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.

Estimating mutation rates in a Markov branching process using approximate Bayesian computation.

Estimating microbial mutation rates is an essential task in evolutionary biology, with wide range applications in related fields such as virology, epidemiology, clinic and public health, and antibiotic research. Significant progress has been made on this research since 1943 when Luria-Delbrück fluctuation analysis was first introduced. However, existing estimators of mutation rates are heavily reliant on model assumptions in fluctuation analysis, and become less applicable to real microbial experiments which deviate from the model assumptions.

A validation study for a bat-inspired sonar sensing simulator.

Many species of bats rely on echoes to forage and navigate in densely vegetated environments. Foliage echoes in some cases can help bats gather information about the environment, whereas in others may generate clutter that can mask prey echoes during foraging. It is therefore important to study foliage echoes and their role in bat's sensory ecology.

Association testing for binary trees-A Markov branching process approach.

We propose a new approach to test associations between binary trees and covariates. In this approach, binary-tree structured data are treated as sample paths of binary fission Markov branching processes (bMBP). We propose a generalized linear regression model and developed inference procedures for association testing, including variable selection and estimation of covariate effects.

Estimate the Unknown Environment with Biosonar Echoes-A Simulation Study.

Unmanned aerial vehicles (UAVs) have shown great potential in various applications such as surveillance, search and rescue. To perform safe and efficient navigation, it is vitally important for a UAV to evaluate the environment accurately and promptly. In this work, we present a simulation study for the estimation of foliage distribution as a UAV equipped with biosonar navigates through a forest.

A simulation framework for bio-inspired sonar sensing with Unmanned Aerial Vehicles.

We introduce a unified simulation framework that generates natural sensing environments and produces biosonar echoes under various sensing scenarios. This framework produces rich sensory data with environmental information completely known, thus can be used for the training of robotic algorithms for biosonar-based Unmanned Aerial Vehicles. The simulated environment consists of random trees with full geometry of the tree foliage.

[Characteristics and Sources Apportionment of Fine Particulate Matter in a Typical Coastal City During the Heating Period].

To clarify the pollution characteristics and sources of PM in Weihai during the heating period, PM samples from ambient air were collected at three routine air quality monitoring sites from January to March 2018. The OC, EC, water-soluble ions, and elements in PM were analyzed, and the sources of PM were identified using the PMF model. The results showed that the average daily mass concentration of PM was (33.

Precision Implementation of Minimal Erythema Dose (MED) Testing to Assess Individual Variation in Human Inflammatory Response.

Minimal erythema dose (MED) testing is frequently used in clinical settings for determining the smallest amount of ultraviolet (UV) irradiation necessary to produce erythema (inflammatory reddening) on the surface of the skin. In this context, the MED is regarded as a key factor in determining starting doses for UV phototherapy for common skin conditions such as psoriasis and eczema. In research settings, MED testing also has potential to be a powerful tool for assessing within- and between-persons variation in inflammatory responses.

vi-HMM: a novel HMM-based method for sequence variant identification in short-read data.

Background: Accurate and reliable identification of sequence variants, including single nucleotide polymorphisms (SNPs) and insertion-deletion polymorphisms (INDELs), plays a fundamental role in next-generation sequencing (NGS) applications. Existing methods for calling these variants often make simplified assumptions of positional independence and fail to leverage the dependence between genotypes at nearby loci that is caused by linkage disequilibrium (LD).

Results And Conclusion: We propose vi-HMM, a hidden Markov model (HMM)-based method for calling SNPs and INDELs in mapped short-read data.

Identifying Transcriptional Regulatory Modules Among Different Chromatin States in Mouse Neural Stem Cells.

Gene expression regulation is a complex process involving the interplay between transcription factors and chromatin states. Significant progress has been made toward understanding the impact of chromatin states on gene expression. Nevertheless, the mechanism of transcription factors binding combinatorially in different chromatin states to enable selective regulation of gene expression remains an interesting research area.

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.

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.

Identification of Differentially Methylated Sites with Weak Methylation Effects.

Deoxyribonucleic acid (DNA) methylation is an epigenetic alteration crucial for regulating stress responses. Identifying large-scale DNA methylation at single nucleotide resolution is made possible by whole genome bisulfite sequencing. An essential task following the generation of bisulfite sequencing data is to detect differentially methylated cytosines (DMCs) among treatments.

A simplified model of biosonar echoes from foliage and the properties of natural foliages.

Foliage echoes could play an important role in the sensory ecology of echolocating bats, but many aspects of their sensory information content remain to be explored. A realistic numerical model for these echoes could support the development of hypotheses for the relationship between foliage properties and echo parameters. In prior work by the authors, a simple foliage model based on circular disks distributed uniformly in space has been developed.

A computational model for biosonar echoes from foliage.

Since many bat species thrive in densely vegetated habitats, echoes from foliage are likely to be of prime importance to the animals' sensory ecology, be it as clutter that masks prey echoes or as sources of information about the environment. To better understand the characteristics of foliage echoes, a new model for the process that generates these signals has been developed. This model takes leaf size and orientation into account by representing the leaves as circular disks of varying diameter.

Dynamic Substrate for the Physical Encoding of Sensory Information in Bat Biosonar.

Horseshoe bats have dynamic biosonar systems with interfaces for ultrasonic emission (reception) that change shape while diffracting the outgoing (incoming) sound waves. An information-theoretic analysis based on numerical and physical prototypes shows that these shape changes add sensory information (mutual information between distant shape conformations <20%), increase the number of resolvable directions of sound incidence, and improve the accuracy of direction finding. These results demonstrate that horseshoe bats have a highly effective substrate for dynamic encoding of sensory information.

A Bayesian analysis of quantal bioassay experiments incorporating historical controls via Bayes factors.

This paper addresses model-based Bayesian inference in the analysis of data arising from bioassay experiments. In such experiments, increasing doses of a chemical substance are given to treatment groups (usually rats or mice) for a fixed period of time (usually 2 years). The goal of such an experiment is to determine whether an increased dosage of the chemical is associated with increased probability of an adverse effect (usually presence of adenoma or carcinoma).

Functional CAR models for large spatially correlated functional datasets.

We develop a functional conditional autoregressive (CAR) model for spatially correlated data for which functions are collected on areal units of a lattice. Our model performs functional response regression while accounting for spatial correlations with potentially nonseparable and nonstationary covariance structure, in both the space and functional domains. We show theoretically that our construction leads to a CAR model at each functional location, with spatial covariance parameters varying and borrowing strength across the functional domain.

Mammalian Brain Development is Accompanied by a Dramatic Increase in Bipolar DNA Methylation.

DNA methylation is an epigenetic mechanism critical for tissue development and cell specification. Mammalian brains consist of many different types of cells with assumedly distinct DNA methylation profiles, and thus some genomic loci may demonstrate bipolar DNA methylation pattern, i.e.

'Cut from the same cloth': Shared microsatellite variants among cancers link to ectodermal tissues-neural tube and crest cells.

The pluripotent cells of the embryonic ectodermal tissues are known to be a precursor for multiple tumor types. The adaptability of these cells is a trait exploited by cancer. We previously described cancer-associated microsatellite loci (CAML) shared between glioblastoma (GBM) and lower-grade gliomas.

Nonparametric Bayesian clustering to detect bipolar methylated genomic loci.

Background: With recent development in sequencing technology, a large number of genome-wide DNA methylation studies have generated massive amounts of bisulfite sequencing data. The analysis of DNA methylation patterns helps researchers understand epigenetic regulatory mechanisms. Highly variable methylation patterns reflect stochastic fluctuations in DNA methylation, whereas well-structured methylation patterns imply deterministic methylation events.

Fast maximum likelihood estimation of mutation rates using a birth-death process.

Since fluctuation analysis was first introduced by Luria and Delbrück in 1943, it has been widely used to make inference about spontaneous mutation rates in cultured cells. Under certain model assumptions, the probability distribution of the number of mutants that appear in a fluctuation experiment can be derived explicitly, which provides the basis of mutation rate estimation. It has been shown that, among various existing estimators, the maximum likelihood estimator usually demonstrates some desirable properties such as consistency and lower mean squared error.

Somatic intronic microsatellite loci differentiate glioblastoma from lower-grade gliomas.

Genomic studies of glioma sub-types have amassed new disease specific mutations, yet these only partially explain how mutations are linked to predisposition or progression. We hypothesized that microsatellite variation could expand the understanding of glioma etiology. Furthermore, germline markers for gliomas are typically undetectable; therefore we also hypothesize that the predictability of cancer-associated microsatellite loci in germline DNA may support the current hypothesis of a glioma cell of origin.