PTSD) has been associated with increased mortality, primarily in studies of veterans. The World Trade Center Health Registry (Registry) provides a unique opportunity to study the association between PTSD and mortality among a population exposed to the World Trade Center attacks in New York, New York, on September 11, 2001 (9/11). To assess whether 9/11-related probable PTSD (PTSD) is associated with increased mortality risk, as well as whether this association differs when including repeated measures of PTSD over time vs a single baseline assessment. A longitudinal cohort study of 63 666 Registry enrollees (29 270 responders and 34 396 civilians) was conducted from September 5, 2003, to December 31, 2016, with PTSD assessments at baseline (wave 1: 2003-2004) and 3 follow-up time points (wave 2: 2006-2007, wave 3: 2011-2012, wave 4: 2015-2016). Data analyses were conducted from December 4, 2018, to May 20, 2019. Posttraumatic stress disorder was defined using the 17-item PTSD Checklist-Specific (PCL-S) self-report measure (score ≥50) at each wave (waves 1-4). Baseline PTSD was defined using wave 1 PCL-S, and time-varying PTSD was defined using the PCL-S assessments from all 4 waves. Mortality outcomes were ascertained through National Death Index linkage from 2003 to 2016 and defined as all-cause, cardiovascular, and external-cause mortality. Of 63 666 enrollees (38 883 men [61.1%]; mean [SD] age at 9/11, 40.4 [10.4] years), 6689 (10.8%) had PTSD at baseline (responders: 2702 [9.5%]; civilians: 3987 [12.0%]). Participants who were middle aged (2022 [12.5%]), female (3299 [13.8%]), non-Latino black (1295 [17.0%]), or Latino (1835 [22.2%]) were more likely to have PTSD. During follow-up, 2349 enrollees died (including 230 external-cause deaths and 487 cardiovascular deaths). Among all enrollees in time-varying analyses, PTSD was associated with all-cause, cardiovascular, and external-cause mortality, with adjusted hazard ratios (AHRs) of greater magnitude compared with analyses examining baseline PTSD. Among responders, time-varying PTSD was significantly associated with increased risk of all-cause (AHR, 1.91; 95% CI, 1.58-2.32), cardiovascular (AHR, 1.95; 95% CI, 1.25-3.04), and external-cause (AHR, 2.40; 95% CI, 1.47-3.91) mortality. Among civilians, time-varying PTSD was significantly associated with increased risk of all-cause (AHR, 1.54; 95% CI, 1.28-1.85), cardiovascular (AHR, 1.72; 95% CI, 1.15-2.58), and external-cause (AHR, 2.11; 95% CI, 1.06-4.19) mortality. The risk of mortality differed in examination of baseline PTSD vs repeated measures of PTSD over time, suggesting that longitudinal data should be used where possible. Comparable findings between responders and civilians suggest that 9/11-related PTSD is associated with an increased mortality risk.

Many genetic studies have demonstrated an association between the 7-repeat (7r) allele of a 48-base pair variable number of tandem repeats (VNTR) in exon 3 of the DRD4 gene and the phenotype of attention deficit hyperactivity disorder (ADHD). Previous studies have shown inconsistent associations between the 7r allele and neurocognitive performance in children with ADHD. We investigated the performance of 128 children with and without ADHD on the Fixed and Random versions of the Sustained Attention to Response Task (SART). We employed time-series analyses of reaction-time data to allow a fine-grained analysis of reaction time variability, a candidate endophenotype for ADHD. Children were grouped into either the 7r-present group (possessing at least one copy of the 7r allele) or the 7r-absent group. The ADHD group made significantly more commission errors and was significantly more variable in RT in terms of fast moment-to-moment variability than the control group, but no effect of genotype was found on these measures. Children with ADHD without the 7r allele made significantly more omission errors, were significantly more variable in the slow frequency domain and showed less sensitivity to the signal (d') than those children with ADHD the 7r and control children with or without the 7r. These results highlight the utility of time-series analyses of reaction time data for delineating the neuropsychological deficits associated with ADHD and the DRD4 VNTR. Absence of the 7-repeat allele in children with ADHD is associated with a neurocognitive profile of drifting sustained attention that gives rise to variable and inconsistent performance.

This paper presents preliminary results and findings of a dynamic gene regulatory networks analysis obtained from Caenorhabditis elegans (C. elegans) time course DNA microarray data using a maximum a posteriori probability and time-varying autoregression model (MAP-TVAR) approach. High dimensionality and non-stationarity of the time course microarray data are two major challenges of time-varying GRNs analysis. The proposed method employs the L 1 -regularization based sparsity and continuity constraints, which facilitate the identification of sparse GRNs and reduce the estimation variance respectively. To process dataset which may contain extremely large amount of genes, the MAP-TVAR is extended to a distributed framework based on the concept similar to the spirit of Split Bregman method. Well-known interactions such as the eEF-1A.1 and RPL-12, can be identified by the MAP-TVAR approach. These interactions and their corresponding genes are found to be related in the embryo development process of C. elegans. These suggest that the MAP-TVAR approach may serve as a wonderful tool for large-scale time-varying GRNs analysis using gene microarray data and other related datasets.

This study assesses the functioning of the karst aquifer system located on the Croatian coast of the Adriatic Sea, where saltwater intrusion often presents a major problem for freshwater supply. We use two years of sensor data collected from two coastal springs to conduct a range of time-invariant and time-variant statistical analyses over various timescales. We perform separate analyses of the within-day and longer-term variation in the data as well as the interactions between the spring levels, salinity, rainfall, and sea levels. Such comprehensive analyses provide a greater understanding into the inner functioning of the intricate, heavily karstified aquifers. Time-invariant time-series analyses of the hourly data indicate that the spring levels and salinity are strongly controlled by sea levels. Furthermore, time-variant wavelet analyses demonstrate that the variation in spring levels in both springs has two modes defined by flow regime. Increases in the delay of the spring response to sea level indicate that aquifer diffusivity decreases in low flow conditions. Analyses facilitated the development of a conceptual model of the karst subsurface in the discharge zone. Using daily data, we constructed a linear mixed model of the spring levels. This model identified long-term sea level changes, rainfall from previous weeks, and seasonal recharge patterns as the primary factors influencing longer-term spring dynamics.

An approach to the time-varying spectral analysis for shock and vibration data is presented. The approach follows and extends Page's concept of instantaneous power spectra. Various input-output relations for simple linear systems are derived. The error involved in the evaluation of the time-varying spectra of the response and conditions that reduce it are discussed in some detail. Numerical spectra and energy distribution function of four shock functions are provided and their significance is discussed.

The Cox proportional hazards model is commonly used in evaluating risk factors in cancer survival data. The model assumes an additive, linear relationship between the risk factors and the log hazard. However, this assumption may be too simplistic. Further, failure to take time-varying covariates into account, if present, may lower prediction accuracy. In this retrospective, population-based, prognostic study of data from patients diagnosed with cancer from 2008 to 2015 in Ontario, Canada, we applied machine learning-based time-to-event prediction methods and compared their predictive performance in two sets of analyses: (1) yearly-cohort-based time-invariant and (2) fully time-varying covariates analysis. Machine learning-based methods—gradient boosting model (gbm), random survival forest (rsf), elastic net (enet), lasso and ridge—were compared to the traditional Cox proportional hazards (coxph) model and the prior study which used the yearly-cohort-based time-invariant analysis. Using Harrell’s C index as our primary measure, we found that using both machine learning techniques and incorporating time-dependent covariates can improve predictive performance. Gradient boosting machine showed the best performance on test data in both time-invariant and time-varying covariates analysis.

We present our efforts of applying information visualization techniques to the domain of microseismic monitoring. Microseismic monitoring is a crucial process for a number of tasks related to oil and gas reservoir development, e.g., optimizing hydraulic fracturing operations and heavy-oil stimulation. Microseismic data has many challenging features including high dimensionality and uncertainty. We present a brief introduction to the domain of microseismic monitoring, and derive a set of tasks and data abstractions that can establish common ground between microseismic monitoring domain experts and visualization researchers. We then present FractVis, a prototype for visual analysis of microseismic data, describing the ongoing process of iteratively refining FractVis through close collaboration and consultation with domain experts. FractVis is designed to offer microseismic monitoring experts with visual analytic tools that allow investigation of the 3D spatial distribution of microseismic events, time-varying analysis and interactive exploration of high-dimensional parameter spaces, extensively complementing the existing tools in their disposal.

Observational studies have reported significant negative associations between sporadic non-steroidal anti-inflammatory drug (NSAID) use and keratinocyte carcinoma (KC) while reporting null results for regular use. This pattern may be partially explained by the operational expression of NSAID exposure and analytic model assumptions. Our goals were to quantify the association between NSAIDs and KC and to explore the impact of exposure metrics and modeling assumptions on observed associations. We conducted a prospective cohort study by linking data from the Veterans Affairs Topical Tretinoin Chemoprevention Trial and the VA Pharmacy Benefits Management database. NSAID use was categorized according to cyclooxygenase selectivity, timing of initiation, and frequency of use. Data were analyzed using time-varying and time-fixed multivariable-adjusted Cox proportional hazard models [Correction made here after initial online publication]. Simulated null data were generated and analyzed to explore potential biases introduced by the models and the exposure metrics. During a median follow-up time of 2 years for basal cell carcinoma and 2.5 years for squamous cell carcinoma, 472 occurrences of BCC and 309 occurrences of SCC were observed. Time-fixed analyses of NSAID exposure metrics produced significant negative associations, whereas time-varying analyses produced null results. Analysis of simulated null data revealed the potential for strong bias in the time-fixed analyses. This study did not identify a negative association between NSAIDs and KC. The disparity between the time-fixed and the time-varying analyses highlights the extent to which operational definitions of drug exposures and reliance on time-fixed methods may introduce bias.