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McCarty, E.

Publications and source records attributed to McCarty, E..

2 recordsLinked to original sources

Searching the Pinus taeda foliar mycobiome for emerging pathogens among brown spot needle blight and needlecast outbreaks in the Southeast United States

Needle pathogens cause the discoloration, death, or premature abscission of conifer foliage, which reduce growth and vigor, and repeated defoliation may eventually result in tree mortality. Since 2016, forest managers in the southeast United States have reported an increasing scale, frequency, and severity of needle disease outbreaks on the regions principal timber species, loblolly pine (Pinus taeda L.). These recent outbreaks are raising concern throughout the region, as needle diseases are not traditionally considered a threat to P. taeda. Lecanosticta acicola (Thum.) Syd., the native causal agent of brown-spot needle blight, has been recovered from some outbreaks, however, the full array of fungi associated with symptoms has not been explored. In this research, P. taeda foliage was collected from affected stands throughout the region and analyzed to identify fungi associated with needle disease symptoms. We employed both targeted molecular diagnostics, to confirm the presence or absence of L. acicola, and DNA metabarcoding, to characterize the foliar mycobiome and screen for other potential pathogens. Lecanosticta acicola was detected among symptomatic needles from multiple states, particularly in western portions of the P. taeda range but rarely from stands in eastern states. Fungal ITS1 metabarcoding revealed multiple pathogens in symptomatic needles and identified associations between known pathogens fungi and differing symptoms. Additionally, the fungal community composition of needles varied with patterns of symptom presentation. This study is the first regionwide assessment of fungi associated with recent large-scale needle disease outbreaks on P. taeda and identifies multiple pathogens that warrant study in greater detail.

microbiology↗

Multi-modal digital pathology for colorectal cancer diagnosis by high-plex immunofluorescence imaging and traditional histology of the same tissue section

Precision medicine is critically dependent on better methods for diagnosing and staging disease and predicting drug response. Histopathology using Hematoxylin and Eosin (H&E) stained tissue - not genomics - remains the primary diagnostic method in cancer. Recently developed highly-multiplexed tissue imaging methods promise to enhance research studies and clinical practice with precise, spatially-resolved, single-cell data. Here we describe the "Orion" platform for collecting and analyzing H&E and high-plex immunofluorescence (IF) images from the same cells in a whole-slide format suitable for diagnosis. Using a retrospective cohort of 74 colorectal cancer resections, we show that IF and H&E images provide human experts and machine learning algorithms with complementary information that can be used to generate interpretable, multiplexed image-based models predictive of progression-free survival. Combining models of immune infiltration and tumor-intrinsic features achieves a hazard ratio of [~]0.05, demonstrating the ability of multi-modal Orion imaging to generate high-performance biomarkers.

cancer biology↗