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The 3D convolutional neural network (CNN) is able to make full use of the spatial 3D context information of lung nodules, and the multi-view strategy has been shown to be useful for improving the performance of 2D CNN in classifying lung nodules. In this paper, we explore the classification of lung nodules using the 3D multi-view convolutional neural networks (MV-CNN) with both chain architecture and directed acyclic graph architecture, including 3D Inception and 3D Inception-ResNet. All networks employ the multi-view-one-network strategy. We conduct a binary classification (benign and malignant) and a ternary classification (benign, primary malignant and metastatic malignant) on Computed Tomography (CT) images from Lung Image Database Consortium and Image Database Resource Initiative database (LIDC-IDRI). All results are obtained via 10-fold cross validation. As regards the MV-CNN with chain architecture, results show that the performance of 3D MV-CNN surpasses that of 2D MV-CNN by a significant margin. Finally, a 3D Inception network achieved an error rate of 4.59% for the binary classification and 7.70% for the ternary classification, both of which represent superior results for the corresponding task. We compare the multi-view-one-network strategy with the one-view-one-network strategy. The results reveal that the multi-view-one-network strategy can achieve a lower error rate than the one-view-one-network strategy.

Air leakage from surface mining-induced fissures can easily cause spontaneous combustion of residual coal in the goaf, which threatens the safe production of the underground working face. In order to study the air leakage law of the goaf under the surface air leakage and the prevention and control technology of spontaneous combustion of residual coal. Based on engineering data from the 6104 working face of the Chuancao Gedan coal mine, this study uses a combination of theoretical analyses, numerical simulations, and field observations to study the dynamic distribution characteristics of the air leakage velocity of surface mining-induced fissures in shallow coal seams, the distribution characteristics of relative pressure, the air leakage velocity, the air leakage flow field, the distribution ranges for the “three zones” of spontaneous combustion in the goaf, and a reasonable range for the pressurized ventilation of the working face. The results show that there is a quadratic relationship between the air leakage speed from the surface mining-induced fissures in shallow coal seams and the distance from the working face. The air leakage speed decreases as the distance from the working face increases, and the air leakage speed in the middle of the working face is slower than the air leakage on either side of the goaf. The pressure difference between the goaf and the surface mining-induced fissures is the root cause of air leakage into the goaf, and a change in the pressure difference has a significant impact on the air leakage flow field and the distributions of the \"three zones\" of spontaneous combustion in the goaf. When the pressure difference between the ground surface and the working face is maintained within the range of 200~-200 Pa, air leakage is effectively reduced, and the spontaneous combustion of residual coal is inhibited. The research results reveal the air leakage mechanism in the goaf of shallow coal seams and provide a reference for the prevention and control of spontaneous combustion of residual coal in the goaf.

Background Traditional Chinese Medicine (TCM) is characterized by the wide use of herbal formulae, which are capable of systematically treating diseases determined by interactions among various herbs. However, the combination rule of TCM herbal formulae remains a mystery due to the lack of appropriate methods. Methods From a network perspective, we established a method called Distance-based Mutual Information Model (DMIM) to identify useful relationships among herbs in numerous herbal formulae. DMIM combines mutual information entropy and “between-herb-distance” to score herb interactions and construct herb network. To evaluate the efficacy of the DMIM-extracted herb network, we conducted in vitro assays to measure the activities of strongly connected herbs and herb pairs. Moreover, using the networked Liu-wei-di-huang (LWDH) formula as an example, we proposed a novel concept of “co-module” across herb-biomolecule-disease multilayer networks to explore the potential combination mechanism of herbal formulae. Results DMIM, when used for retrieving herb pairs, achieves a good balance among the herb’s frequency, independence, and distance in herbal formulae. A herb network constructed by DMIM from 3865 Collaterals-related herbal formulae can not only nicely recover traditionally-defined herb pairs and formulae, but also generate novel anti-angiogenic herb ingredients (e.g. Vitexicarpin with IC50=3.2 μM, and Timosaponin A-III with IC50=3.4 μM) as well as herb pairs with synergistic or antagonistic effects. Based on gene and phenotype information associated with both LWDH herbs and LWDH-treated diseases, we found that LWDH-treated diseases show high phenotype similarity and identified certain “co-modules” enriched in cancer pathways and neuro-endocrine-immune pathways, which may be responsible for the action of treating different diseases by the same LWDH formula. Conclusions DMIM is a powerful method to identify the combination rule of herbal formulae and lead to new discoveries. We also provide the first evidence that the co-module across multilayer networks may underlie the combination mechanism of herbal formulae and demonstrate the potential of network biology approaches in the studies of TCM.

Background GATA transcription factors are type IV zinc-finger proteins that play key roles in plant growth and responses to environmental stimuli. Although these proteins have been studied in model plants, the related studies of GATA gene family under abiotic stresses are rarely reported in grapevine ( Vitis vinifera L.). Results In the current study, a total of 23 VviGATA genes were identified in grapevine and classified into four groups (I, II, III, and IV), based on phylogenetic analysis. The proteins in the same group exhibited similar exon–intron structures and conserved motifs and were found to be unevenly distributed among the thirteen grapevine chromosomes. Accordingly, it is likely that segmental and tandem duplication events contributed to the expansion of the VviGATA gene family. Analysis of cis- acting regulatory elements in their promoters suggested that VviGATA genes respond to light and are influenced by multiple hormones and stresses. Organ/tissue expression profiles showed tissue specificity for most of the VviGATA genes, and five were preferentially upregulated in different fruit developmental stages, while others were strongly induced by drought, salt and cold stress treatments. Heterologously expressed VamGATA5a, VamGATA8b, VamGATA24a, VamGATA24c and VamGATA24d from cold-resistant V. amurensis ‘Shuangyou’ showed nuclear localization and transcriptional activity was shown for VamGATA5a, VamGATA8b and VamGATA24d. Conclusions The results of this study provide useful information for GATA gene function analysis and aid in the understanding of stress responses in grapevine for future molecular breeding initiatives.

Off-grid renewable power-to-hydrogen (ReP2H) systems face stability and economic constraints driven by the variability of renewable resources. This paper presents a comparative analysis of grid-forming (GFM) service requirements under three approaches, i.e., centralized GFM battery energy storage system (BESS), GFM electrolyzers and coordinated multi-source GFM strategies. We first establish detailed GFM models for off-grid ReP2H systems under each approach and then conduct hardware-in-the-loop (HIL) real-time simulations. By evaluating both dynamic performance and cost, we identify the strengths and limitations of the three strategies and quantify the GFM capacity needed to ensure stable off-grid hydrogen production.

Recent research on the use of heptazine-based polymeric carbon nitride materials as potential photocatalysts for hydrogen evolution has made significant progress. However, the impact of the water cluster’s size on the time-dependent photochemical mechanisms during the water splitting process of heptazine–water clusters remains largely unexplored. Here, we present a Landau–Zener trajectory surface hopping dynamics calculation for heptazine–(H2O)4 clusters at the ADC(2) level. The electron-driven proton transfer (EDPT) mechanism reaction from water to hydrogen-bonded heptazine–water clusters was confirmed using this method, yielding a heptazinyl radical and an OH biradical as products. The calculated quantum yield of the EDPT for the heptazine–(H2O)4 complex was 6.5%, which was slightly lower than that of the heptazine–H2O complex (9%), suggesting that increasing the water cluster size does not significantly enhance the efficiency of hydrogen transfer. Interestingly, our results show that the de-excitation of the heptazine–water complex from the excited state to the ground state via the EDPT process follows both fast and slow decay modes, which govern population relaxation and facilitate the photochemical water splitting reaction. This newly identified differential decay behavior offers valuable insights that could help deepen our understanding of the EDPT process, potentially improving the efficiency of water splitting under sunlight.

Using image recognition technology to realize coal gangue recognition is one of the development directions of intelligent fully mechanized caving mining. Aiming at the problem of low accuracy of coal gangue recognition in fully mechanized caving mining, the extraction method of Coal and gangue images features is proposed, and the corresponding coal gangue recognition model is constructed. The illuminance value is an important factor affecting the imaging quality. Therefore, a multi-light source image acquisition system is designed, and the optimal illuminance value suitable for coal and gangue images acquisition is determined to be 17,130 Lux. There is a large amount of image noise in the gray-sc5ale image, so Gaussian filtering is used to eliminate the noise in the gray-scale image of coal and gangue. Then, six gray-scale features and four texture features are extracted from 900 coal and gangue images respectively. It is concluded that the three kinds of features of gray skewness, gray variance and texture contrast have the highest discrimination on coal and gangue images. Least squares vector machine has a strong ability to classify, so the use of least squares vector machine to achieve coal gangue identification, and build coal gangue identification model. The results show that the recognition accuracy of the model for coal gangue images is 92.2% and 91.5%, respectively, with gray skewness and texture contrast as indicators. This study provides a reliable theoretical support for solving the problem of low recognition rate of coal gangue in fully mechanized caving mining.

The problems of water scarcity and ecological fragility are common in the loess gully area. To research the distribution and evolution of the overburden fissures and quantitatively analyze them have certain theoretical and engineering significance for realizing the evaluation of overburden damage degree and safe and green mining. This paper takes the 6102 working face of Chuancao Gedan Coal Mine as the engineering background. The development law and distribution characteristics of overburden fissures caused by the mining of shallow coal seams in the loess gully area were studied by the combination of physical similarity simulation, numerical similarity simulation and fractal theory. The results show that the fractal dimension change of the overburden fissures caused by the shallow mining of coal seam groups in the loess gully area can be divided into three stages during the mining process of the working face. Repeated mining causes the activation and development of overburden fissures, the fractal dimension increases significantly, and the regularity of changes weakens. The magnitude of the stress near the working face and the fluctuation times of the stress in the goaf have an influence on the change of the fractal dimension of the overburden fissures. According to the development angle and the fractal dimension of the overburden fissures, the overburden rock above the goaf is divided into the collapse fissure area, the compaction fissure area, and the vertical fissure area. Overburden fissures develop violently in the vertical fissure area, the overburden fissures in the compaction fissure area are mostly transverse fissures, and the overburden fissures in the caving fissure area are irregular.

The current study investigated the therapeutic potential of Ph-triazole in silico and in vitro against pancreatic cancer. Common targets between Ph-triazole and pancreatic cancer, notably P53 protein, were identified using the venny 2.1.0 tool and imported into the String database to construct the protein-protein network. Box plot revealed that the most prominent hub gene TP53 is strongly up-regulated in pancreatic tumor tissues (N = 179) compared to normal tissue samples (N = 171), and stage plots confirmed that its upregulation is found during all four stages of the disease. Survival analysis of the pancreatic cancer patients revealed a strong correlation between TP53 gene overexpression and low overall survival and disease-free survival. Molecular docking showed that Ph-triazole exhibits a strong binding affinity for P53 protein. In vitro data also confirmed the anti-proliferative effect of Ph-triazole in pancreatic cancer cell models. Therefore, Ph-triazole can act as an anti-proliferative agent for pancreatic cancer and needs to be investigated further by in vivo studies.

To address the technical limitations of automatic coal and gangue detection technology in fully mechanized top coal caving mining operations, the low radiation level radioactivity measurement method is utilized to assess the degree of coal-gangue mixture in top coal caving process. This approach is based on the distinguishing radiation characteristics of natural γ-rays between coal and gangue. This study analyzed the distribution characteristics of natural γ-rays in coal and rock layers of thick coal seams and the applicability of this method, introduced the basic principle of coal-gangue detection technology based on natural γ-ray, developed the test system about automatic coal-gangue detection, studied the radiation characteristics of coal and gangue, proposed determination model of the coal-gangue mixed degree, combined with the time sequence characteristics of the top coal’s releasing flow and the energy spectrum characteristics of different layers of rock, realized the precise coal-gangue detection technology in complex structure thick coal seam with multiple gangue. Field tests were conducted in Lilou, Xiaoyu and Tashan Coal Mine. The test results were well corroborated with the research results and achieved the expected results, which laid the foundation for the field application of intelligent coal mining.