The Performance of Critical Congenital Heart Disease Screening in Rural Versus Urban Locations in the Northwest United States.

Objectives: Pulse oximetry screening of newborns detects critical congenital heart disease (CCHD). Rural birth location is known to affect timing and management of when infants with CHD undergo surgery, but its association with CCHD screening is unknown. We assess the relationship between rural location and postnatal CCHD diagnosis and describe lesion-specific modes of diagnosis.

Prevalence, risk factors, and surveillance patterns for gastric intestinal metaplasia among patients undergoing upper endoscopy with biopsy.

Background And Aims: Gastric intestinal metaplasia (GIM) is an important precursor lesion to gastric cancer (GC), the second leading cause of cancer death worldwide. There exist few data regarding the prevalence of, risk factors for, and clinical practice patterns regarding GIM in the United States. Furthermore, there are currently no U.

Most genetic risk for autism resides with common variation.

A key component of genetic architecture is the allelic spectrum influencing trait variability. For autism spectrum disorder (herein termed autism), the nature of the allelic spectrum is uncertain. Individual risk-associated genes have been identified from rare variation, especially de novo mutations.

REFINING GENETICALLY INFERRED RELATIONSHIPS USING TREELET COVARIANCE SMOOTHING.

Recent technological advances coupled with large sample sets have uncovered many factors underlying the genetic basis of traits and the predisposition to complex disease, but much is left to discover. A common thread to most genetic investigations is familial relationships. Close relatives can be identified from family records, and more distant relatives can be inferred from large panels of genetic markers.

An optimal transportation approach for nuclear structure-based pathology.

Nuclear morphology and structure as visualized from histopathology microscopy images can yield important diagnostic clues in some benign and malignant tissue lesions. Precise quantitative information about nuclear structure and morphology, however, is currently not available for many diagnostic challenges. This is due, in part, to the lack of methods to quantify these differences from image data.

A SPECTRAL GRAPH APPROACH TO DISCOVERING GENETIC ANCESTRY.

Mapping human genetic variation is fundamentally interesting in fields such as anthropology and forensic inference. At the same time, patterns of genetic diversity confound efforts to determine the genetic basis of complex disease. Due to technological advances, it is now possible to measure hundreds of thousands of genetic variants per individual across the genome.

Discovering genetic ancestry using spectral graph theory.

As one approach to uncovering the genetic underpinnings of complex disease, individuals are measured at a large number of genetic variants (usually SNPs) across the genome and these SNP genotypes are assessed for association with disease status. We propose a new statistical method called Spectral-GEM for the analysis of genome-wide association studies; the goal of Spectral-GEM is to quantify the ancestry of the sample from such genotypic data. Ignoring structure due to differential ancestry can lead to an excess of spurious findings and reduce power.

On the use of general control samples for genome-wide association studies: genetic matching highlights causal variants.

Resources being amassed for genome-wide association (GWA) studies include "control databases" genotyped with a large-scale SNP array. How to use these databases effectively is an open question. We develop a method to match, by genetic ancestry, controls to affected individuals (cases).

Diffusion maps and coarse-graining: A unified framework for dimensionality reduction, graph partitioning, and data set parameterization.

We provide evidence that nonlinear dimensionality reduction, clustering, and data set parameterization can be solved within one and the same framework. The main idea is to define a system of coordinates with an explicit metric that reflects the connectivity of a given data set and that is robust to noise. Our construction, which is based on a Markov random walk on the data, offers a general scheme of simultaneously reorganizing and subsampling graphs and arbitrarily shaped data sets in high dimensions using intrinsic geometry.