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Atherosclerosis is the most common arterial disease and is closely related to vascular calcification. CircHIPK3 has been implicated in atherosclerosis development, but the possible downstream regulatory mechanisms remain unclear. The levels of circHIPK3, miR-106a and MFN2 in tissues and blood samples of patients with atherosclerosis were detected by RT-qPCR. The levels of circHIPK3, miR-106a and MFN2 were detected by RT-qPCR and the expression levels of MFN2, osteogenic and cartilage differentiation marker proteins were detected by western blot in vitro. ALP staining, Alizarin Red staining, and calcium content detection evaluated the degree of osteogenic differentiation of cells. Alcian blue staining detected the level of cell cartilage differentiation. Luciferase detected the targeting relationship between circHIPK3 and miR-106a-5p, as well as miR-106a-5p and MFN2. CircHIPK3 and MFN2 were low expressed and miR-106a-5p was highly expressed in tissues and blood samples of patients with atherosclerosis, as well as vascular smooth muscle cell (VSMC) with osteogenic and cartilage differentiation. Overexpression of circHIPK3 reduced the cell mineralization and calcium content. Overexpression of circHIPK3 inhibited osteogenic differentiation by decreasing ALP activity, RUNX2, and OPG expression, and increasing SM22α and SMA level. What's more, overexpression of circHIPK3 decreased the chondrogenic differentiation by inhibiting the protein level of SOX9, aggrecan, and collagen II. CircHIPK3 targeted miR-106a-5p and miR-106a-5p targeted MFN2. MiR-106a-5p overexpression or MFN2 depletion repressed the effect of circHIPK3 overexpression on VSMC calcification. CircHIPK3 regulated osteogenic and cartilage differentiation of VSMC via miR-106a-5p/MFN2 axis, indicating a target for treating vascular calcification.

Hard and brittle materials usually exhibit a much lower strength when loaded in tension than in compression. However, this common-sense behaviour may not be intrinsic to these materials, but arises from their higher flaw sensitivity to tensile loading. Here, we demonstrate a reversed and unusually pronounced tension–compression asymmetry (tensile strength exceeds compressive strength by a large margin) in submicrometre-sized samples of isotropic amorphous silicon. The abnormal asymmetry in the yield strength and anelasticity originates from the reduction in shear modulus and the densification of the shear-activated configuration under compression, altering the magnitude of the activation energy barrier for elementary shear events in amorphous Si. In situ coupled electrical tests corroborate that compressive strains indeed cause increased atomic coordination (metallization) by transforming some local structures from sp3-bonded semiconducting motifs to more metallic-like sites, lending credence to the mechanism we propose. This finding opens up an unexplored regime of intrinsic tension–compression asymmetry in materials. Submicrometre-sized amorphous silicon samples show an unusually large tensile strength relative to the compressive strength, which is due to the reduced shear modulus and the activation energy barrier for shear transformations under compression.

Although there is a large literature demonstrating rapid and accurate enumeration of small sets of simultaneously presented items (i.e., subitizing), it is unclear whether this small numerosity advantage (SNA) can also manifest in sequential enumeration. The present study thus has two aims: to establish a robust processing advantage for small numerosities during sequential enumeration using a rapid serial visual presentation (RSVP) paradigm, and to examine the underlying mechanism for a SNA in sequential enumeration. The results indicate that a small set of items presented in fast sequences can be enumerated accurately with a high precision and a SOA (stimulus onset asynchrony)-sensitive capacity limit, essentially generalizing the large literature on small numerosity advantage from spatial domain to temporal domain. A resource competition hypothesis was proposed and confirmed in further experiments. Specifically, sequential enumeration and other cognitive process, such as visual working memory (VWM), compete for a shared resource of object individuation by which items are segregated as individual entities. These results implied that the limited resource of object individuation can be allocated within time windows of flexible temporal scales during simultaneous and sequential enumerations. Taken together, the present study calls for attention to the dynamic aspect of the enumeration process and highlights the pivotal role of object individuation in underlying a wide range of mental operations, such as enumeration and VWM.

It has been a long-standing materials science challenge to establish structure-property relations in amorphous solids. Here we introduce a rotationally non-invariant local structure representation that enables different predictions for different loading orientations, which is found essential for high-fidelity prediction of the propensity for stress-driven shear transformations. This novel structure representation, when combined with convolutional neural network (CNN), a powerful deep learning algorithm, leads to unprecedented accuracy for identifying atoms with high propensity for shear transformations (i.e., plastic susceptibility), solely from the static structure in both two- and three-dimensional model glasses. The data-driven models trained on samples at one composition and a given processing history are found transferrable to glass samples with different processing histories or at different compositions in the same alloy system. Our analysis of the new structure representation also provides valuable insight into key atomic packing features that influence the local mechanical response and its anisotropy in glasses., Predicting a priori local defects in amorphous materials remains a grand challenge. Here authors combine a rotationally non-invariant structure representation with deep-learning to predict the propensity for shear transformations of amorphous solids for different loading orientations, only given the static structure.

On the basis of strike-slip fault scale in the Qaidam Basin, the authors hold that the strike-slip fault can be graded into basin-scale, zone-scale, trap-scale and microscope-scale, the complicated strike-slip fault and relevant tectonic combination are related to tectonic movement and evolution, the authors make an analysis of the evolution age in this area, and pointed out that the strike-slip fault is extensional fault depression basin in Early Himalayan movement, reversal fault in Middle Himalayan movement and compressional strike-slip fault in Late Himalayan movement. Corresponding to the tectonic movements, three kinds of strike-slip fault (extensional faulted depression, depression and compressional strike-slip) were formed with different mechanical characteristics. Finally, the paper discusses how strike-slip fault impact trap formation, oil and gas transmission, and reservoir, and how such impacts are related to hydrocarbon accumulation, research on strike-slip fault play an important role in accumulation forming process, controlling the hydrocarbon accumulation, oil and gas distribution patterns.

In this work, we propose to extend the bi-directional evolutionary structural optimization (BESO) method for compliance minimization design subject to both constraints on volume fraction and maximum von Mises stress. To this end, the aggregated p-norm global stress measure is first adopted to approximate the maximum stress. The conventional compliance design objective is augmented with p-norm stress measures by introducing one or multiple Lagrange multipliers. The Lagrange multipliers are employed to yield compromised designs of the compliance and the p-norm stress. An empirical scheme is developed for the determination of the Lagrange multipliers such that the maximum von Mises stress could be effectively constrained through the controlling of the aggregated p-norm stress. To further enforce the satisfaction of stress constraints, the stress norm parameter p is assigned to a higher value after attaining the objective volume. The update of the binary design variables lies in the computationally efficient sensitivity numbers derived using the adjoint method. A series of comparison studies has been conducted to validate the effectiveness of the method on several benchmark design problems.