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Johnson, R. H.

Publications and source records attributed to Johnson, R. H..

2 recordsLinked to original sources

Population structure of human-infecting and environmental isolates of Coccidioides immitis, the fungus causing Valley fever

Coccidioides is a soil-dwelling fungus that can cause serious disease (coccidioidomycosis or Valley fever) when its spores are inhaled. Limiting exposure to environmental spores is critical for disease prevention, yet how Coccidioides is distributed in the soil and where people become exposed remain almost entirely unknown. Here we report the first genomes of environmental isolates of C. immitis, from sampling of rodent burrows in the highly endemic region--California's San Joaquin Valley. We sequenced 16 soil-derived isolates alongside 26 novel patient-derived isolates collected from the same region to investigate genetic structure and diversity in C. immitis, and the potential origins of human infection. We found evidence of regional population structure among environmental C. immitis strains, though with extensive allele sharing between clusters. Most patient isolates derived nearly 100% of their ancestry from a genetic cluster shared with soil isolates, suggesting that the Coccidioides strain inhaled by a human host represents a random draw from the environmental pathogen pool. Further, we found that high levels of genomic diversity in environmental C. immitis are partitioned into effectively clonal lineages within individual rodent burrows, with isolates from neighboring burrows differing by over 13,000 SNPs. Together, these data support a picture of diverse, regionally structured environmental populations of C. immitis, suggesting that strategies to assign patient exposures to general regions within the San Joaquin Valley may be possible.

ecology↗

Deep learning tools and modeling to estimate the temporal expression of E2Fs over the cell cycle from 2D still images

Automatic characterization of fluorescent labeling in intact mammalian tissues remains a challenge due to the lack of quantifying techniques capable of segregating densely packed nuclei and intricate tissue patterns. Here, we describe a powerful deep learning-based approach that couples remarkably precise nuclear segmentation with quantitation of fluorescent labeling intensity within segmented nuclei, and then apply it to the analysis of cell cycle dependent protein concentration in mouse tissues using 2D fluorescent still images. First, several existing deep learning-based methods were evaluated to accurately segment nuclei using different imaging modalities with a small training dataset. Next, we developed a deep learning-based approach to identify and measure fluorescent labels within segmented nuclei, and created an ImageJ plugin to allow for efficient manual correction of nuclear segmentation and label identification. Lastly, using fluorescence intensity as a readout for protein concentration, a three-step global estimation method was applied to the characterization of the cell cycle dependent expression of E2F proteins in the developing mouse intestine.

bioinformatics↗