Biology subjects

Huff, D.

Publications and source records attributed to Huff, D..

2 recordsLinked to original sources

bioRxiv2023

Chromosome-scale genome assembly of Poa trivialis and population genomics reveals widespread gene flow in a cool-season grass seed production system

Poa trivialis (L.) is a cool-season grass species found in various environments worldwide. In addition to being a desired turfgrass species, it is a common weed of agricultural systems and natural areas. As a weed, it is an important contaminant of commercial cool-season grass seed lots, resulting in widespread gene flow facilitated by human activities and causing significant economic losses to farmers. To better understand and manage infestations, we assembled and annotated a haploid genome of P. trivialis and studied troublesome field populations from Oregon, the largest cool-season grass seed producing region in the United States. The genome assembly resulted in 1.35 Gb of DNA sequence distributed among seven chromosome-scale scaffolds, revealing a high content of transposable elements, conserved synteny with P. annua, and a close relationship with other C3 grasses. A reduced-representation sequencing analysis of field populations revealed limited genetic diversity and suggested potential gene flow and human-assisted dispersal in the region. The genetic resources and insights into P. trivialis provided by this study will improve weed management strategies and enable the development of molecular detection tests for contaminated seed lots to limit seed-mediated gene flow. These resources should also be beneficial for turfgrass breeders seeking to improve desirable traits of commercial P. trivialis varieties and help to guide breeding efforts in other crops to enhance the resiliency of agricultural ecosystems under climate change. Significance statementThe chromosome-scale assembly of Poa trivialis and population genomic analyses provide crucial insights into the gene flow of weedy populations and contribute a valuable genomic resource for the plant science community.

genomics↗

bioRxiv2019

Automated Quantification of Enlarged Perivascular Spaces in Clinical Brain MRI across Sites

Enlarged perivascular spaces (PVS) are structural brain changes visible in MRI, and are a marker of cerebral small vessel disease. Most studies use time-consuming and subjective visual scoring to assess these structures. Recently, automated methods to quantify enlarged perivascular spaces have been proposed. Most of these methods have been evaluated only in high resolution scans acquired in controlled research settings. We evaluate and compare two recently published automated methods for the quantification of enlarged perivascular spaces in 76 clinical scans acquired from 9 different scanners. Both methods are neural networks trained on high resolution research scans and are applied without fine-tuning the networks parameters. By adapting the preprocessing of clinical scans, regions of interest similar to those computed from research scans can be processed. The first method estimates only the number of PVS, while the second method estimates simultaneously also a high resolution attention map that can be used to detect and segment PVS. The Pearson correlations between visual and automated scores of enlarged perivascular spaces were higher with the second method. With this method, in the centrum semiovale, the correlation was similar to the inter-rater agreement, and also similar to the performance in high resolution research scans. Results were slightly lower than the inter-rater agreement for the hippocampi, and noticeably lower in the basal ganglia. By computing attention maps, we show that the neural networks focus on the enlarged perivascular spaces. Assessing the burden of said structures in the centrum semiovale with the automated scores reached a satisfying performance, could be implemented in the clinic and, e.g., help predict the bleeding risk related to cerebral amyloid angiopathy.

neuroscience↗