Annexin A2 (ANXA2), a phospholipid-binding protein, has multiple biological functions depending on its cellular localization. We previously demonstrated that IFN-γ-triggered ANXA2 secretion is ...associated with exosomal release. Here, we show that IFN-γ-induced autophagy is essential for the extracellular secretion of ANXA2 in lung epithelial cells. We observed colocalization of ANXA2-containing autophagosomes with multivesicular bodies (MVBs) after IFN-γ stimulation, followed by exosomal release. IFN-γ-induced exophagic release of ANXA2 could not be observed in ATG5-silenced or mutant RAB11-expressing cells. Furthermore, knockdown of RAB8A and RAB27A, but not RAB27B, reduced IFN-γ-triggered ANXA2 secretion. Surface translocation of ANXA2 enhanced efferocytosis by epithelial cells, and inhibition of different exophagic steps, including autophagosome formation, fusion of autophagosomes with MVBs, and fusion of amphisomes with plasma membrane, reduced ANXA2-mediated efferocytosis. Our data reveal a novel route of IFN-γ-induced exophagy of ANXA2.
Segmentation of 3D micro-Computed Tomographic (μCT) images of rock samples is essential for further Digital Rock Physics (DRP) analysis, however, conventional methods such as thresholding and ...watershed segmentation are susceptible to user-bias. Deep Convolutional Neural Networks (CNNs) have produced accurate pixelwise semantic (multi-category) segmentation results with natural images and μCT rock images, however, physical accuracy is not well documented. The performance of 4 CNN architectures is tested for 2D and 3D cases in 10 configurations. Manually segmented μCT images of Mt. Simon Sandstone guided by QEMSCANs are treated as ground truth and used as training and validation data, with a high voxelwise accuracy (over 99%) achieved. Downstream analysis is used to validate physical accuracy. The topology of each mineral is measured, the pore space absolute permeability and single/mixed wetting multiphase flow is modelled with direct simulation. These physical measures show high variance, with models that achieve 95%+ in voxelwise accuracy possessing permeabilities and connectivities orders of magnitude off. A network architecture is introduced as a hybrid fusion of U-Net and ResNet, combining short and long skip connections in a Network-in-Network configuration, which overall outperforms U-Net and ResNet variants in some minerals, while outperforming SegNet in all minerals in voxelwise and physical accuracy measures. The network architecture and the dataset volume fractions influence accuracy trade-off since sparsely occurring minerals are over-segmented by lower accuracy networks such as SegNet at the expense of under-segmenting other minerals which can be alleviated with loss weighting. This is an especially important consideration when training a physically accurate model for segmentation.
•Multi-Mineral Segmentation is performed by Convolutional Neural Networks in 2D and 3D.•A Hybrid U-Net ResNet Architecture is introduced, improving upon U-Net performance.•Physical accuracy measured on segmented minerals/pore space by digital petrophysics.•Mineral Topology, Single Phase Flow (Permeability), and Multi Phase Flow measured.•Networks with 95%–99% pixel/voxel accuracy still show high physical uncertainty.
Direct numerical simulations of flow on micro-computed tomography (micro-CT) images are extensively used in many disciplines of science and engineering. Recently, we have developed a pore-scale ...finite volume solver (PFVS) to directly solve for flow on micro-CT images and predict permeability of digital cores. The solver assigns a local conductivity to each voxel based on geometrical and topological constraints. The local conductivity term in PFVS is conventionally calculated by an iterative local scanning algorithm, where the number of iterations depends on the size of the largest flow channel. This can increase the computation time of PFVS significantly if the largest flow channel is reasonably large. In this paper, we apply convolutional neural networks (CNN) to predict local conductivity for each voxel, thus bypassing the iterative algorithm while also preserving the mass conservation in the system by still solving for flow using conventional methods. The network is trained to convert segmented binary images of rocks into a numerical map required for flow simulation by the use of paired image-to-image translation using a ResNet-Style architecture. Comparison of the generated and original coefficient maps shows that the average error is within 1% over the 3D pore geometries used in this study. Then, we compare the absolute permeability results obtained from the original PFVS and the CNN-PFVS and the errors are within 20% with the average of 13.8%. Machine learning improves the computation time significantly especially on the images with large domain size and flow channels. On the samples tested, the speedup factor is 10 times using CNN compared to iterative calculations.
Fluid mechanics simulation of steady state flow in complex geometries has many applications, from the micro-scale (cell membranes, filters, rocks) to macro-scale (groundwater, hydrocarbon reservoirs, ...and geothermal) and beyond. Direct simulation of steady state flow in such porous media requires significant computational resources to solve within reasonable timeframes. This study outlines an integrated method combining predictions of fluid flow (fast, limited accuracy) with direct flow simulation (slow, high accuracy) is outlined that reduces computation time by an order of magnitude without loss of accuracy. A convolutional neural network (CNNs) is trained with various configurations on simulations in 2D and 3D porous media to estimate steady state velocity fields. Permeability estimation (as an average of the field) is accurate, but the velocity fields themselves are error prone, unsuitable for further transport studies. This estimate can either be used as an indicative prediction, or as initial conditions in direct simulation to reach a fully accurate result in a fraction of the compute time. Using Deep Learning predictions (or potentially any other approximation method) to accelerate flow simulation to steady state in complex structures shows promise as a technique to push the boundaries fluid flow modelling.
Article Highlights
Steady State velocity fields predicted in 2D and 3D using CNNs
Permeability estimation with predicted fields over 95% accurate in most cases
Fine scale velocity field prediction is error-prone, limited by CNN performance
Fast, low accuracy CNN prediction is combined with slow, high accuracy simulation
Accelerated technique produces fully accurate results in 10x less time
Simulation of flow directly at the pore scale depends on high‐quality digital rock images but is constrained by detector hardware. A trade‐off between the image field of view (FOV) and image ...resolution is made. This can be compensated for with superresolution (SR) techniques that take a wide FOV, low‐resolution (LR) image, and superresolve a high resolution (HR). The Enhanced Deep Super Resolution Generative Adversarial Network (EDSRGAN) is trained on the DeepRock‐SR, a diverse compilation of raw and processed micro‐computed tomography (
μCT) images in 2D and 3D. The 2D and 3D trained networks show comparable performance of 50% to 70% reduction in relative error over bicubic interpolation with minimal computational cost during usage. Texture regeneration with EDSRGAN shows superior visual similarity versus Super Resolution Convolutional Neural Network (SRCNN) and other methods. Difference maps show SRCNN recovers large‐scale edge features while EDSRGAN regenerates perceptually indistinguishable high‐frequency texture. Physical accuracy is measured by permeability and phase topology on consistently segmented images, showing EDSRGAN results achieving the closest match. Performance is generalized with augmentation, showing high adaptability to noise and blur. HR images are fed into the network, generating HR‐SR images to extrapolate network performance to subresolution features present in the HR images themselves. Underresolution features are regenerated despite operating outside of trained specifications. Comparison with scanning electron microscopy (SEM) images shows details are consistent with the underlying geometry. Images that are normally constrained by the mineralogy of the rock, by fast transient imaging, or by the energy of the source can be superresolved accurately for further analysis.
Plain Language Summary
When capturing an X‐ray image of the insides of a rock sample (or any opaque object), hardware limitations on the image quality and size exist. These limitations can be overcome with the use of machine learning algorithms that “superresolve” a lower resolution image. Once trained, the machine algorithm can sharpen otherwise blurry features and regenerate the underlying texture of the imaged object. We train such an algorithm on a large and wide array of digital rock images and test its flexibility on some images that it had never seen before, as well as on some very high quality images that it was not trained to superresolve. The results of training and testing the algorithm shows a promising degree of accuracy and flexibility in handling a wide array of images of different quality and allows for higher quality images to be generated for use in other image‐based analysis techniques.
Key Points
Digital rock image quality improved beyond hardware limits
Method is useful for underresolved micro‐porous rocks and fast transient imaging
2D and 3D neural networks used, topology, and permeability are accurately measured
Phylogenetic analyses inferred from the nuc rDNA ITS1-5.8S-ITS2 (ITS) data set and the combined 2-locus data set 5.8S + nuc 28S rDNA (nLSU) of taxa of Trechisporales around the world show that
family ...forms a monophyletic lineage within Trechisporales. Bayesian evolutionary and divergence time analyses on two data sets of 5.8S and nLSU sequences indicate an ancient divergence of
family from Hydnodontaceae during the Triassic period (224.25 Mya).
family is characterized by resupinate and thin basidiomata, smooth, verruculose, or odontoid-semiporoid hymenophore, a monomitic hyphal structure, and generative hyphae bearing clamp connections, the presence of cystidia and hyphidia in some species, thin-walled, smooth, inamyloid, and acyanophilous basidiospores. In addition, four new species, namely,
,
,
, and
, are described and illustrated. In addition, three new combinations, namely,
,
, and
, are also proposed.
Myocardial ischemia/reperfusion (IR) injury is caused by the restoration of the coronary blood flow following an ischemic episode. Accumulating evidence suggests that galectin-3, a ...β-galactoside-binding lectin, acts as a biomarker in heart disease. However, it remains unclear whether manipulating galectin-3 affects the susceptibility of the heart to IR injury. In this study, RNA sequencing (RNA-seq) analysis identified that Lgals3 (galecin-3) plays an indispensable role in IR-induced cardiac damage. Immunostaining and immunoblot assays confirmed that the expression of galectin-3 was markedly increased in myocardial IR injury both in vivo and in vitro. Echocardiographic analysis showed that cardiac dysfunction in experimental IR injury was significantly attenuated by galectin-3 inhibitors including pectin (1%, i.p.) from citrus and binding peptide G3-C12 (5.0 mg/kg, i.p.). Galectin-3 inhibitor-treated mice exhibited smaller infarct sizes and decreased tissue injury. Furthermore, TUNEL staining showed that galectin-3 inhibition suppressed IR-mediated cardiomyocyte apoptosis. Mitochondrial membrane potential (MMP) and mitochondrial permeability transition pore (mPTP) levels were well-preserved and IR-induced changes of mitochondrial cyto c, cytosol cyto c, caspase-9, caspase-3, Bcl-2 and Bax in the galectin-3 inhibitor-treated groups were observed. Our findings indicate that the pathological upregulation of galectin-3 contributes to IR-induced cardiac dysfunction and that galectin-3 inhibition ameliorates myocardial injury, highlighting its therapeutic potential.
Calcaneal quantitative ultrasonography (QUS) is a useful prescreening tool for osteoporosis, while the dual-energy X-ray absorptiometry (DXA) is the mainstream in clinical practice. We evaluated the ...correlation between QUS and DXA in a Taiwanese population. A total of 772 patients were enrolled and demographic data were recorded with the QUS and DXA T-score over the hip and spine. The correlation coefficient of QUS with the DXA-hip was 0.171. For DXA-spine, it was 0.135 overall, 0.237 in females, and 0.255 in males. The logistic regression model using DXA-spine as a dependent variable was established, and the classification table showed 66.2% accuracy. A receiver operating characteristic (ROC) analyses with Youden's Index revealed the optimal cut-off point of QUS for predicting osteoporosis to be 2.72. This study showed a meaningful correlation between QUS and DXA in a Taiwanese population. Thus, it is important to pre-screen for osteoporosis with calcaneus QUS.
•Extending 2D MLA into 3D with assist of deep learning.•Micro-XRF coupling with micro-CT for 3D mineral identification.•EfficentU-Net was used to segment the 3D intact and crushed ore ...sample.•Comparison between 2D and 3D MLA stressed the importance of 3D MLA.•The MLA showed bias and sampling errors in 2D due to the heterogeneity of the sample.
Mineral liberation analysis (MLA) is an automated mineral analysis system that identifies minerals in polished two-dimensional (2D) sections of drill or lump cores or particulate mineral matter. MLA allows a wide range of mineral characteristics to be investigated, including fragment size, mineral abundance, and liberation. To date, this analysis has been primarily limited to two-dimensional (2D) section information. In this study, we describe an MLA workflow that enables the extension of MLA into 3D via the utilization of 3D X-ray microcomputed tomography and convolutional neural network (CNN) guided by M4-Tornado micro-X-ray fluorescence (micro-XRF) data. With the combination of 3D greyscale micro-CT data with several 2D identified element mappings, the state-of-the-art CNN architecture called EfficientU-Net-b3 is trained and tested for multimineral segmentation on both an intact complex iron ore sample and the corresponding crushed fragments. Compared to traditional manual segmentation methods, where only greyscale thresholds are selected by humans, CNN-based segmentation takes the information from unbiased microXRF and extracts not only the greyscale values but also the texture features from the image. After the segmentation of the 3D micro-CT datasets, several mineral liberation analyses are performed in the 3D domain, as well as 2D slices that are uniformly selected from the 3D segmented fragments data. The results from 2D and 3D MLA demonstrate that the 2D analysis results are heterogeneous and significantly different (up to a 14 % difference in the association indicator matrix) from the 3D analysis results. The loss of mineral information from 2D could influence ore body characterization and the proposed mineral processing procedure. Overall, the proposed workflow provides a digital mineral framework for 3D MLA for future ore characterization applications.
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