Search

The modeling of genotype × environment interactions (GEI) is important to understand how new crops perform in different environments and management systems. Naked barley (Hordeum vulgareL.) is a type of barley where the hull threshes freely from the grain and can be used for multiple end uses, including food, malt, and animal feed. We examined the performance of a winter naked barley trial grown in organic conditions across three regions and 3 years in northern latitude regions of the United States. We recorded yield, test weight, heading date, plant height, threshability, winter survival, barley stripe rust (Puccinia striiformisf. sp. hordeiWestend), barley scald (Rhynchosporium communeZaffarano, McDonald and Linde sp. nov), and spot blotch (Cochliobolus sativus, anamorph Bipolaris sorokiniana(S. Ito & Kurib.). Barley stripe rust, plant height, heading date, preharvest sprouting, and threshability had high heritability and low GEI. Grain yield, test weight, scald, and winter survival had high GEI and were analyzed using the additive main effect and multiplicative interaction (AMMI) model, genotype plus GEI biplot (GGE), and Finlay–Wilkinson (FW) stability analysis. The environmental patterns explained by the AMMI and GGE models indicated that selection for high GEI traits should occur within target environments and across multiple years within the target environments. Environmental groups were separated by Oregon as the first group and Wisconsin and New York as the second group. Barley stripe rust, plant height, heading date, preharvest sprouting, and threshability had high heritability and low genotype × environment interactions.Grain yield, test weight, scald, and winter survival had high genotype × environment interaction effects.Environmental groupings were separated into OR as the first group and NY and WI as the second group.

Threshability, defined here as the propensity of grains to lose their hull after harvest, is a key trait in naked barley (Hordeum vulgareL.). While threshability is a defining characteristic of naked grains and has been found to be associated with grain size and shape, its genetic architecture is poorly described. The goals of this study were to identify quantitative trait loci associated with threshability and evaluate their utility as covariates in genomic prediction models. A genome‐wide association study identified two loci on chromosomes 2H and 3H associated with threshability. The locus on chromosome 2H accounted for 9.9% of the phenotypic variance explained (PVE). The locus on chromosomes 3H accounted for 7.8% of the PVE. With effects on threshability of 0.18 and 0.29 for each marker, respectively, these markers could have a limited impact when implemented in marker‐assisted selection. Predictive ability for threshability was 0.842 using a structured genomic best linear unbiased prediction model. Incorporation of the markers with significant associations as covariates in the model did not improve predictive ability. Predictive ability was improved by the use of a multi‐trait model including grain test weight. The high predictive ability for threshability overall indicate that genomic selection would be useful in selection for threshability in naked barley. Two markers were identified with significant associations to threshability.Predictive ability for threshability was 0.842 using a structured gBLUP model.Predictive ability was not increased by inclusion of identified QTL as covariates.Predictive ability was increased using a multi‐trait prediction model.

Three dimensional, whole-heart (3DWH) MRI is an established non-invasive imaging modality in patients with congenital heart disease (CHD) for the diagnosis of cardiovascular morphology and for clinical decision making. Current techniques utilise diaphragmatic navigation (dNAV) for respiratory motion correction and gating and are frequently limited by long acquisition times. This study proposes and evaluates the diagnostic performance of a respiratory gating-free framework, which considers respiratory image-based navigation (iNAV), and highly accelerated variable density Cartesian sampling in concert with non-rigid motion correction and low-rank patch-based denoising (iNAV-3DWH-PROST). The method is compared to the clinical dNAV-3DWH sequence in adult patients with CHD.

Myocardial quantitative susceptibility mapping (QSM) may offer better specificity to iron than conventional T2* imaging in the assessment of cardiac diseases, including intra-myocardial hemorrhage. However, the precision and repeatability of cardiac QSM have not yet been characterized. The aim of this study is to characterize these key metrics in a healthy volunteer cohort and show the feasibility of the method in patients.

Naked barley (Hordeum vulgare L.) grain threshes freely from its hull during harvesting and cleaning. Much of the available naked barley germplasm is unadapted to U.S. barley growing regions, and few genotypes have been selected to thrive under organic systems. The goal of this research was to characterize a set of spring naked barley genotypes for agronomic traits in the northern United States under organic conditions and estimate the degree of genotype × environment interaction (GEI). To achieve this goal, a multienvironment trial was conducted over 3 yr. Experiments were grown under organic conditions where traits including grain yield, test weight, plant height, heading date, and threshability were evaluated. Contributions of environment, genotype, and GEI to the phenotypic variance were calculated. In the tested germplasm, test weight, plant height, heading date, and threshability were found to have higher variance attributed to genotypic effects compared to GEI. Grain yield variance attributed to GEI was five times greater than that of genotype alone. Genotype + GEI (GGE) and location‐grouping (LG) biplots showed two sets of environments where naked barley genotypes performed similarly. Sensitivity analysis by Finlay–Wilkinson regression found that grain yield was highly sensitive to changes in environment mean yield. These results show that selection for grain yield should be conducted within mega‐environments to leverage GEI patterns. Heading date, plant height, test weight, and threshability can be selected across mega‐environments. There is five‐times greater genotype × environment interaction (GEI) than genetic variance for grain yield.Biplots define two mega‐environments in the northern United States for naked barley grain yield.Other assessed agronomic traits and threshability are more affected by genotype than GEI.

The aim of this study was to investigate the diagnostic accuracy and reader confidence for late-gadolinium enhancement (LGE) detection of a novel free-breathing, image-based navigated 3D whole-heart LGE sequence with fat–water separation, compared to a free-breathing motion-corrected 2D LGE sequence in patients with ischemic and non-ischemic cardiomyopathy. Cardiac MRI patients including the respective sequences were retrospectively included. Two independent, blinded readers rated image quality, depiction of segmental LGE and documented acquisition time, SNR, CNR and amount of LGE. Results were compared using the Friedman or the Kruskal–Wallis test. For LGE rating, a jackknife free-response receiver operating characteristic analysis was performed with a figure of merit (FOM) calculation. Forty-two patients were included, thirty-two were examined with a 1.5 T-scanner and ten patients with a 3 T-scanner. The mean acquisition time of the 2D sequence was significantly shorter compared to the 3D sequence (07:12 min vs. 09:24 min; p < 0.001). The 3D scan time was significantly shorter when performed at 3 T compared to 1.5 T (07:47 min vs. 09:50 min; p < 0.001). There were no differences regarding SNR, CNR or amount of LGE. 3D imaging had a significantly higher FOM (0.89 vs. 0.78; p < 0.001). Overall image quality ratings were similar, but 3D sequence ratings were higher for fine anatomical structures. Free-breathing motion-corrected 3D LGE with high isotropic resolution results in enhanced LGE-detection with higher confidence and better delineation of fine structures. The acquisition time for 3D imaging was longer, but may be reduced by performing on a 3 T-scanner.

The diagnosis of myocarditis by CMR requires the use of T2 and T1 weighted imaging, ideally incorporating parametric mapping. Current 2D mapping sequences are acquired sequentially and involve multiple breath-holds resulting in prolonged scan times and anisotropic image resolution. We developed an isotropic free-breathing 3D whole-heart sequence which allows simultaneous T1 and T2 mapping and validated it in patients with suspected acute myocarditis.

Coronary computed tomography angiography (CCTA) is recommended as the first line diagnostic imaging modality in low to intermediate risk individuals suspected of stable coronary artery disease (CAD). However, CCTA exposes patients to ionising radiation and potentially nephrotoxic contrast agents. Invasive coronary angiography (ICA) is the gold-standard investigation to guide coronary revascularisation strategy, however, invasive procedures incur an inherent risk to the patient. Coronary magnetic resonance angiography (Coronary MRA) avoids these issues. Nevertheless, clinical implementation is currently limited due to extended scanning durations, inconsistent image quality, and consequent lack of diagnostic accuracy. Several technical Coronary MRA innovations including advanced respiratory motion correction with 100% scan efficiency (no data rejection), fast image acquisition with motion-corrected undersampled image reconstruction and deep-learning (DL)-based automated planning have been implemented and now await clinical validation in multi-centre trials.

Quantification of three-dimensional (3D) cardiac anatomy is important for the evaluation of cardiovascular diseases. Changes in anatomy are indicative of remodeling processes as the heart tissue adapts to disease. Although robust segmentation methods exist for computed tomography angiography (CTA), few methods exist for whole-heart cardiovascular magnetic resonance angiograms (CMRA) which are more challenging due to variable contrast, lower signal to noise ratio and a limited amount of labeled data.

Coronary magnetic resonance angiography (coronary MRA) is increasingly being considered as a clinically viable method to investigate coronary artery disease (CAD). Accurate determination of the trigger delay to place the acquisition window within the quiescent part of the cardiac cycle is critical for coronary MRA in order to reduce cardiac motion. This is currently reliant on operator-led decision making, which can negatively affect consistency of scan acquisition. Recently developed deep learning (DL) derived software may overcome these issues by automation of cardiac rest period detection.

Cine Displacement Encoding with Stimulated Echoes (DENSE) facilitates the quantification of myocardial deformation, by encoding tissue displacements in the cardiovascular magnetic resonance (CMR) image phase, from which myocardial strain can be estimated with high accuracy and reproducibility. Current methods for analyzing DENSE images still heavily rely on user input, making this process time-consuming and subject to inter-observer variability. The present study sought to develop a spatio-temporal deep learning model for segmentation of the left-ventricular (LV) myocardium, as spatial networks often fail due to contrast-related properties of DENSE images.