Stress waves, known as acoustic emissions (AEs), are released by localized inelastic deformation events during the progressive failure of brittle rocks. Although several numerical models have been developed to simulate the deformation and damage processes of rocks, such as non-linear stress-strain behaviour and localization of failure, only a limited number have been capable of providing quantitative information regarding the associated seismicity. Moreover, the majority of these studies have adopted a pseudo-static approach based on elastic strain energy dissipation that completely disregards elastodynamic effects. This paper describes a new AE modelling technique based on the combined finite-discrete element method (FEM/DEM), a numerical tool that simulates material failure by explicitly considering fracture nucleation and propagation in the modelling domain. Given the explicit time integration scheme of the solver, stress wave propagation and the effect of radiated seismic energy can be directly captured. Quasi-dynamic seismic information is extracted from a FEM/DEM model with a newly developed algorithm based on the monitoring of internal variables (e.g. relative displacements and kinetic energy) in proximity to propagating cracks. The AE of a wing crack propagation model based on this algorithm are cross-analysed by traveltime inversion and energy estimation from seismic recordings. Results indicate a good correlation of AE initiation times and locations, and scaling of energies, independently calculated with the two methods. Finally, the modelling technique is validated by simulating a laboratory compression test on a granite sample. The micromechanical parameters of the heterogeneous model are first calibrated to reproduce the macroscopic stress-strain response measured during standard laboratory tests. Subsequently, AE frequency-magnitude statistics, spatial clustering of source locations and the evolution of AE rate are investigated. The distribution of event magnitude tends to decay as power law while the spatial distribution of sources exhibits a fractal character, in agreement with experimental observations. Moreover, the model can capture the decrease of seismic b value associated with the macrorupture of the rock sample and the transition of AE spatial distribution from diffuse, in the pre-peak stage, to strongly localized at the peak and post-peak stages, as reported in a number of published laboratory studies. In future studies, the validated FEM/DEM-AE modelling technique will be used to obtain further insights into the micromechanics of rock failure with potential applications ranging from laboratory-scale microcracking to engineering-scale processes (e.g. excavations within mines, tunnels and caverns, petroleum and geothermal reservoirs) to tectonic earthquakes triggering.

PurposeNon-small cell lung cancer (NSCLC) is the first leading cause of cancer-related death globally. Long noncoding RNA KCNQ1 overlapping transcript 1 (KCNQ1OT1) was involved in the progression of multiple cancers by sponging target miRNA. We aimed to explore the pathological mechanism of KCNQ1OT1 in NSCLC progression.MethodsThe expression of KCNQ1OT1, miR-204-5p and autophagy-related gene 3 (ATG3) was measured by quantitative real-time polymerase chain reaction (qRT-PCR). 3-(4, 5-Dimethyl-2-thiazolyl)-2, 5-diphenyl-2-H-tetrazolium bromide (MTT) assay and flow cytometry assay were conducted for the detection of cell proliferation and apoptosis, respectively. Western blot assay was performed to examine the protein levels of B-cell lymphoma-2 (BCL-2), BCL2-Associated X (Bax), cleaved caspase-3, cleaved caspase-9 and LC3Ⅱ/LC3Ⅰ and P62. The interaction between miR-204-5p and KCNQ1OT1 or ATG3 was validated by dual-luciferase reporter system and RNA immunoprecipitation (RIP) assay. Murine xenograft assay was conducted to explore the function of KCNQ1OT1 in vivo. Immunohistochemistry (IHC) staining assay was used for the analysis of ki67-positive cell percentage.ResultsThe expression of KCNQ1OT1 and ATG3 was up-regulated whereas miR-204-5p was down-regulated in NSCLC tumors and cells. MiR-204-5p was inversely correlated with KCNQ1OT1 or ATG3. In addition, KCNQ1OT1 knockdown facilitated apoptosis, inhibited autophagy and proliferation of NSCLC cells in vitro and blocked tumor growth in vivo. However, the miR-204-5p inhibitor reversed the effects. More importantly, ATG3 was a target gene of miR-204-5p and ATG3 overexpression restored the effect of miR-204-5p on NSCLC cell progression.ConclusionKCNQ1OT1 promotes cell proliferation and autophagy and inhibits cell apoptosis via regulating miR-204-5p/ATG3 axis, providing a promising target for NSCLC therapy.

Neuritin is an important neurotrophin that regulates neural development, synaptic plasticity, and neuronal survival. Elucidating the downstream molecular signaling is important for potential therapeutic applications of neuritin in neuronal dysfunctions. We previously showed that neuritin up-regulates transient potassium outward current (IA) subunit Kv4.2 expression and increases IA densities, in part by activating the insulin receptor signaling pathway. Molecular mechanisms of neuritin-induced Kv4.2 expression remain elusive. Here, we report that the Ca2+/calcineurin (CaN)/nuclear factor of activated T-cells (NFAT) c4 axis is required for neuritin-induced Kv4.2 transcriptional expression and potentiation of IA densities in cerebellum granule neurons. We found that neuritin elevates intracellular Ca2+ and increases Kv4.2 expression and IA densities; this effect was sensitive to CaN inhibition and was eliminated in Nfatc4−/− mice but not in Nfatc2−/− mice. Stimulation with neuritin significantly increased nuclear accumulation of NFATc4 in cerebellum granule cells and HeLa cells, which expressed IR. Furthermore, NFATc4 was recruited to the Kv4.2 gene promoter loci detected by luciferase reporter and chromatin immunoprecipitation assays. More importantly, data obtained from cortical neurons following adeno-associated virus-mediated overexpression of neuritin indicated that reduced neuronal excitability and increased formation of dendritic spines were abrogated in the Nfatc4−/− mice. Together, these data demonstrate an indispensable role for the CaN/NFATc4 signaling pathway in neuritin-regulated neuronal functions.

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We propose Switching Poisson gamma dynamical systems (SPGDS) to model sequentially observed multivariate count data. Different from previous models, SPGDS assigns its latent variables into mixture of gamma distributed parameters to model complex sequences and describe the nonlinear dynamics, meanwhile, capture various temporal dependencies. For efficient inference, we develop a scalable hybrid stochastic gradient-MCMC and switching recurrent autoencoding variational inference, which is scalable to large scale sequences and fast in out-of-sample prediction. Experiments on both unsupervised and supervised tasks demonstrate that the proposed model not only has excellent fitting and prediction performance on complex dynamic sequences, but also separates different dynamical patterns within them.

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