Deep learning classification of chest x-ray images

We propose a deep learning based method for classification of commonly occurring pathologies in chest X-ray images. The vast number of publicly available chest X-ray images provides the data necessary for successfully employing deep learning methodologies to reduce the misdiagnosis of thoracic diseases. We applied our method to the classification of two example pathologies, pulmonary nodules and cardiomegaly, and we compared the performance of our method to three existing methods. The results show an improvement in AUC for detection of nodules and cardiomegaly compared to the existing methods.Comment: 4 pages, 4 figures, 2 tables, conference , SSIAI 202

From cheek swabs to consensus sequences : an A to Z protocol for high-throughput DNA sequencing of complete human mitochondrial genomes

Background: Next-generation DNA sequencing (NGS) technologies have made huge impacts in many fields of biological research, but especially in evolutionary biology. One area where NGS has shown potential is for high-throughput sequencing of complete mtDNA genomes (of humans and other animals). Despite the increasing use of NGS technologies and a better appreciation of their importance in answering biological questions, there remain significant obstacles to the successful implementation of NGS-based projects, especially for new users. Results: Here we present an ‘A to Z’ protocol for obtaining complete human mitochondrial (mtDNA) genomes – from DNA extraction to consensus sequence. Although designed for use on humans, this protocol could also be used to sequence small, organellar genomes from other species, and also nuclear loci. This protocol includes DNA extraction, PCR amplification, fragmentation of PCR products, barcoding of fragments, sequencing using the 454 GS FLX platform, and a complete bioinformatics pipeline (primer removal, reference-based mapping, output of coverage plots and SNP calling). Conclusions: All steps in this protocol are designed to be straightforward to implement, especially for researchers who are undertaking next-generation sequencing for the first time. The molecular steps are scalable to large numbers (hundreds) of individuals and all steps post-DNA extraction can be carried out in 96-well plate format. Also, the protocol has been assembled so that individual ‘modules’ can be swapped out to suit available resources

Neolithic Mitochondrial Haplogroup H Genomes and the Genetic Origins of Europeans

Haplogroup H dominates present-day Western European mitochondrial DNA variability (\u3e40%), yet was less common (~19%) among Early Neolithic farmers (~5450 BC) and virtually absent in Mesolithic hunter-gatherers. Here we investigate this major component of the maternal population history of modern Europeans and sequence 39 complete haplogroup H mitochondrial genomes from ancient human remains. We then compare this ‘real-time’ genetic data with cultural changes taking place between the Early Neolithic (~5450 BC) and Bronze Age (~2200 BC) in Central Europe. Our results reveal that the current diversity and distribution of haplogroup H were largely established by the Mid Neolithic (~4000 BC), but with substantial genetic contributions from subsequent pan-European cultures such as the Bell Beakers expanding out of Iberia in the Late Neolithic (~2800 BC). Dated haplogroup H genomes allow us to reconstruct the recent evolutionary history of haplogroup H and reveal a mutation rate 45% higher than current estimates for human mitochondria

Jetstream Annual Report FY1

National Science Foundation (Award 1445604

Usability Of A Smartphone Food Picture App For Assisting 24-Hour Dietary Recall: A Pilot Study

PubMedWoSScopu

Jetstream

Jetstream is a first-of-a-kind system for the NSF - a distributed production cloud resource. The NSF awarded funds to create Jetstream in November 2014. Here we review the purpose for creating Jetstream, present the acceptance test results that define Jetstream’s key characteristics, describe our experiences in standing up an OpenStack-based cloud environment, and share some of the early scientific results that have been obtained by researchers and students using this system. Jetstream offers unique capability within the XSEDE-supported US national cyberinfrastructure, delivering interactive virtual machines (VMs) via the Atmosphere interface developed by the University of Arizona. As a multi-region deployment that operates as a single integrated system, Jetstream is proving effective in supporting modes and disciplines of research traditionally underrepresented on larger XSEDE-supported clusters and supercomputers. Already, researchers in biology, network science, economics, earth science, and computer science have used Jetstream to perform research – much of it research in the “long tail of science.

Accelerate Synthesis in Ecology and Environmental Sciences

Ecology is a leading discipline in the synthesis of diverse knowledge. Ecologists have had considerable experience in bringing together diverse, multinational data sets, disciplines, and cultural perspectives to address a wide range of issues in basic and applied science. Now is the time to build on this foundation and invest in ecological synthesis through new national or international programs. While synthesis takes place through many mechanisms, including individual efforts, working groups, and research networks, centers are extraordinarily effective institutional settings for advancing synthesis projects