Damage Evolution Characteristics and Constitutive Model for Coal and Rock under Asymmetric Loading.

Tunnels and shaft mining roadways are often subjected to varying degrees of asymmetric loading due to terrain relief or project excavation. In order to analyze the influence of the asymmetric degree of loading on the mechanical properties and damage rupture law of coal rock, uniaxial compression tests of coal rock under four asymmetric loading modes were carried out, the influence of the asymmetric coefficients of loading on macro- and micromechanical properties of coal and rock was analyzed, and a statistical damage constitutive model of coal and rock was established to reflect the asymmetric loading degree. The results of the study show that the peak stress of the coal rock decreases gradually with the increase in the asymmetric coefficient of loading, and the two are linear functions of each other. The distribution of the acoustic emission ringing count peak value is concentrated under uniform loading, while the acoustic emission ringing count rate presents a multipeak phenomenon under asymmetric loading, and the peak value points are scattered. In the case of asymmetric loading, the stress concentration on the edge of the upper loading plate leads to shear failure, and the microscopic cracks are concentrated near the interface between the loading zone and the nonloading zone. According to the established damage constitutive model, when the damage degree is the same, the larger the asymmetric coefficient, the smaller the strain value, which indicates that the asymmetric loading promotes the damage of coal and rock.

Synergistic Effect of Small Molecular Organic Matter and Functional Groups in Coal on Methane Adsorption.

Functional groups and small-molecule organic matter are two key parts of coal. To explore the microscopic mechanism underlying the synergistic effect of both parts on methane adsorption, the oxygen-containing (-OH, -COOH, and -C=O) and nitrogen-containing (-NH2) functional groups and two common small molecular organic matter methylbenzene and tetrahydrofuran-2-alcohol in coal are selected. The quantum chemical meta-GGA functional method is used to optimize all structures. The electrostatic potential analyses, weak interaction analyses, and theory of atoms in molecules have been used to delve further into the nature of this synergistic effect. Our results show that functional groups inhibit methane adsorption by coal molecules, and the inhibition effect is enhanced in the presence of methylbenzene. Interestingly, the synergistic effects between functional groups and small molecular organic matter are changed from inhibition to promotion after introducing tetrahydrofuran-2-alcohol, wherein -COOH has the best synergistic effect. This work not only offers a theoretical foundation for exploring the synergistic effect of small molecular organic matter and functional groups on methane adsorption by coal molecules but also lays a foundation for further research on gas prevention and extraction.

Vocal Cord Leukoplakia Classification Using Siamese Network Under Small Samples of White Light Endoscopy Images.

OBJECTIVE: Accurate vocal cord leukoplakia classification is instructive for clinical diagnosis and surgical treatment. This article introduces a reliable very deep Siamese network for accurate vocal cord leukoplakia classification. STUDY DESIGN: A study of a classification network based on a retrospective database. SETTING: Academic university and hospital. METHODS: The white light image datasets of vocal cord leukoplakia used in this article were classified into 6 classes: normal tissues, inflammatory keratosis, mild dysplasia, moderate dysplasia, severe dysplasia, and squamous cell carcinoma. The classification performance was assessed by comparing it with 6 classical deep learning models, including AlexNet, VGG Net, Google Inception, ResNet, DenseNet, and Vision Transformer. RESULTS: Experiments show the superior classification performance of our proposed network compared to state-of-the-art methods. The overall accuracy is 0.9756. The values of sensitivity and specificity are very high as well. The confusion matrix provides information for the 6-class classification task and demonstrates the superiority of our proposed network. CONCLUSION: Our very deep Siamese network can provide accurate classification results of vocal cord leukoplakia, which facilitates early detection, clinical diagnosis, and surgical treatment. The excellent performance obtained in white light images can reduce the cost for patients, especially those living in developing countries.

Vocal cord leukoplakia classification using deep learning models in white light and narrow band imaging endoscopy images.

BACKGROUND: Accurate vocal cord leukoplakia classification is critical for the individualized treatment and early detection of laryngeal cancer. Numerous deep learning techniques have been proposed, but it is unclear how to select one to apply in the laryngeal tasks. This article introduces and reliably evaluates existing deep learning models for vocal cord leukoplakia classification. METHODS: We created white light and narrow band imaging (NBI) image datasets of vocal cord leukoplakia which were classified into six classes: normal tissues (NT), inflammatory keratosis (IK), mild dysplasia (MiD), moderate dysplasia (MoD), severe dysplasia (SD), and squamous cell carcinoma (SCC). Vocal cord leukoplakia classification was performed using six classical deep learning models, AlexNet, VGG, Google Inception, ResNet, DenseNet, and Vision Transformer. RESULTS: GoogLeNet (i.e., Google Inception V1), DenseNet-121, and ResNet-152 perform excellent classification. The highest overall accuracy of white light image classification is 0.9583, while the highest overall accuracy of NBI image classification is 0.9478. These three neural networks all provide very high sensitivity, specificity, and precision values. CONCLUSION: GoogLeNet, ResNet, and DenseNet can provide accurate pathological classification of vocal cord leukoplakia. It facilitates early diagnosis, providing judgment on conservative treatment or surgical treatment of different degrees, and reducing the burden on endoscopists.

Research on Resistance to Water Intoxication of LaCoO3 Doped with Fe at B Sites.

In this paper, the methods of spin polarization density functional theory and vasp software package are used to simulate the adsorption of H2O molecules on the surface of LaCoO3 and La2CoFeO6(001). It was found that when Fe was doped at B-sites, the adsorption energy changed from -3.7493 eV at CoO2 to -2.5397 eV at CoFeO4, which decreased by about 1/3. Meanwhile, the change of electric charge and the amount of electron transfer decreased overall. The results indicated that Fe doping could inhibit the adsorption of H2O by perovskites and thus hinder the next toxic reaction. Therefore, this paper will lay a certain theoretical foundation for the study of perovskite anti-poisoning mechanism and provide a meaningful reference for further experimental research.

Promoting the mechanism of OMS-2 for gas adsorption in different K+ concentrations.

Catalytic combustion technology is an efficient and green method to deal with low concentration methane. Gas adsorption over the catalyst surface is a key step in the catalytic combustion process, which has attracted much interest. In this work, the first-principles density functional theory calculation method has been applied to explore the adsorption processes of CH4 and O2 molecules on the surface of cryptomelane type manganese oxide octahedral molecular sieves (OMS-2). In addition, the effect of K+ concentration in the OMS-2 tunnel on the adsorption of the two gaseous molecules has also been investigated. The results of adsorption energy and structural characteristics show that the adsorption energies of CH4 and O2 molecules over the catalyst surface are favorable. Adsorption sites of CH4 are the K+ and O sites, among which the K+ site is the most stable adsorption site. In addition, Mn sites are favorable for adsorbing O2 molecules. The interactions between the catalyst and the adsorbed CH4 and O2 are enhanced with the increasing tunnel potassium ions. It should be noted that with the increasing strength of the adsorption energies, equilibrium distances from the two gaseous molecules to the active sites become shorter and the bond lengths of C-H and O-O bonds become longer. Moreover, the adsorption sites of CH4 on the catalyst surface increase with the increasing K+ concentration. Bader charge and cohesive energy calculations reveal that the tunnel K+ can balance charges and help strengthen the structural stability of OMS-2. Interestingly, the electronegativity of the catalyst has been altered after introducing K+, which leads to better adsorption of gaseous CH4 and O2. The microscopic mechanism of the effect of K+ concentration on the adsorption of CH4 and O2 over the catalyst surface paves the way for further deciphering the mechanism underlying the catalytic oxidation process and helps design more efficient catalysts for methane utilization.

Multiscale segmentation- and error-guided iterative convolutional neural network for cerebral neuron segmentation in microscopic images.

This article uses microscopy images obtained from diverse anatomical regions of macaque brain for neuron semantic segmentation. The complex structure of brain, the large intra-class staining intensity difference within neuron class, the small inter-class staining intensity difference between neuron and tissue class, and the unbalanced dataset increase the difficulty of neuron semantic segmentation. To address this problem, we propose a multiscale segmentation- and error-guided iterative convolutional neural network (MSEG-iCNN) to improve the semantic segmentation performance in major anatomical regions of the macaque brain. After evaluating microscopic images from 17 anatomical regions, the semantic segmentation performance of neurons is improved by 10.6%, 4.0%, 1.5%, and 1.2% compared with Random Forest, FCN-8s, U-Net, and UNet++, respectively. Especially for neurons with brighter staining intensity in the anatomical regions such as lateral geniculate, globus pallidus and hypothalamus, the performance is improved by 66.1%, 23.9%, 11.2%, and 6.7%, respectively. Experiments show that our proposed method can efficiently segment neurons with a wide range of staining intensities. The semantic segmentation results are of great significance and can be further used for neuron instance segmentation, morphological analysis and disease diagnosis. Cell segmentation plays a critical role in extracting cerebral information, such as cell counting, cell morphometry and distribution analysis. Accurate automated neuron segmentation is challenging due to the complex structure of brain, the large intra-class staining intensity difference within neuron class, the small inter-class staining intensity difference between neuron and tissue class, and the unbalanced dataset. The proposed multiscale segmentation- and error-guided iterative convolutional neural network (MSEG-iCNN) improve the segmentation performance in 17 major anatomical regions of the macaque brain. HIGHLIGHTS: Cell segmentation plays a critical role in extracting cerebral information, such as cell counting, cell morphometry and distribution analysis. Accurate automated neuron segmentation is challenging due to the complex structure of brain, the large intra-class staining intensity difference within neuron class, the small inter-class staining intensity difference between neuron and tissue class, and the unbalanced dataset. The proposed multiscale segmentation- and error-guided iterative convolutional neural network (MSEG-iCNN) improve the segmentation performance in 17 major anatomical regions of the macaque brain.

Evaluation of automated segmentation algorithms for neurons in macaque cerebral microscopic images.

Accurate cerebral neuron segmentation is required before neuron counting and neuron morphological analysis. Numerous algorithms for neuron segmentation have been published, but they are mainly evaluated using limited subsets from a specific anatomical region, targeting neurons of clear contrast and/or neurons with similar staining intensity. It is thus unclear how these algorithms perform on cerebral neurons in diverse anatomical regions. In this article, we introduce and reliably evaluate existing machine learning algorithms using a data set of microscopy images of macaque brain. This data set highlights various anatomical regions (e.g., cortex, caudate, thalamus, claustrum, putamen, hippocampus, subiculum, lateral geniculate, globus pallidus, etc.), poor contrast, and staining intensity differences of neurons. The evaluation was performed using 10 architectures of six classic machine learning algorithms in terms of typical Recall, Precision, F-score, aggregated Jaccard index (AJI), as well as a performance ranking of algorithms. F-score of most of the algorithms is superior to 0.7. Deep learning algorithms facilitate generally higher F-scores. U-net with suitable layer depth has been evaluated to be excellent classifiers with F-score of 0.846 and 0.837 when performing cross validation. The evaluation and analysis indicate the performance gap among algorithms in various anatomical regions and the strengths and limitations of each algorithm. The comparative result highlights at the same time the importance and difficulty of neuron segmentation and provides clues for future improvement. To the best of our knowledge, this work is the first comprehensive study for neuron segmentation in such large-scale anatomical regions. Neuron segmentation plays a critical role in extracting cerebral information, such as neuron counting and neuron morphological analysis. Accurate automated cerebral neuron segmentation is a challenging task due to different kinds, poor contrast, staining intensity differences, and fuzzy boundaries of neurons. The comprehensive evaluation and analysis of performance among existing machine learning algorithms in diverse anatomical regions allows to make clear of the strengths and limitations of state-of-the-art algorithm. The comprehensive study provides clues for future improvement and creation of automated methods.

High quality and fast compressed sensing MRI reconstruction via edge-enhanced dual discriminator generative adversarial network.

Generative adversarial networks (GAN) are widely used for fast compressed sensing magnetic resonance imaging (CSMRI) reconstruction. However, most existing methods are difficult to make an effective trade-off between abstract global high-level features and edge features. It easily causes problems, such as significant remaining aliasing artifacts and clearly over-smoothed reconstruction details. To tackle these issues, we propose a novel edge-enhanced dual discriminator generative adversarial network architecture called EDDGAN for CSMRI reconstruction with high quality. In this model, we extract effective edge features by fusing edge information from different depths. Then, leveraging the relationship between abstract global high-level features and edge features, a three-player game is introduced to control the hallucination of details and stabilize the training process. The resulting EDDGAN can offer more focus on edge restoration and de-aliasing. Extensive experimental results demonstrate that our method consistently outperforms state-of-the-art methods and obtains reconstructed images with rich edge details. In addition, our method also shows remarkable generalization, and its time consumption for each 256 × 256 image reconstruction is approximately 8.39 ms.

Click RNA for Rapid Capture and Identification of Intracellular MicroRNA Targets.

Rapid capture and identification of the intracellular target genes of microRNAs (miRNAs) are the key to understanding miRNA functions and development of RNA-based therapeutics. However, developing biochemical tools that can fish out the target genes of miRNAs in live cells is a significant technical challenge. Here, we report a remarkably simple yet powerful technology capable of loading virtually any miRNA into Ago2 of the RNA-induced silencing complexes (RISCs). This surprising discovery enables rapid capture and identification of target mRNAs and long noncoding RNAs. It is achieved by linking dibenzocyclooctyne (DBCO), a classical chemical moiety in copper-free click chemistry, to the 3' end of miRNAs. DBCO serves as a high-affinity tag to the Ago2 protein, thus boosting the formation of RISCs with miRNA target genes in living cells. Upon cell lysing, DBCO's routine function in click chemistry allows rapid enrichment of target genes for analysis without the need of additional molecular handles. A series of miR-21 and miR-27a target genes that were previously unknown were pulled down from various cell lines and identified with qRT-PCR, demonstrating the utility of this innovative technology in both transcriptomic research and RNA-based studies.

Reversible Self-Assembly of Nanoprobes in Live Cells for Dynamic Intracellular pH Imaging.

Self-assembly is a powerful tool to organize the elementary molecular units into functional nanostructures, which provide reversible stimulus-responsive systems for a variety of purposes. However, the ability to modulate the reversible self-assembly in live systems remains a great challenge owing to the chemical complexity of intracellular environments, which often damage synthetic assembled superstructures. Herein, we describe a robust reversible self-assembly system that is composed of a hydrophobic gold nanoparticle (AuNP) core and a shell of pH-responsive dye-incorporated block copolymers. The reversible assembly-disassembly processes were precisely controlled through mediating the molecular interactions between the copolymers and AuNPs. More importantly, the major endogenous biospecies such as proteins will not impair the reversible self-assembly, which was supported by free-energy calculations. The reversible pH-responsive nanostructures were employed as "smart" probes for visualizing the subtle dynamic pH changes among different intracellular compartments, facilitating the study of pH influence on biological processes.

Addressing Polarization Phenomena in Molecular Machines Containing Transition Metal Ions with an Additive Force Field.

Modeling transition metals in supramolecular assemblies, in general, is extremely challenging due to polarization and charge transfer. In this work, we demonstrate that the inherent shortcomings of additive force fields in modeling Cu+-ether-O and Cu+-olefin-C interactions are rooted in the Lorentz-Berthelot rules. A general method for investigating transition-metal-containing molecular assays using classical force fields is, therefore, proposed. In this strategy, QM/MM calculations have been performed to determine the potential of mean force (PMF) describing the interaction of a cation and a specific functional group. van der Waals parameters for the corresponding pairs of particles have then been optimized using the NBFIX feature of the CHARMM force field to fit the QM/MM PMF. This method has been applied to decipher the mechanism underlying the "dialing" of a molecular machine controlled by Li+ and Cu+ cations, indicating that the process is controlled by the competition between cation-ether-O and cation-olefin-C interactions.