Bayesian two-stage modeling of longitudinal and time-to-event data with an integrated fractional Brownian motion covariance structure.

It is difficult to characterize complex variations of biological processes, often longitudinally measured using biomarkers that yield noisy data. While joint modeling with a longitudinal submodel for the biomarker measurements and a survival submodel for assessing the hazard of events can alleviate measurement error issues, the continuous longitudinal submodel often uses random intercepts and slopes to estimate both between- and within-patient heterogeneity in biomarker trajectories. To overcome longitudinal submodel challenges, we replace random slopes with scaled integrated fractional Brownian motion (IFBM). As a more generalized version of integrated Brownian motion, IFBM reasonably depicts noisily measured biological processes. From this longitudinal IFBM model, we derive novel target functions to monitor the risk of rapid disease progression as real-time predictive probabilities. Predicted biomarker values from the IFBM submodel are used as inputs in a Cox submodel to estimate event hazard. This two-stage approach to fit the submodels is performed via Bayesian posterior computation and inference. We use the proposed approach to predict dynamic lung disease progression and mortality in women with a rare disease called lymphangioleiomyomatosis who were followed in a national patient registry. We compare our approach to those using integrated Ornstein-Uhlenbeck or conventional random intercepts-and-slopes terms for the longitudinal submodel. In the comparative analysis, the IFBM model consistently demonstrated superior predictive performance.

Multilevel joint model of longitudinal continuous and binary outcomes for hierarchically structured data.

Joint modeling has been a useful strategy for incorporating latent associations between different types of outcomes simultaneously, often focusing on a longitudinal continuous outcome characterized by an LME submodel and a terminal event subject to a Cox proportional hazard or parametric survival submodel. Applications to hierarchical longitudinal studies have been less frequent, particularly with respect to a binary process, which is commonly specified by a GLMM. Furthermore, many of the joint model developments have not allowed for investigations of nested effects, such as those arising from multicenter studies. To fill this gap, we propose a multilevel joint model that encompasses the LME submodel and GLMM through a Bayesian approach. Motivated by the need for timely detection of pulmonary exacerbation and characterization of irregularly observed lung function measurements in people living with cystic fibrosis (CF) receiving care across multiple centers, we apply the model to the data arising from US CF Foundation Patient Registry. In parallel, we examine the extent of bias induced by a non-hierarchical model. Our simulation study and application results show that incorporating the center effect along with individual stochastic variation over time within the LME submodel improves model estimation and prediction. Given that the center effect is evident in lung function observed in the CF population, accounting for center-specific power parameters by incorporating the symmetric power exponential power (spep) link function in the GLMM can facilitate more accurate conclusions in clinical studies.

Capillary-Force-Pattern Formation of Indium-Zinc-Oxide Thin-Film Transistor.

Studies of the pattern-formation technique used with solution-processed oxide thin-film transistors (TFTs) continue to explore its uses as an efficient manufacturing method. However, research remains to be completed to achieve high performance and to apply the refined technique to various current industrial technologies. We studied the patterning technique of solution-processed indiumzinc-oxide (IZO) by using the capillary-force phenomenon, the method of controlling the pattern of the IZO semiconductor layer, and approaches to reducing problems such as the cracking that occurs during patterning. The device we fabricated was filled uniformly with droplets in the capillary-force pattern. It showed a high current-on/current-off ratio, high mobility, low threshold voltage, and low subthreshold slope. Consequently, this paper demonstrates a strategy that uses the capillary-forcepattern technique to exceed the performance of traditional fabrication techniques in managing the electrical properties of solution-processed oxide TFTs.

Predicting Individualized Lung Disease Progression in Treatment-Naive Patients With Lymphangioleiomyomatosis.

BACKGROUND: Lung function decline varies significantly in patients with lymphangioleiomyomatosis (LAM), impeding individualized clinical decision-making. RESEARCH QUESTION: Can we aid individualized decision-making in LAM by developing a dynamic prediction model that can estimate the probability of clinically relevant FEV1 decline in patients with LAM before treatment initiation? STUDY DESIGN AND METHODS: Patients observed in the US National Heart, Lung, and Blood Institute (NHLBI) Lymphangioleiomyomatosis Registry were included. Using routinely available variables such as age at diagnosis, menopausal status, and baseline lung function (FEV1 and diffusing capacity of the lungs for carbon monoxide [Dlco]), we used novel stochastic modeling and evaluated predictive probabilities for clinically relevant drops in FEV1. We formed predictive probabilities of transplant-free survival by jointly modeling longitudinal FEV1 and lung transplantation or death events. External validation used the UK Lymphangioleiomyomatosis Natural History cohort. RESULTS: Analysis of the NHLBI Lymphangioleiomyomatosis Registry and UK Lymphangioleiomyomatosis Natural History cohorts consisted of 216 and 185 individuals, respectively. We derived a joint model that accurately estimated the risk of future lung function decline and 5-year probabilities of transplant-free survival in patients with LAM not taking sirolimus (area under the receiver operating characteristic curve [AUC], approximately 0.80). The prediction model provided estimates of forecasted FEV1, rate of FEV1 decline, and probabilities for risk of prolonged drops in FEV1 for untreated patients with LAM with a high degree of accuracy (AUC > 0.80) for the derivation cohort as well as the validation cohort. Our tool is freely accessible at: https://anushkapalipana.shinyapps.io/testapp_v2/. INTERPRETATION: Longitudinal modeling of routine clinical data can allow individualized LAM prognostication and assist in decision-making regarding the timing of treatment initiation.

Genetic diversity of microsatellite loci in hierarchically structured populations.

Microsatellite loci are widely used for investigating patterns of genetic variation within and among populations. Those patterns are in turn determined by population sizes, migration rates, and mutation rates. We provide exact expressions for the first two moments of the allele frequency distribution in a stochastic model appropriate for studying microsatellite evolution with migration, mutation, and drift under the assumption that the range of allele sizes is bounded. Using these results, we study the behavior of several measures related to Wright's F(ST), including Slatkin's R(ST). Our analytical approximations for F(ST) and R(ST) show that familiar relationships between N(e)m and F(ST) or R(ST) hold when the migration and mutation rates are small. Using the exact expressions for F(ST) and R(ST), our numerical results show that, when the migration and mutation rates are large, these relationships no longer hold. Our numerical results also show that the diversity measures most closely related to F(ST) depend on mutation rates, mutational models (stepwise versus two-phase), migration rates, and population sizes. Surprisingly, R(ST) is relatively insensitive to the mutation rates and mutational models. The differing behaviors of R(ST) and F(ST) suggest that properties of the among-population distribution of allele frequencies may allow the roles of mutation and migration in producing patterns of diversity to be distinguished, a topic of continuing investigation.

Development of a noise metric for assessment of exposure risk to complex noises.

Many noise guidelines currently use A-weighted equivalent sound pressure level L(Aeq) as the noise metric and the equal energy hypothesis to assess the risk of occupational noises. Because of the time-averaging effect involved with the procedure, the current guidelines may significantly underestimate the risk associated with complex noises. This study develops and evaluates several new noise metrics for more accurate assessment of exposure risks to complex and impulsive noises. The analytic wavelet transform was used to obtain time-frequency characteristics of the noise. 6 basic, unique metric forms that reflect the time-frequency characteristics were developed, from which 14 noise metrics were derived. The noise metrics were evaluated utilizing existing animal test data that were obtained by exposing 23 groups of chinchillas to, respectively, different types of noise. Correlations of the metrics with the hearing losses observed in chinchillas were compared and the most promising noise metric was identified.

Temporal dynamics in meta longitudinal RNA-Seq data.

Identification of differentially expressed genes has been a high priority task of downstream analyses to further advances in biomedical research. Investigators have been faced with an array of issues in dealing with more complicated experiments and metadata, including batch effects, normalization, temporal dynamics (temporally differential expression), and isoform diversity (isoform-level quantification and differential splicing events). To date, there are currently no standard approaches to precisely and efficiently analyze these moderate or large-scale experimental designs, especially with combined metadata. In this report, we propose comprehensive analytical pipelines to precisely characterize temporal dynamics in differential expression of genes and other genomic features, i.e., the variability of transcripts, isoforms and exons, by controlling batch effects and other nuisance factors that could have significant confounding effects on the main effects of interest in comparative models and may result in misleading interpretations.

A quantitative method for representing balance strategies of goal-directed human motions.

A movement index termed balance strategy vector (BSV) was developed as a research tool for studying human balance strategies during goal-directed motions. For a given motion (motion capture data), the BSV index quantitatively represents its balance strategy by computing how available body joint DoFs' angular motions affect the formation of the wholebody center-of-mass trajectory. The index facilitates easy and intuitive understanding of balance strategies of various human motions and can assist scientific investigations on human balance strategies. As an example demonstrating the utility of the index, the index was used to explore balance strategies of free-style, sagittal-plane load lifting motions.

Palatable Food Affects HPA Axis Responsivity and Forebrain Neurocircuitry in an Estrous Cycle-specific Manner in Female Rats.

Eating palatable foods can provide stress relief, but the mechanisms by which this occurs are unclear. We previously characterized a limited sucrose intake (LSI) paradigm in which twice-daily access to a small amount of 30% sucrose (vs. water as a control) reduces hypothalamic-pituitary-adrenocortical (HPA) axis responses to stress and alters neuronal activation in stress-regulatory brain regions in male rats. However, women may be more prone to 'comfort feeding' behaviors than men, and stress-related eating may vary across the menstrual cycle. This suggests that LSI effects may be sex- and estrous cycle-dependent. The present study therefore investigated the effects of LSI on HPA axis stress responsivity, as well as markers of neuronal activation/plasticity in stress- and reward-related neurocircuitry in female rats across the estrous cycle. We found that LSI reduced post-restraint stress plasma ACTH in female rats specifically during proestrus/estrus (P/E). LSI also increased basal (non-stress) FosB/deltaFosB- and pCREB-immunolabeling in the basolateral amygdala (BLA) and central amygdala specifically during P/E. Finally, Bayesian network modeling of the FosB/deltaFosB and pCREB expression data identified a neurocircuit that includes the BLA, nucleus accumbens, prefrontal cortex, and bed nucleus of the stria terminalis as likely being modified by LSI during P/E. When considered in the context of our prior results, the present findings suggest that palatable food reduces stress responses in female rats similar to males, but in an estrous cycle-dependent manner. Further, the BLA may contribute to the LSI effects in both sexes, whereas the involvement of other brain regions appears to be sex-dependent.

Differential Gene Expression (DEX) and Alternative Splicing Events (ASE) for Temporal Dynamic Processes Using HMMs and Hierarchical Bayesian Modeling Approaches.

In gene expression profile, data analysis pipeline is categorized into four levels, major downstream tasks, i.e., (1) identification of differential expression; (2) clustering co-expression patterns; (3) classification of subtypes of samples; and (4) detection of genetic regulatory networks, are performed posterior to preprocessing procedure such as normalization techniques. To be more specific, temporal dynamic gene expression data has its inherent feature, namely, two neighboring time points (previous and current state) are highly correlated with each other, compared to static expression data which samples are assumed as independent individuals. In this chapter, we demonstrate how HMMs and hierarchical Bayesian modeling methods capture the horizontal time dependency structures in time series expression profiles by focusing on the identification of differential expression. In addition, those differential expression genes and transcript variant isoforms over time detected in core prerequisite steps can be generally further applied in detection of genetic regulatory networks to comprehensively uncover dynamic repertoires in the aspects of system biology as the coupled framework.

Random-effects meta-analysis for systematic reviews of phase I clinical trials: Rare events and missing data.

Phase I trials aim to establish appropriate clinical and statistical parameters to guide future clinical trials. With individual trials typically underpowered, systematic reviews and meta-analysis are desired to assess the totality of evidence. A high percentage of zero or missing outcomes often complicate such efforts. We use a systematic review of pediatric phase I oncology trials as an example and illustrate the utility of advanced Bayesian analysis. Standard random-effects methods rely on the exchangeability of individual trial effects, typically assuming that a common normal distribution sufficiently describes random variation among the trial level effects. Summary statistics of individual trial data may become undefined with zero counts, and this assumption may not be readily examined. We conduct Bayesian semi-parametric analysis with a Dirichlet process prior and examine the assumption. The Bayesian semi-parametric analysis is also useful for visually summarizing individual trial data. It provides alternative statistics that are computed free of distributional assumptions about the shape of the population of trial level effects. Outcomes are rarely entirely missing in clinical trials. We utilize available information and conduct Bayesian incomplete data analysis. The advanced Bayesian analyses, although illustrated with the specific example, are generally applicable. © 2016 The Authors. Research Synthesis Methods Published by John Wiley & Sons Ltd.

The role of PDF neurons in setting the preferred temperature before dawn in Drosophila.

Animals have sophisticated homeostatic controls. While mammalian body temperature fluctuates throughout the day, small ectotherms, such as Drosophila achieve a body temperature rhythm (BTR) through their preference of environmental temperature. Here, we demonstrate that pigment dispersing factor (PDF) neurons play an important role in setting preferred temperature before dawn. We show that small lateral ventral neurons (sLNvs), a subset of PDF neurons, activate the dorsal neurons 2 (DN2s), the main circadian clock cells that regulate temperature preference rhythm (TPR). The number of temporal contacts between sLNvs and DN2s peak before dawn. Our data suggest that the thermosensory anterior cells (ACs) likely contact sLNvs via serotonin signaling. Together, the ACs-sLNs-DN2s neural circuit regulates the proper setting of temperature preference before dawn. Given that sLNvs are important for sleep and that BTR and sleep have a close temporal relationship, our data highlight a possible neuronal interaction between body temperature and sleep regulation.

Differentiation among populations with migration, mutation, and drift: implications for genetic inference.

Populations may become differentiated from one another as a result of genetic drift. The amounts and patterns of differentiation at neutral loci are determined by local population sizes, migration rates among populations, and mutation rates. We provide exact analytical expressions for the mean, variance, and covariance of a stochastic model for hierarchically structured populations subject to migration, mutation, and drift. In addition to the expected correlation in allele frequencies among populations in the same geographic region, we demonstrate that there is a substantial correlation in allele frequencies among regions at the top level of the hierarchy. We propose a hierarchical Bayesian model for inference of Wright's F-statistics in a two-level hierarchy in which we estimate the among-region correlation in allele frequencies by substituting replication across loci for replication across time. We illustrate the approach through an analysis of human microsatellite data, and we show that approaches ignoring the among-region correlation in allele frequencies underestimate the amount of genetic differentiation among major geographic population groups by approximately 30%. Finally, we discuss the implications of these results for the use and interpretation of F-statistics in evolutionary studies.

Finding Clocks in Genes: A Bayesian Approach to Estimate Periodicity.

Identification of rhythmic gene expression from metabolic cycles to circadian rhythms is crucial for understanding the gene regulatory networks and functions of these biological processes. Recently, two algorithms, JTK_CYCLE and ARSER, have been developed to estimate periodicity of rhythmic gene expression. JTK_CYCLE performs well for long or less noisy time series, while ARSER performs well for detecting a single rhythmic category. However, observing gene expression at high temporal resolution is not always feasible, and many scientists are interested in exploring both ultradian and circadian rhythmic categories simultaneously. In this paper, a new algorithm, named autoregressive Bayesian spectral regression (ABSR), is proposed. It estimates the period of time-course experimental data and classifies gene expression profiles into multiple rhythmic categories simultaneously. Through the simulation studies, it is shown that ABSR substantially improves the accuracy of periodicity estimation and clustering of rhythmic categories as compared to JTK_CYCLE and ARSER for the data with low temporal resolution. Moreover, ABSR is insensitive to rhythmic patterns. This new scheme is applied to existing time-course mouse liver data to estimate period of rhythms and classify the genes into ultradian, circadian, and arrhythmic categories. It is observed that 49.2% of the circadian profiles detected by JTK_CYCLE with 1-hour resolution are also detected by ABSR with only 4-hour resolution.

Statistical modeling implicates neuroanatomical circuit mediating stress relief by 'comfort' food.

A history of eating highly palatable foods reduces physiological and emotional responses to stress. For instance, we have previously shown that limited sucrose intake (4 ml of 30 % sucrose twice daily for 14 days) reduces hypothalamic-pituitary-adrenocortical (HPA) axis responses to stress. However, the neural mechanisms underlying stress relief by such 'comfort' foods are unclear, and could reveal an endogenous brain pathway for stress mitigation. As such, the present work assessed the expression of several proteins related to neuronal activation and/or plasticity in multiple stress- and reward-regulatory brain regions of rats after limited sucrose (vs. water control) intake. These data were then subjected to a series of statistical analyses, including Bayesian modeling, to identify the most likely neurocircuit mediating stress relief by sucrose. The analyses suggest that sucrose reduces HPA activation by dampening an excitatory basolateral amygdala-medial amygdala circuit, while also potentiating an inhibitory bed nucleus of the stria terminalis principle subdivision-mediated circuit, resulting in reduced HPA activation after stress. Collectively, the results support the hypothesis that sucrose limits stress responses via plastic changes to the structure and function of stress-regulatory neural circuits. The work also illustrates that advanced statistical methods are useful approaches to identify potentially novel and important underlying relationships in biological datasets.

The analytical landscape of static and temporal dynamics in transcriptome data.

Interpreting gene expression profiles often involves statistical analysis of large numbers of differentially expressed genes, isoforms, and alternative splicing events at either static or dynamic spectrums. Reduced sequencing costs have made feasible dense time-series analysis of gene expression using RNA-seq; however, statistical methods in the context of temporal RNA-seq data are poorly developed. Here we will review current methods for identifying temporal changes in gene expression using RNA-seq, which are limited to static pairwise comparisons of time points and which fail to account for temporal dependencies in gene expression patterns. We also review recently developed very few number of temporal dynamic RNA-seq specific methods. Application and development of RNA-specific temporal dynamic methods have been continuously under the development, yet, it is still in infancy. We fully cover microarray specific temporal methods and transcriptome studies in initial digital technology (e.g., SAGE) between traditional microarray and new RNA-seq.

Time series expression analyses using RNA-seq: a statistical approach.

RNA-seq is becoming the de facto standard approach for transcriptome analysis with ever-reducing cost. It has considerable advantages over conventional technologies (microarrays) because it allows for direct identification and quantification of transcripts. Many time series RNA-seq datasets have been collected to study the dynamic regulations of transcripts. However, statistically rigorous and computationally efficient methods are needed to explore the time-dependent changes of gene expression in biological systems. These methods should explicitly account for the dependencies of expression patterns across time points. Here, we discuss several methods that can be applied to model timecourse RNA-seq data, including statistical evolutionary trajectory index (SETI), autoregressive time-lagged regression (AR(1)), and hidden Markov model (HMM) approaches. We use three real datasets and simulation studies to demonstrate the utility of these dynamic methods in temporal analysis.

Handle grip span for optimising finger-specific force capability as a function of hand size.

Five grip spans (45 to 65 mm) were tested to evaluate the effects of handle grip span and user's hand size on maximum grip strength, individual finger force and subjective ratings of comfort using a computerised digital dynamometer with independent finger force sensors. Forty-six males participated and were assigned into three hand size groups (small, medium, large) according to their hands' length. In general, results showed the 55- and 50-mm grip spans were rated as the most comfortable sizes and showed the largest grip strength (433.6 N and 430.8 N, respectively), whereas the 65-mm grip span handle was rated as the least comfortable size and the least grip strength. With regard to the interaction effect of grip span and hand size, small and medium-hand participants rated the best preference for the 50- to 55-mm grip spans and the least for the 65-mm grip span, whereas large-hand participants rated the 55- to 60-mm grip spans as the most preferred and the 45-mm grip span as the least preferred. Normalised grip span (NGS) ratios (29% and 27%) are the ratios of user's hand length to handle grip span. The NGS ratios were obtained and applied for suggesting handle grip spans in order to maximise subjective comfort as well as gripping force according to the users' hand sizes. In the analysis of individual finger force, the middle finger force showed the highest contribution (37.5%) to the total finger force, followed by the ring (28.7%), index (20.2%) and little (13.6%) finger. In addition, each finger was observed to have a different optimal grip span for exerting the maximum force, resulting in a bow-contoured shaped handle (the grip span of the handle at the centre is larger than the handle at the end) for two-handle hand tools. Thus, the grip spans for two-handle hand tools may be designed according to the users' hand/finger anthropometrics to maximise subjective ratings and performance based on this study. Results obtained in this study will provide guidelines for hand tool designers and manufacturers for designing grip spans of two-handle tools, which can maximise handle comfort and performance.