Numerical simulations on fluid dynamics problems primarily rely on spatially or/and temporally discretization of the governing equation using polynomials into a finite-dimensional algebraic system. Due to the multi-scale nature of the physics and sensitivity from meshing a complicated geometry, such process can be computational prohibitive for most realtime applications (e.g., clinical diagnosis and surgery planning) and many-query analyses (e.g., optimization design and uncertainty quantification). Therefore, developing a costeffective surrogate model is of great practical significance. Deep learning (DL) has shown new promises for surrogate modeling due to its capability of handling strong nonlinearity and high dimensionality. However, the off-the-shelf DL architectures, success of which heav-* Corresponding author. of internal flows relevant to hemodynamics applications, and the forward propagation of uncertainties in fluid properties and domain geometry is studied as well. The results show excellent agreement on the flow field and forward-propagated uncertainties between the DL surrogate approximations and the first-principle numerical simulations.
Background
Rotator cuff tears (RCTs) often require reconstructive surgery. Tendon-bone healing is critical for the outcome of rotator cuff reconstruction, but the process of tendon-bone healing is complex and difficult. Mesenchymal stem cells (MSCs) are considered to be an effective method to promote tendon-bone healing. MSCs have strong paracrine, anti-inflammatory, immunoregulatory, and angiogenic potential. Recent studies have shown that MSCs achieve many regulatory functions through exosomes. The purpose of this study was to explore the role of bone marrow mesenchymal stem cell-derived exosomes (BMSC-Exos) in tendon-bone healing.
Methods
Our study found that BMSC-Exos promote the proliferation, migration, and angiogenic tube formation of human umbilical vein endothelial cells (HUVECs). The mechanism by which BMSC-Exos achieve this may be through the regulation of the angiogenic signaling pathway. In addition, BMSC-Exos can inhibit the polarization of M1 macrophages and inhibit the secretion of proinflammatory factors by M1 macrophages. After rotator cuff reconstruction in rats, BMSC-Exos were injected into the tail vein to analyze their effect on the rotator cuff tendon-bone interface healing.
Results
It was confirmed that BMSC-Exos increased the breaking load and stiffness of the rotator cuff after reconstruction in rats, induced angiogenesis around the rotator cuff endpoint, and promoted growth of the tendon-bone interface.
Conclusion
BMSC-Exos promote tendon-bone healing after rotator cuff reconstruction in rats by promoting angiogenesis and inhibiting inflammation.
Invariant spatial context can facilitate visual search. For instance, detection of a target is faster if it is presented within a repeatedly encountered, as compared to a novel, layout of nontargets, demonstrating a role of contextual learning for attentional guidance ('contextual cueing'). Here, we investigated how context-based learning adapts to target location (and identity) changes. Three experiments were performed in which, in an initial learning phase, observers learned to associate a given context with a given target location. A subsequent test phase then introduced identity and/or location changes to the target. The results showed that contextual cueing could not compensate for target changes that were not 'predictable' (i.e. learnable). However, for predictable changes, contextual cueing remained effective even immediately after the change. These findings demonstrate that contextual cueing is adaptive to predictable target location changes. Under these conditions, learned contextual associations can be effectively 'remapped' to accommodate new task requirements.
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