MRI-based quantification of posterior ocular globe flattening during 60 days of strict 6° head-down tilt bed rest with and without daily centrifugation.

Spaceflight associated neuro-ocular syndrome (SANS) is associated with acquired optic disc edema, hyperopia, and posterior globe flattening in some astronauts during long-duration spaceflight possibly due to the headward fluid redistribution in microgravity. The goal of this study was to assess whether strict head-down tilt (HDT) bed rest as a spaceflight analog would produce globe flattening and whether centrifugation could prevent these changes. Twenty-four healthy subjects separated into three groups underwent 60 days of strict 6° HDT bed rest: one control group with no countermeasure ( = 8) and two countermeasure groups exposed to 30 min daily of short-arm centrifugation as a means of artificial gravity (AG), either intermittent (iAG, = 8) or continuous (cAG, = 8).

MRPC: An R Package for Inference of Causal Graphs.

Understanding the causal relationships between variables is a central goal of many scientific inquiries. Causal relationships may be represented by directed edges in a graph (or equivalently, a network). In biology, for example, gene regulatory networks may be viewed as a type of causal networks, where X→Y represents gene X regulating (i.

MRI-based quantification of ophthalmic changes in healthy volunteers during acute 15° head-down tilt as an analogue to microgravity.

Spaceflight is known to cause ophthalmic changes in a condition known as spaceflight-associated neuro-ocular syndrome (SANS). It is hypothesized that SANS is caused by cephalad fluid shifts and potentially mild elevation of intracranial pressure (ICP) in microgravity. Head-down tilt (HDT) studies are a ground-based spaceflight analogue to create cephalad fluid shifts.

Intrathecal catheter implantation decreases cerebrospinal fluid dynamics in cynomolgus monkeys.

A detailed understanding of the CSF dynamics is needed for design and optimization of intrathecal drug delivery devices, drugs, and protocols. Preclinical research using large-animal models is important to help define drug pharmacokinetics-pharmacodynamics and safety. In this study, we investigated the impact of catheter implantation in the sub-dural space on CSF flow dynamics in Cynomolgus monkeys.

Imputation of single-cell gene expression with an autoencoder neural network.

Background: Single-cell RNA-sequencing (scRNA-seq) is a rapidly evolving technology that enables measurement of gene expression levels at an unprecedented resolution. Despite the explosive growth in the number of cells that can be assayed by a single experiment, scRNA-seq still has several limitations, including high rates of dropouts, which result in a large number of genes having zero read count in the scRNA-seq data, and complicate downstream analyses.

Methods: To overcome this problem, we treat zeros as missing values and develop nonparametric deep learning methods for imputation.

Learning Causal Biological Networks With the Principle of Mendelian Randomization.

Although large amounts of genomic data are available, it remains a challenge to reliably infer causal (i. e., regulatory) relationships among molecular phenotypes (such as gene expression), especially when multiple phenotypes are involved.

Clinical Relevance of Noncoding Adenosine-to-Inosine RNA Editing in Multiple Human Cancers.

Purpose: RNA editing is a post-transcriptional process that alters the nucleotide sequences of certain transcripts, in vertebrate most often converting adenosines to inosines. Multiple studies have recently implicated RNA editing in cancer development; however, most studies have focused on recoding RNA editing events. The function and clinical relevance of noncoding RNA (ncRNA) editing events in cancers have not been systematically examined.

Inferring Relevant Cell Types for Complex Traits by Using Single-Cell Gene Expression.

Previous studies have prioritized trait-relevant cell types by looking for an enrichment of genome-wide association study (GWAS) signal within functional regions. However, these studies are limited in cell resolution by the lack of functional annotations from difficult-to-characterize or rare cell populations. Measurement of single-cell gene expression has become a popular method for characterizing novel cell types, and yet limited work has linked single-cell RNA sequencing (RNA-seq) to phenotypes of interest.

Estimating intrinsic and extrinsic noise from single-cell gene expression measurements.

Gene expression is stochastic and displays variation ("noise") both within and between cells. Intracellular (intrinsic) variance can be distinguished from extracellular (extrinsic) variance by applying the law of total variance to data from two-reporter assays that probe expression of identically regulated gene pairs in single cells. We examine established formulas [Elowitz, M.

Widespread genetic epistasis among cancer genes.

Quantitative genetic epistasis has been hypothesized to be an important factor in the development and progression of complex diseases. Cancers in particular are driven by the accumulation of mutations that may act epistatically during the course of the disease. However, as cancer mutations are uncovered at an unprecedented rate, determining which combinations of genetic alterations interact to produce cancer phenotypes remains a challenge.

Transcriptional dynamics elicited by a short pulse of notch activation involves feed-forward regulation by E(spl)/Hes genes.

Dynamic activity of signaling pathways, such as Notch, is vital to achieve correct development and homeostasis. However, most studies assess output many hours or days after initiation of signaling, once the outcome has been consolidated. Here we analyze genome-wide changes in transcript levels, binding of the Notch pathway transcription factor, CSL [Suppressor of Hairless, Su(H), in Drosophila], and RNA Polymerase II (Pol II) immediately following a short pulse of Notch stimulation.

A graphical modelling approach to the dissection of highly correlated transcription factor binding site profiles.

Inferring the combinatorial regulatory code of transcription factors (TFs) from genome-wide TF binding profiles is challenging. A major reason is that TF binding profiles significantly overlap and are therefore highly correlated. Clustered occurrence of multiple TFs at genomic sites may arise from chromatin accessibility and local cooperation between TFs, or binding sites may simply appear clustered if the profiles are generated from diverse cell populations.

Statistical inference of in vivo properties of human DNA methyltransferases from double-stranded methylation patterns.

DNA methyltransferases establish methylation patterns in cells and transmit these patterns over cell generations, thereby influencing each cell's epigenetic states. Three primary DNA methyltransferases have been identified in mammals: DNMT1, DNMT3A and DNMT3B. Extensive in vitro studies have investigated key properties of these enzymes, namely their substrate specificity and processivity.

STATISTICAL INFERENCE OF TRANSMISSION FIDELITY OF DNA METHYLATION PATTERNS OVER SOMATIC CELL DIVISIONS IN MAMMALS.

We develop Bayesian inference methods for a recently-emerging type of epigenetic data to study the transmission fidelity of DNA methylation patterns over cell divisions. The data consist of parent-daughter double-stranded DNA methylation patterns with each pattern coming from a single cell and represented as an unordered pair of binary strings. The data are technically difficult and time-consuming to collect, putting a premium on an efficient inference method.

Scoring overlapping and adjacent signals from genome-wide ChIP and DamID assays.

Much of the research utilising genome-wide ChIP and DamID assays aims to understand the combinatorial feature of transcription factor binding and the chromatin modification code. With these experimental methods becoming more affordable and widespread, the focus of research is shifting to making sense of the data. Amongst the many challenges arising from data analyses, we are concerned with identifying biologically meaningful co-occurrences of transcription factor binding or chromatin modifications, using genome-wide profiles generated from ChIP and DamID assays.

Comparison of multipoint linkage analyses for quantitative traits in the CEPH data: parametric LOD scores, variance components LOD scores, and Bayes factors.

We performed multipoint linkage analyses with multiple programs and models for several gene expression traits in the Centre d'Etude du Polymorphisme Humain families. All analyses provided consistent results for both peak location and shape. Variance-components (VC) analysis gave wider peaks and Bayes factors gave fewer peaks.

Comparison of marker types and map assumptions using Markov chain Monte Carlo-based linkage analysis of COGA data.

We performed multipoint linkage analysis of the electrophysiological trait ECB21 on chromosome 4 in the full pedigrees provided by the Collaborative Study on the Genetics of Alcoholism (COGA). Three Markov chain Monte Carlo (MCMC)-based approaches were applied to the provided and re-estimated genetic maps and to five different marker panels consisting of microsatellite (STRP) and/or SNP markers at various densities. We found evidence of linkage near the GABRB1 STRP using all methods, maps, and marker panels.