Epigenetic chromatin modifiers in barley: IV. The study of barley Polycomb group (PcG) genes during seed development and in response to external ABA

<p>Abstract</p> <p>Background</p> <p>Epigenetic phenomena have been associated with the regulation of active and silent chromatin states achieved by modifications of chromatin structure through DNA methylation, and histone post-translational modifications. The latter is accomplished, in part, through the action of PcG (Polycomb group) protein complexes which methylate nucleosomal histone tails at specific sites, ultimately leading to chromatin compaction and gene silencing. Different PcG complex variants operating during different developmental stages have been described in plants. In particular, the so-called FIE/MEA/FIS2 complex governs the expression of genes important in embryo and endosperm development in <it>Arabidopsis</it>. In our effort to understand the epigenetic mechanisms regulating seed development in barley (<it>Hordeum vulgare</it>), an agronomically important monocot plant cultivated for its endosperm, we set out to characterize the genes encoding barley PcG proteins.</p> <p>Results</p> <p>Four barley <it>PcG </it>gene homologues, named <it>HvFIE</it>, <it>HvE(Z), HvSu(z)12a</it>, and <it>HvSu(z)12b </it>were identified and structurally and phylogenetically characterized. The corresponding genes <it>HvFIE</it>, <it>HvE(Z), HvSu(z)12a</it>, and <it>HvSu(z)12b </it>were mapped onto barley chromosomes 7H, 4H, 2H and 5H, respectively. Expression analysis of the <it>PcG </it>genes revealed significant differences in gene expression among tissues and seed developmental stages and between barley cultivars with varying seed size. Furthermore, <it>HvFIE </it>and <it>HvE(Z) </it>gene expression was responsive to the abiotic stress-related hormone abscisic acid (ABA) known to be involved in seed maturation, dormancy and germination.</p> <p>Conclusion</p> <p>This study reports the first characterization of the <it>PcG </it>homologues, <it>HvFIE, HvE(Z)</it>, <it>HvSu(z)12a </it>and <it>HvSu(z)12b </it>in barley. All genes co-localized with known chromosomal regions responsible for malting quality related traits, suggesting that they might be used for developing molecular markers to be applied in marker assisted selection. The <it>PcG </it>differential expression pattern in different tissues and seed developmental stages as well as in two barley cultivars with different seed size is suggestive of a role for these genes in barley seed development. <it>HvFIE </it>and <it>HvE(Z) </it>were also found to be induced by the plant hormone ABA implying an association with ABA-mediated processes during seed development, germination and stress response.</p

Iron Deposition following Chronic Myocardial Infarction as a Substrate for Cardiac Electrical Anomalies: Initial Findings in a Canine Model

Purpose: Iron deposition has been shown to occur following myocardial infarction (MI). We investigated whether such focal iron deposition within chronic MI lead to electrical anomalies. Methods: Two groups of dogs (ex-vivo (n = 12) and in-vivo (n = 10)) were studied at 16 weeks post MI. Hearts of animals from ex-vivo group were explanted and sectioned into infarcted and non-infarcted segments. Impedance spectroscopy was used to derive electrical permittivity () and conductivity (). Mass spectrometry was used to classify and characterize tissue sections with (IRON+) and without (IRON-) iron. Animals from in-vivo group underwent cardiac magnetic resonance imaging (CMR) for estimation of scar volume (late-gadolinium enhancement, LGE) and iron deposition (T2*) relative to left-ventricular volume. 24-hour electrocardiogram recordings were obtained and used to examine Heart Rate (HR), QT interval (QT), QT corrected for HR (QTc) and QTc dispersion (QTcd). In a fraction of these animals (n = 5), ultra-high resolution electroanatomical mapping (EAM) was performed, co-registered with LGE and T2* CMR and were used to characterize the spatial locations of isolated late potentials (ILPs). Results: Compared to IRON- sections, IRON+ sections had higher, but no difference in. A linear relationship was found between iron content and (p1.5%)) with similar scar volumes (7.28%±1.02% (Iron (1.5%)), p = 0.51) but markedly different iron volumes (1.12%±0.64% (Iron (1.5%)), p = 0.02), QT and QTc were elevated and QTcd was decreased in the group with the higher iron volume during the day, night and 24-hour period (p<0.05). EAMs co-registered with CMR images showed a greater tendency for ILPs to emerge from scar regions with iron versus without iron. Conclusion: The electrical behavior of infarcted hearts with iron appears to be different from those without iron. Iron within infarcted zones may evolve as an arrhythmogenic substrate in the post MI period

The role of noise in denoising models for anomaly detection in medical images

Pathological brain lesions exhibit diverse appearance in brain images, in terms of intensity, texture, shape, size, and location. Comprehensive sets of data and annotations are difficult to acquire. Therefore, unsupervised anomaly detection approaches have been proposed using only normal data for training, with the aim of detecting outlier anomalous voxels at test time. Denoising methods, for instance classical denoising autoencoders (DAEs) and more recently emerging diffusion models, are a promising approach, however naive application of pixelwise noise leads to poor anomaly detection performance. We show that optimization of the spatial resolution and magnitude of the noise improves the performance of different model training regimes, with similar noise parameter adjustments giving good performance for both DAEs and diffusion models. Visual inspection of the reconstructions suggests that the training noise influences the trade-off between the extent of the detail that is reconstructed and the extent of erasure of anomalies, both of which contribute to better anomaly detection performance. We validate our findings on two real-world datasets (tumor detection in brain MRI and hemorrhage/ischemia/tumor detection in brain CT), showing good detection on diverse anomaly appearances. Overall, we find that a DAE trained with coarse noise is a fast and simple method that gives state-of-the-art accuracy. Diffusion models applied to anomaly detection are as yet in their infancy and provide a promising avenue for further research.Comment: Submitted to Medical Image Analysis special issue for MIDL 202

AutoRoot: open-source software employing a novel image analysis approach to support fully-automated plant phenotyping

Background: Computer-based phenotyping of plants has risen in importance in recent years. Whilst much software has been written to aid phenotyping using image analysis, to date the vast majority has been only semi-automatic. However, such interaction is not desirable in high throughput approaches. Here, we present a system designed to analyse plant images in a completely automated manner, allowing genuine high throughput measurement of root traits. To do this we introduce a new set of proxy traits. Results: We test the system on a new, automated image capture system, the Microphenotron, which is able to image many 1000s of roots/h. A simple experiment is presented, treating the plants with differing chemical conditions to produce different phenotypes. The automated imaging setup and the new software tool was used to measure proxy traits in each well. A correlation matrix was calculated across automated and manual measures, as a validation. Some particular proxy measures are very highly correlated with the manual measures (e.g. proxy length to manual length, r2 > 0.9). This suggests that while the automated measures are not directly equivalent to classic manual measures, they can be used to indicate phenotypic differences (hence the term, proxy). In addition, the raw discriminative power of the new proxy traits was examined. Principal component analysis was calculated across all proxy measures over two phenotypically-different groups of plants. Many of the proxy traits can be used to separate the data in the two conditions. Conclusion: The new proxy traits proposed tend to correlate well with equivalent manual measures, where these exist. Additionally, the new measures display strong discriminative power. It is suggested that for particular phenotypic differences, different traits will be relevant, and not all will have meaningful manual equivalent measures. However, approaches such as PCA can be used to interrogate the resulting data to identify differences between datasets. Select images can then be carefully manually inspected if the nature of the precise differences is required. We suggest such flexible measurement approaches are necessary for fully automated, high throughput systems such as the Microphenotron

AI in Medical Imaging Informatics: Current Challenges and Future Directions

This paper reviews state-of-the-art research solutions across the spectrum of medical imaging informatics, discusses clinical translation, and provides future directions for advancing clinical practice. More specifically, it summarizes advances in medical imaging acquisition technologies for different modalities, highlighting the necessity for efficient medical data management strategies in the context of AI in big healthcare data analytics. It then provides a synopsis of contemporary and emerging algorithmic methods for disease classification and organ/ tissue segmentation, focusing on AI and deep learning architectures that have already become the de facto approach. The clinical benefits of in-silico modelling advances linked with evolving 3D reconstruction and visualization applications are further documented. Concluding, integrative analytics approaches driven by associate research branches highlighted in this study promise to revolutionize imaging informatics as known today across the healthcare continuum for both radiology and digital pathology applications. The latter, is projected to enable informed, more accurate diagnosis, timely prognosis, and effective treatment planning, underpinning precision medicine

DNA methylation in diploid inbred lines of potatoes and its possible role in the regulation of heterosis

Self-incompatible diploid potatoes were altered to self-compatible ones by a function of S-locus inhibitor gene and continued selfing generated highly homozygous inbreds. In this study, this process was investigated for the status of DNA methylation by a simple method using genomic DNA digested by methylation-sensitive restriction enzymes prior to RAPD analysis. We detected 31 methylation-sensitive RAPD bands, of which 11 were newly appeared in the selfed progenies, and 6 of them stably inherited to subsequent generations. Aberrant segregations and paternal- or atavism-like transmission were also found. Segregating methylation-sensitive bands in initial populations became fixed in the advanced selfed progenies by 75.0–93.8%, of which 41.7% were fixed to all present and 58.3% to all absent. Because DNA methylation is generally recognized to suppress gene expression as regulatory factors, homozygosity/heterozygosity of methylated DNA may be involved in inbreeding depression/heterosis

Generative AI for Medical Imaging: extending the MONAI Framework

Recent advances in generative AI have brought incredible breakthroughs in several areas, including medical imaging. These generative models have tremendous potential not only to help safely share medical data via synthetic datasets but also to perform an array of diverse applications, such as anomaly detection, image-to-image translation, denoising, and MRI reconstruction. However, due to the complexity of these models, their implementation and reproducibility can be difficult. This complexity can hinder progress, act as a use barrier, and dissuade the comparison of new methods with existing works. In this study, we present MONAI Generative Models, a freely available open-source platform that allows researchers and developers to easily train, evaluate, and deploy generative models and related applications. Our platform reproduces state-of-art studies in a standardised way involving different architectures (such as diffusion models, autoregressive transformers, and GANs), and provides pre-trained models for the community. We have implemented these models in a generalisable fashion, illustrating that their results can be extended to 2D or 3D scenarios, including medical images with different modalities (like CT, MRI, and X-Ray data) and from different anatomical areas. Finally, we adopt a modular and extensible approach, ensuring long-term maintainability and the extension of current applications for future features

Leaf segmentation in plant phenotyping: a collation study

Image-based plant phenotyping is a growing application area of computer vision in agriculture. A key task is the segmentation of all individual leaves in images. Here we focus on the most common rosette model plants, Arabidopsis and young tobacco. Although leaves do share appearance and shape characteristics, the presence of occlusions and variability in leaf shape and pose, as well as imaging conditions, render this problem challenging. The aim of this paper is to compare several leaf segmentation solutions on a unique and first-of-its-kind dataset containing images from typical phenotyping experiments. In particular, we report and discuss methods and findings of a collection of submissions for the first Leaf Segmentation Challenge of the Computer Vision Problems in Plant Phenotyping workshop in 2014. Four methods are presented: three segment leaves by processing the distance transform in an unsupervised fashion, and the other via optimal template selection and Chamfer matching. Overall, we find that although separating plant from background can be accomplished with satisfactory accuracy (>>90 % Dice score), individual leaf segmentation and counting remain challenging when leaves overlap. Additionally, accuracy is lower for younger leaves. We find also that variability in datasets does affect outcomes. Our findings motivate further investigations and development of specialized algorithms for this particular application, and that challenges of this form are ideally suited for advancing the state of the art. Data are publicly available (online at http://​www.​plant-phenotyping.​org/​datasets) to support future challenges beyond segmentation within this application domain