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Methane (CH4) hydrate dissociation and CH4 release are potential geohazards currently investigated using X-ray computed tomography (XCT). Image segmentation is an important data processing step for this type of research. However, it is often time consuming, computing resource-intensive, operator-dependent, and tailored for each XCT dataset due to differences in greyscale contrast. In this paper, an investigation is carried out using U-Nets, a class of Convolutional Neural Network, to segment synchrotron XCT images of CH4-bearing sand during hydrate formation, and extract porosity and CH4 gas saturation. Three U-Net deployments previously untried for this task are assessed: (1) a bespoke 3D hierarchical method, (2) a 2D multi-label, multi-axis method and (3) RootPainter, a 2D U-Net application with interactive corrections. U-Nets are trained using small, targeted hand-annotated datasets to reduce operator time. It was found that the segmentation accuracy of all three methods surpass mainstream watershed and thresholding techniques. Accuracy slightly reduces in low-contrast data, which affects volume fraction measurements, but errors are small compared with gravimetric methods. Moreover, U-Net models trained on low-contrast images can be used to segment higher-contrast datasets, without further training. This demonstrates model portability, which can expedite the segmentation of large datasets over short timespans.

As sample preparation and imaging techniques have expanded and improved to include a variety of options for larger sized and numbers of samples, the bottleneck in volumetric imaging is now data analysis. Annotation and segmentation are both common, yet difficult, data analysis tasks which are required to bring meaning to the volumetric data. The SuRVoS application has been updated and redesigned to provide access to both manual and machine learning-based segmentation and annotation techniques, including support for crowd sourced data. Combining adjacent, similar voxels (supervoxels) provides a mechanism for speeding up segmentation both in the painting of annotation and by training a segmentation model on a small amount of annotation. The support for layers allows multiple datasets to be viewed and annotated together which, for example, enables the use of correlative data (e.g. crowd-sourced annotations or secondary imaging techniques) to guide segmentation. The ability to work with larger data on high-performance servers with GPUs has been added through a client-server architecture and the Pytorch-based image processing and segmentation server is flexible and extensible, and allows the implementation of deep learning-based segmentation modules. The client side has been built around Napari allowing integration of SuRVoS into an ecosystem for open-source image analysis while the server side has been built with cloud computing and extensibility through plugins in mind. Together these improvements to SuRVoS provide a platform for accelerating the annotation and segmentation of volumetric and correlative imaging data across modalities and scales.

JmjC histone demethylases (KDMs) are cancer targets due to their links to cell proliferation, but selective inhibition remains a challenge. Here the authors identify potent inhibitors of KDM4A-C—viain vitroselection from a vast library of cyclic peptides—that show selectivity over other KDMs.

BACKGROUND:Small molecule modulators of epigenetic processes are currently sought as basic probes for biochemical mechanisms, and as starting points for development of therapeutic agents. N(ε)-Methylation of lysine residues on histone tails is one of a number of post-translational modifications that together enable transcriptional regulation. Histone lysine demethylases antagonize the action of histone methyltransferases in a site- and methylation state-specific manner. N(ε)-Methyllysine demethylases that use 2-oxoglutarate as co-factor are associated with diverse human diseases, including cancer, inflammation and X-linked mental retardation; they are proposed as targets for the therapeutic modulation of transcription. There are few reports on the identification of templates that are amenable to development as potent inhibitors in vivo and large diverse collections have yet to be exploited for the discovery of demethylase inhibitors. PRINCIPAL FINDINGS:High-throughput screening of a ∼236,000-member collection of diverse molecules arrayed as dilution series was used to identify inhibitors of the JMJD2 (KDM4) family of 2-oxoglutarate-dependent histone demethylases. Initial screening hits were prioritized by a combination of cheminformatics, counterscreening using a coupled assay enzyme, and orthogonal confirmatory detection of inhibition by mass spectrometric assays. Follow-up studies were carried out on one of the series identified, 8-hydroxyquinolines, which were shown by crystallographic analyses to inhibit by binding to the active site Fe(II) and to modulate demethylation at the H3K9 locus in a cell-based assay. CONCLUSIONS:These studies demonstrate that diverse compound screening can yield novel inhibitors of 2OG dependent histone demethylases and provide starting points for the development of potent and selective agents to interrogate epigenetic regulation.