Foliar Endophytic Fungi from the Endangered Eastern Mountain Avens (Geum peckii, Rosaceae) in Canada

Eastern Mountain Avens (Geum peckii Pursh, Rosaceae) is a globally rare and endangered perennial plant found only at two coastal bogs within Digby County (Nova Scotia, Canada) and at several alpine sites in the White Mountains of New Hampshire (USA). In Canada, the G. peckii population has declined over the past forty years due in part to habitat degradation. We investigated the culturable foliar fungi present in G. peckii leaves at five locations with varying degrees of human impact within this plant species’ Canadian range. Fungal identifications were made using ITS rDNA barcoding of axenic fungal cultures isolated from leaf tissue. Differences in foliar fungal communities among sites were documented, with a predominance of Gnomoniaceae (Class: Sordariomycetes, Phylum: Ascomycota). Habitats with more human impact showed lower endophytic diversities (10–16 species) compared to the pristine habitat (27 species). Intriguingly, several fungi may represent previously unknown taxa. Our work represents a significant step towards understanding G. peckii’s mycobiome and provides relevant data to inform conservation of this rare and endangered plant.

TXI: Texture and Color Enhancement Imaging for Endoscopic Image Enhancement

Recognition of lesions with subtle morphological and/or color changes during white light imaging (WLI) endoscopy remains a challenge. Often the endoscopic image suffers from nonuniform illumination across the image due to curvature in the lumen and the direction of the illumination light of the endoscope. We propose an image enhancement technology to resolve the drawbacks above called texture and color enhancement imaging (TXI). TXI is designed to enhance three image factors in WLI (texture, brightness, and color) in order to clearly define subtle tissue differences. In our proposed method, retinex-based enhancement is employed in the chain of endoscopic image processing. Retinex-based enhancement is combined with color enhancement to greatly accentuate color tone differences of mucosal surfaces. We apply TXI to animal endoscopic images and evaluate the performance of TXI compared with conventional endoscopic enhancement technologies, conventionally used techniques for real-world image processing, and newly proposed techniques for surgical endoscopic image augmentation. Our experimental results show that TXI can enhance brightness selectively in dark areas of an endoscopic image and can enhance subtle tissue differences such as slight morphological or color changes while simultaneously preventing over-enhancement. These experimental results demonstrate the potential of the proposed TXI algorithm as a future clinical tool for detecting gastrointestinal lesions having difficult-to-recognize tissue differences.

Impact of between-tissue differences on pan-cancer predictions of drug sensitivity

Increased availability of drug response and genomics data for many tumor cell lines has accelerated the development of pan-cancer prediction models of drug response. However, it is unclear how much between-tissue differences in drug response and molecular characteristics may contribute to pan-cancer predictions. Also unknown is whether the performance of pan-cancer models could vary by cancer type. Here, we built a series of pan-cancer models using two datasets containing 346 and 504 cell lines, each with MEK inhibitor (MEKi) response and mRNA expression, point mutation, and copy number variation data, and found that, while the tissue-level drug responses are accurately predicted (between-tissue ρ = 0.88–0.98), only 5 of 10 cancer types showed successful within-tissue prediction performance (within-tissue ρ = 0.11–0.64). Between-tissue differences make substantial contributions to the performance of pan-cancer MEKi response predictions, as exclusion of between-tissue signals leads to a decrease in Spearman’s ρ from a range of 0.43–0.62 to 0.30–0.51. In practice, joint analysis of multiple cancer types usually has a larger sample size, hence greater power, than for one cancer type; and we observe that higher accuracy of pan-cancer prediction of MEKi response is almost entirely due to the sample size advantage. Success of pan-cancer prediction reveals how drug response in different cancers may invoke shared regulatory mechanisms despite tissue-specific routes of oncogenesis, yet predictions in different cancer types require flexible incorporation of between-cancer and within-cancer signals. As most datasets in genome sciences contain multiple levels of heterogeneity, careful parsing of group characteristics and within-group, individual variation is essential when making robust inference.

Abstract 13593: Global Aortic Stiffness in Bicuspid-associated Aortopathy is Independent of Aortic Dimensions and Similar to Tricuspid Aortic Valve Control Subjects

Introduction: Bicuspid aortic valve (BAV) carries high aortopathy risk, and debate exists about relative contributions of altered wall structure vs. BAV-mediated hemodynamics. Pulse wave velocity (PWV), a surrogate for stiffness, can be quantified from 4D flow MRI. Here we use PWV to study wall stiffness in a large cohort of BAV aortic dilatation patients and two control groups. Hypotheses: 1. abnormal thoracic aortic (Ao) wall biomechanics for BAV patients result in altered PWV compared to controls and 2. PWV correlates with Ao diameter. Methods: This retrospective IRB approved study included 483 subjects: 124 healthy (no known cardiovascular disease), 168 BAV patients with ascending Ao (AAo) dilatation—maximal-area AAo (MAA) or sinus of Valsalva (SOV) diameter ≥4cm—and 191 TAV AAo dilatation (see table). No subjects had valve stenosis or ejection fraction ≤50%. Global PWV was assessed from MRI by cross-correlating flow in 80-100 Ao cutplanes and median AAo diameter by geometric mesh analysis of 3D Ao segmentations. Results: In multivariate regression, older age was the most significant predictor of higher PWV (controls: 0.073 m/s / y; TAV: 0.072 m/s / y; BAV: 0.092 m/s / y; all p<1E-4). Increased Ao diameter in controls associated with higher PWV (0.10 m/s / mm, p=0.03). Both BAV and TAV patients had no association between any Ao diameter (MAA, SOV, median) and PWV (p≥0.1 for all metrics). Between subject groups in a given age range, no significant differences of PWV existed except in some younger groups (see image; p≤0.04 in some under-40y). Conclusion: Global Ao wall stiffness from PWV in BAV Ao dilatation patients has minimal/no significant difference from non-BAV control PWV, despite genetic factors and different wall structure in BAV patients. This suggests wall tissue differences of BAV patients do not coincide with globally altered Ao stiffness. However, BAV-mediated Ao flow changes manifest regionally, and further study of localized properties would be valuable.

Impact of between-tissue differences on pan-cancer predictions of drug sensitivity

ABSTRACTIncreased availability of drug response and genomics data for many tumor cell lines has accelerated the development of pan-cancer prediction models of drug response. However, it is unclear how much between-tissue differences in drug response and molecular characteristics may contribute to pan-cancer predictions. Also unknown is whether the performance of pan-cancer models could vary by cancer type. Here, we built a series of pan-cancer models using two datasets containing 346 and 504 cell lines with MEK inhibitor (MEKi) response and RNA, SNP, and CNV data, and found that, while the tissue-level drug responses are accurately predicted (between-tissue ρ=0.88-0.98), only 5 of 10 cancer types showed successful within-tissue prediction performance (within-tissue ρ=0.11-0.64). Between-tissue differences make substantial contributions to the performance of pan-cancer MEKi response predictions, as we estimate that exclusion of between-tissue signals leads to a 22% decrease in performance metrics. In practice, joint analysis of multiple cancer types usually has a larger sample size, hence greater power, than for one cancer type; and we observe that the higher accuracy of pan-cancer prediction of MEKi response is almost entirely due to the sample size advantage. Success of pan-cancer prediction reveals how drug response in different cancers may invoke shared regulatory mechanisms despite tissue-specific routes of oncogenesis, yet predictions in different cancer types require flexible incorporation of between-cancer and within-cancer signals. As most datasets in genome sciences contain multiple levels of heterogeneity, careful parsing of group characteristics and within-group, individual variation is essential when making robust inference.