A Study of Mexican Free-Tailed Bat Chirp Syllables: Bayesian Functional Mixed Models for Nonstationary Acoustic Time Series

ANOPOW FOR REPLICATED NONSTATIONARY TIME SERIES IN EXPERIMENTS

We propose a novel analysis of power (ANOPOW) model for analyzing replicated nonstationary time series commonly encountered in experimental studies. Based on a locally stationary ANOPOW Cramér spectral representation, the proposed model can be used to compare the second-order time-varying frequency patterns among different groups of time series and to estimate group effects as functions of both time and frequency. Formulated in a Bayesian framework, independent two-dimensional second-order random walk (RW2D) priors are assumed on each of the time-varying functional effects for flexible and adaptive smoothing. A piecewise stationary approximation of the nonstationary time series is used to obtain localized estimates of time-varying spectra. Posterior distributions of the time-varying functional group effects are then obtained via integrated nested Laplace approximations (INLA) at a low computational cost. The large-sample distribution of local periodograms can be appropriately utilized to improve estimation accuracy since INLA allows modeling of data with various types of distributions. The usefulness of the proposed model is illustrated through two real data applications: analyses of seismic signals and pupil diameter time series in children with attention deficit hyperactivity disorder. Simulation studies, Supplementary Materials (Li, Yue and Bruce, 2023a), and R code (Li, Yue and Bruce, 2023b) for this article are also available.

Who is calling? Optimizing source identification from marmoset vocalizations with hierarchical machine learning classifiers

With their highly social nature and complex vocal communication system, marmosets are important models for comparative studies of vocal communication and, eventually, language evolution. However, our knowledge about marmoset vocalizations predominantly originates from playback studies or vocal interactions between dyads, and there is a need to move towards studying group-level communication dynamics. Efficient source identification from marmoset vocalizations is essential for this challenge, and machine learning algorithms (MLAs) can aid it. Here we built a pipeline capable of plentiful feature extraction, meaningful feature selection, and supervised classification of vocalizations of up to 18 marmosets. We optimized the classifier by building a hierarchical MLA that first learned to determine the sex of the source, narrowed down the possible source individuals based on their sex and then determined the source identity. We were able to correctly identify the source individual with high precisions (87.21%-94.42%, depending on call type, and up to 97.79% after the removal of twins from the dataset). We also examine the robustness of identification across varying sample sizes. Our pipeline is a promising tool not only for source identification from marmoset vocalizations but also for analysing vocalizations of other species.

Ultra-Fast Approximate Inference Using Variational Functional Mixed Models

While Bayesian functional mixed models have been shown effective to model functional data with various complex structures, their application to extremely high-dimensional data is limited due to computational challenges involved in posterior sampling. We introduce a new computational framework that enables ultra-fast approximate inference for high-dimensional data in functional form. This framework adopts parsimonious basis to represent functional observations, which facilitates efficient compression and parallel computing in basis space. Instead of performing expensive Markov chain Monte Carlo sampling, we approximate the posterior distribution using variational Bayes and adopt a fast iterative algorithm to estimate parameters of the approximate distribution. Our approach facilitates a fast multiple testing procedure in basis space, which can be used to identify significant local regions that reflect differences across groups of samples. We perform two simulation studies to assess the performance of approximate inference, and demonstrate applications of the proposed approach by using a proteomic mass spectrometry dataset and a brain imaging dataset. Supplementary materials are available online.

Robust and Gaussian spatial functional regression models for analysis of event-related potentials

Event-related potentials (ERPs) summarize electrophysiological brain response to specific stimuli. They can be considered as correlated functions of time with both spatial correlation across electrodes and nested correlations within subjects. Commonly used analytical methods for ERPs often focus on pre-determined extracted components and/or ignore the correlation among electrodes or subjects, which can miss important insights, and tend to be sensitive to outlying subjects, time points or electrodes. Motivated by ERP data in a smoking cessation study, we introduce a Bayesian spatial functional regression framework that models the entire ERPs as spatially correlated functional responses and the stimulus types as covariates. This novel framework relies on mixed models to characterize the effects of stimuli while simultaneously accounting for the multilevel correlation structure. The spatial correlation among the ERP profiles is captured through basis-space Matérn assumptions that allow either separable or nonseparable spatial correlations over time. We induce both adaptive regularization over time and spatial smoothness across electrodes via a correlated normal-exponential-gamma (CNEG) prior on the fixed effect coefficient functions. Our proposed framework includes both Gaussian models as well as robust models using heavier-tailed distributions to make the regression automatically robust to outliers. We introduce predictive methods to select among Gaussian vs. robust models and models with separable vs. non-separable spatiotemporal correlation structures. Our proposed analysis produces global tests for stimuli effects across entire time (or time-frequency) and electrode domains, plus multiplicity-adjusted pointwise inference based on experiment-wise error rate or false discovery rate to flag spatiotemporal (or spatio-temporal-frequency) regions that characterize stimuli differences, and can also produce inference for any prespecified waveform components. Our analysis of the smoking cessation ERP data set reveals numerous effects across different types of visual stimuli.

A Unified Analysis of Structured Sonar-terrain Data using Bayesian Functional Mixed Models

Sonar emits pulses of sound and uses the reflected echoes to gain information about target objects. It offers a low cost, complementary sensing modality for small robotic platforms. While existing analytical approaches often assume independence across echoes, real sonar data can have more complicated structures due to device setup or experimental design. In this paper, we consider sonar echo data collected from multiple terrain substrates with a dual-channel sonar head. Our goals are to identify the differential sonar responses to terrains and study the effectiveness of this dual-channel design in discriminating targets. We describe a unified analytical framework that achieves these goals rigorously, simultaneously, and automatically. The analysis was done by treating the echo envelope signals as functional responses and the terrain/channel information as covariates in a functional regression setting. We adopt functional mixed models that facilitate the estimation of terrain and channel effects while capturing the complex hierarchical structure in data. This unified analytical framework incorporates both Gaussian models and robust models. We fit the models using a full Bayesian approach, which enables us to perform multiple inferential tasks under the same modeling framework, including selecting models, estimating the effects of interest, identifying significant local regions, discriminating terrain types, and describing the discriminatory power of local regions. Our analysis of the sonar-terrain data identifies time regions that reflect differential sonar responses to terrains. The discriminant analysis suggests that a multi- or dual-channel design achieves target identification performance comparable with or better than a single-channel design.

Comparison and Contrast of Two General Functional Regression Modeling Frameworks

In this article, Greven and Scheipl describe an impressively general framework for performing functional regression that builds upon the generalized additive modeling framework. Over the past number of years, my collaborators and I have also been developing a general framework for functional regression, functional mixed models, which shares many similarities with this framework, but has many differences as well. In this discussion, I compare and contrast these two frameworks, to hopefully illuminate characteristics of each, highlighting their respecitve strengths and weaknesses, and providing recommendations regarding the settings in which each approach might be preferable.

Sound signatures and production mechanisms of three species of pipefishes (Family: Syngnathidae)

Background. Syngnathid fishes produce three kinds of sounds, named click, growl and purr. These sounds are generated by different mechanisms to give a consistent signal pattern or signature which is believed to play a role in intraspecific and interspecific communication. Commonly known sounds are produced when the fish feeds (click, purr) or is under duress (growl). While there are more acoustic studies on seahorses, pipefishes have not received much attention. Here we document the differences in feeding click signals between three species of pipefishes and relate them to cranial morphology and kinesis, or the sound-producing mechanism.

Methods. The feeding clicks of two species of freshwater pipefishes, Doryichthys martensii and Doryichthys deokhathoides and one species of estuarine pipefish, Syngnathoides biaculeatus, were recorded by a hydrophone in acoustic dampened tanks. The acoustic signals were analysed using time-scale distribution (or scalogram) based on wavelet transform. A detailed time-varying analysis of the spectral contents of the localized acoustic signal was obtained by jointly interpreting the oscillogram, scalogram and power spectrum. The heads of both Doryichthys species were prepared for microtomographical scans which were analysed using a 3D imaging software. Additionally, the cranial bones of all three species were examined using a clearing and double-staining method for histological studies.

Results. The sound characteristics of the feeding click of the pipefish is species-specific, appearing to be dependent on three bones: the supraoccipital, 1st postcranial plate and 2nd postcranial plate. The sounds are generated when the head of the Dorichthyes pipefishes flexes backward during the feeding strike, as the supraoccipital slides backwards, striking and pushing the 1st postcranial plate against (and striking) the 2nd postcranial plate. In the Syngnathoides pipefish, in the absence of the 1st postcranial plate, the supraoccipital rubs against the 2nd postcranial plate twice as it is pulled backward and released on the return. Cranial morphology and kinesis produce acoustic signals consistent with the bone strikes that produce sharp energy spikes (discrete or merged), or stridulations between bones that produce repeated or multimodal sinusoidal waveforms.

Discussion. The variable structure of the sound-producing mechanism explains the unique acoustic signatures of the three species of pipefish. The differences in cranial bone morphology, cranial kinesis and acoustic signatures among pipefishes (and seahorses) could be attributed to independent evolution within the Syngnathidae, which warrants further investigation.

Identification of differentially methylated loci using wavelet-based functional mixed models

MOTIVATION

DNA methylation is a key epigenetic modification that can modulate gene expression. Over the past decade, a lot of studies have focused on profiling DNA methylation and investigating its alterations in complex diseases such as cancer. While early studies were mostly restricted to CpG islands or promoter regions, recent findings indicate that many of important DNA methylation changes can occur in other regions and DNA methylation needs to be examined on a genome-wide scale. In this article, we apply the wavelet-based functional mixed model methodology to analyze the high-throughput methylation data for identifying differentially methylated loci across the genome. Contrary to many commonly-used methods that model probes independently, this framework accommodates spatial correlations across the genome through basis function modeling as well as correlations between samples through functional random effects, which allows it to be applied to many different settings and potentially leads to more power in detection of differential methylation.

RESULTS

We applied this framework to three different high-dimensional methylation data sets (CpG Shore data, THREE data and NIH Roadmap Epigenomics data), studied previously in other works. A simulation study based on CpG Shore data suggested that in terms of detection of differentially methylated loci, this modeling approach using wavelets outperforms analogous approaches modeling the loci as independent. For the THREE data, the method suggests newly detected regions of differential methylation, which were not reported in the original study.

AVAILABILITY AND IMPLEMENTATION

Automated software called WFMM is available at https://biostatistics.mdanderson.org/SoftwareDownload CpG Shore data is available at http://rafalab.dfci.harvard.edu NIH Roadmap Epigenomics data is available at http://compbio.mit.edu/roadmap

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

CONTACT

jefmorris@mdanderson.org.

Sound production in the tiger-tail seahorse Hippocampus comes: Insights into the sound producing mechanisms

Acoustic signals of the tiger-tail seahorse (Hippocampus comes) during feeding were studied using wavelet transform analysis. The seahorse "click" appears to be a compounded sound, comprising three acoustic components that likely come from two sound producing mechanisms. The click sound begins with a low-frequency precursor signal, followed by a sudden high-frequency spike that decays quickly, and a final, low-frequency sinusoidal component. The first two components can, respectively, be traced to the sliding movement and forceful knock between the supraorbital bone and coronet bone of the cranium, while the third one (purr) although appearing to be initiated here is produced elsewhere. The seahorse also produces a growling sound when under duress. Growling is accompanied by the highest recorded vibration at the cheek indicating another sound producing mechanism here. The purr has the same low frequency as the growl; both are likely produced by the same structural mechanism. However, growl and purr are triggered and produced under different conditions, suggesting that such "vocalization" may have significance in communication between seahorses.