Breast density classification with deep convolutional neural networks

Breast density classification is an essential part of breast cancer screening. Although a lot of prior work considered this problem as a task for learning algorithms, to our knowledge, all of them used small and not clinically realistic data both for training and evaluation of their models. In this work, we explore the limits of this task with a data set coming from over 200,000 breast cancer screening exams. We use this data to train and evaluate a strong convolutional neural network classifier. In a reader study, we find that our model can perform this task comparably to a human expert

Consumers' Shopping Patterns and Expenditures on Ethnic Produce: A Case Study from the Eastern Coastal U.S.A.

This study was undertaken to examine the possible niche markets which East Coast farmers might be able to use to regain their advantage. Their future economic success could hinge on shifting the focus from traditional fruits and vegetables to high-value specialty ethnic produce for which there might be a growing demand. The study results indicate that there is a strong market demand and interest for ethnic produce in the East Coast. Local producers can benefit by concentrating their efforts in producing ethnic vegetables and fresh produce and making these newer products available in the local and regional markets.Food Consumption/Nutrition/Food Safety,

Granular clustering in a hydrodynamic simulation

We present a numerical simulation of a granular material using hydrodynamic equations. We show that, in the absence of external forces, such a system phase-separates into high density and low density regions. We show that this separation is dependent on the inelasticity of collisions, and comment on the mechanism for this clustering behavior. Our results are compatible with the granular clustering seen in experiments and molecular dynamic simulations of inelastic hard disks.Comment: 4 pages, 5 figure

Re-Analysis of Hydroacoustic Fish-Passage Data from Bonneville Dam after Spill-Discharge Corrections

The U.S. Army Corps of Engineers - Portland District asked Pacific Northwest National Laboratory to re-analyze four years of fixed-aspect hydroacoustic data after the District made adjustments to spill discharge estimates. In this report, we present new estimates of all major fish-passage metrics for study years 2000, 2001, 2002, and 2004, as well as estimates for 2005. This study supports the Portland District and its effort to maximize survival of juvenile salmon passing Bonneville Dam. Major passage routes through Bonneville Dam include 10 turbines and a sluiceway at Powerhouse 1 (B1), an 18-bay spillway, and eight turbines at Powerhouse 2 (B2) and a sluiceway including the B2 Corner Collector. The original reports and all associated results, discussion, and conclusions for non flow-related metrics remain valid and useful, but effectiveness measures for study years 2000, 2001, 2002, and 2004 as reported in previous reports by Ploskey et al. should be superseded with the new estimates reported here. The fish-passage metrics that changed the most were related to effectiveness. Re-analysis produced spill effectiveness estimates that ranged from 12% to 21% higher than previous estimates in spring and 16.7% to 27.5% higher in summer, but the mean spill effectiveness over all years was only slightly above 1:1 (1.17 for spring and 1.29 for summer). Conversely surface-passage effectiveness decreased in the years this metric was measured (by 10.1% in spring and 10.7% in summer of 2002 and 9.5% in spring and 10.2% in summer of 2004). The smallest changes in the re-analysis were in project fish passage efficiency (0%-1%) and spill efficiency (0.9%-3.0%)

miRGator: an integrated system for functional annotation of microRNAs

MicroRNAs (miRNAs) constitute an important class of regulators that are involved in various cellular and disease processes. However, the functional significance of each miRNA is mostly unknown due to the difficulty in identifying target genes and the lack of genome-wide expression data combining miRNAs, mRNAs and proteins. We introduce a novel database, miRGator, that integrates the target prediction, functional analysis, gene expression data and genome annotation. MiRNA function is inferred from the list of target genes predicted by miRanda, PicTar and TargetScanS programs. Statistical enrichment test of target genes in each term is performed for gene ontology, pathway and disease annotations. Associated terms may provide valuable insights for the function of each miRNA. For the expression analysis, miRGator integrates public expression data of miRNA with those of mRNA and protein. Expression correlation between miRNA and target mRNA/proteins is evaluated and their expression patterns can be readily compared. Our web implementation supports diverse query types including miRNA name, gene symbol, gene ontology, pathway and disease terms. Interfaces for exploring common targets or regulatory miRNAs and for profiling compendium expression data have been developed as well. Currently, miRGator, available at: http://genome.ewha.ac.kr/miRGator/, supports the human and mouse genomes