Cellular rheological properties affect cell function and are reflective of cell status. It is challenging to perform multiplexed single‐cell rheology probing with high controllability, particularly ...for adherent cells. A surface acoustic wave (SAW)‐based method is presented for this purpose. The method integrates the potent micromanipulation ability of acoustic waves in a microfluidic chamber with the ability of cell‐anchored microbeads to concentrate the acoustic energy to deform the cell. Two strategies are developed for placing a targeted microbead at a desired position on the cell membrane. The power‐law rheological dynamics with plastic components are applied to fit the creep (during the mechanical loading) and relaxation (after force removal) responses of the cell. With more than 400 measurements of adherent cells and each with detailed dynamics, a full range of viscoelastic behaviors of cells far beyond the typical rheology of previously reported adherent cells and unexpected negative plastic compliance is observed. The developed method supports in‐depth investigations of biomechanics at the cellular and subcellular levels, with considerable potential for extension to mechanical force‐based cell function regulation.
Multiplexed single‐cell rheology probing is achieved using surface acoustic waves (SAWs). Targeted microbeads are first located to adhered cells forming binding with cell surface proteins either on top or on the side, and then pushed by acoustic waves to deform cells for cell rheology measurement. The method allows for high‐throughput semi‐quantitative analysis of cell rheology.
The inherently dynamic and anisotropic microenvironment of cells imposes not only global and slow physical stimulations on cells but also acute and local perturbations. However, cell mechanical ...responses to transient subcellular physical signals remain unclear. In this study, acoustically activated targeted microbubbles were used to exert mechanical perturbations to single cells. The cellular contractile force was sensed by elastic micropillar arrays, while the pillar deformations were imaged using brightfield high-speed video microscopy at a frame rate of 1k frames per second for the first 10s and then confocal fluorescence microscopy. Cell mechanical responses are accompanied by cell membrane integrity changes. Both processes are determined by the perturbation strength generated by microbubble volumetric oscillations. The instantaneous cellular traction force relaxation exhibits two distinct patterns, correlated with two cell fates (survival or permanent damage). The mathematical modeling unveils that force-induced actomyosin disassembly leads to gradual traction force relaxation in the first few seconds. The perturbation may also influence the far end subcellular regions from the microbubbles and may propagate into connected cells with attenuations and delays. This study carefully characterizes the cell mechanical responses to local perturbations induced by ultrasound and microbubbles, advancing our understanding of the fundamentals of cell mechano-sensing, -responsiveness, and -transduction.
Subcellular physical perturbations commonly exist but haven't been fully explored yet. The subcellular perturbation generated by ultrasound and targeted microbubbles covers a wide range of strength, from mild, intermediate to intense, providing a broad biomedical relevance. With µm2 spatial sensing ability and up to 1ms temporal resolution, we present spatiotemporal details of the instantaneous cellular contractile force changes followed by attenuated and delayed global responses. The correlation between the cell mechanical responses and cell fates highlights the important role of the instantaneous mechanical responses in the entire cellular reactive processes. Supported by mathematical modeling, our work provides new insights into the dynamics and mechanisms of cell mechanics.
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Geomagnetic depth sounding (GDS) is a geophysical electromagnetic (EM) method that studies the deep structure and composition of the Earth by using long-period EM signals from geomagnetic ...observatories and satellites. In this paper, a 3-D anisotropic GDS modelling algorithm is developed. The curl–curl equation is discretized using the edge-based finite-element method on unstructured tetrahedral grids. In order to solve the computationally demanding problem of EM modelling on a global scale, the complex linear system is first separated into the equivalent real linear systems and then the real system is iteratively solved by the flexible generalized minimum residual method with a block diagonal pre-conditioner. This will greatly reduce the condition number of the linear system and thus speed up the solution process. We verify the accuracy of the proposed algorithm by comparing our results with the existing methods. After that, we design a subduction zone model to simulate the EM responses under isotropic and anisotropic environments, respectively. The numerical results show the high efficiency of the proposed algorithm and the response differences between isotropic and anisotropic models. This research can provide theoretical and technical support for the high-accuracy and efficient inversion of GDS data for the geo-dynamic study.
The web crossed truss boom is one of the commonly used truss boom structures of crawler cranes. However, the existing calculations fail to consider the limiting effect of the web members' bending ...resistance on the chord members, and cannot give full play to the load-bearing capacity of the existing structure. This paper takes the top section of the Crawler crane truss boom as the research object. The single-span truss theoretical model is established according to Timoshenko's elastic stability theory. And the theoretical critical load of the variable cross-section boom is obtained with full consideration of the limitation of the web member's bending resistance on the chord members. The finite element method simulation model is compared and verified. Compared with a large number of simulation experiments and theoretical calculations, it can be concluded that the theoretical calculations in this article are highly consistent with the simulation results, verified the assumptions that the web members' bending resistance help to improve the bending resistance of the chord members, and this will provide certain reference to the engineering designers.
This article investigates the finite-horizon optimal control (FHOC) problem of Boolean control networks (BCNs) from a graph theory perspective. We first formulate two general problems to unify ...various special cases studied in the literature: 1) the horizon length is a priori fixed and 2) the horizon length is unspecified but finite for given destination states. Notably, both problems can incorporate time-variant costs, which are rarely considered in existing work, and a variety of constraints. The existence of an optimal control sequence is analyzed under mild assumptions. Motivated by BCNs' finite state space and control space, we approach the two general problems intuitively and efficiently under a graph-theoretical framework. A weighted state transition graph and its time-expanded variants are developed, and the equivalence between the FHOC problem and the shortest-path (SP) problem in specific graphs is established rigorously. Two algorithms are developed to find the SP and construct the optimal control sequence for the two problems with reduced computational complexity, though technically, a classical SP algorithm in graph theory is sufficient for all problems. Compared with existing algebraic methods, our graph-theoretical approach can achieve state-of-the-art time efficiency while targeting the most general problems. Furthermore, our approach is the first one capable of solving <xref ref-type="other" rid="other4">Problem 2 ) with time-variant costs. Finally, a genetic network in the bacterium E. coli and a signaling network involved in human leukemia are used to validate the effectiveness of our approach. The results of two common tasks for both networks show that our approach can dramatically reduce the running time. Python implementation of our algorithms is available at GitHub https://github.com/ShuhuaGao/FHOC .
Previous studies have shown that benzoylaconine (BAC), a representative monoester alkaloid, has a potential anti-inflammatory effect. This study investigated the underlying molecular mechanisms using ...the mode of LPS-activated RAW264.7 macrophage cells. Our findings showed that BAC significantly suppressed the release of pro-inflammatory cytokines and mediators, including IL-6, TNF-α, IL-1β, ROS, NO, and PGE
2
. BAC treatment also effectively downregulated the elevated protein levels of iNOS and COX-2 induced by LPS in a dose-dependent manner. In this study, we found that BAC inhibited LPS-induced NF-κB activation by reducing the phosphorylation and degradation of IκBα by western blotting and blocking the nuclear translocation of p65 using an immunofluorescence assay. The elevated protein levels of JNK, p38, and ERK phosphorylation after LPS stimulation were restored effectively by BAC treatment. The protein expression of Toll-like receptor 4 (TLR4) and LPS-induced phosphorylation of TAK1, which is a crucial upstream regulatory factor of TLR-induced MAPK and NF-κB signaling, were inhibited by BAC in activated RAW264.7 macrophages. Moreover, BAC decreased the levels of TAK1 phosphorylation and pro-inflammatory cytokines and mediators associated with MAPK and NF-κB activation, similar to TLR4 inhibitor TAK-242. These findings demonstrated that BAC exhibited an anti-inflammatory effect by the inhibition of TLR-induced MAPK and NF-κB pathways, indicating that it could potentially be used for treating inflammatory diseases.
The first facile and efficient copper-catalyzed direct C–P cross-coupling of unprotected propargylic alcohols with P(O)H compounds has been developed, providing a general, one-step approach to ...construct valuable allenylphosphoryl frameworks with operational simplicity and high step- and atom-economy under ligand-, base-, and additive-free conditions.
This study investigates the infinite-horizon optimal control (IHOC) problem for switched Boolean control networks with an average cost criterion. A primary challenge of this problem is the ...prohibitively high computational cost when dealing with large-scale networks. We attempt to develop a more efficient approach from a novel graph-theoretical perspective. First, a weighted directed graph structure called the optimal state transition graph (OSTG) is established, whose edges encode the optimal action for each admissible state transition between states reachable from a given initial state subject to various constraints. Then, we reduce the IHOC problem into a minimum-mean cycle (MMC) problem in the OSTG. Finally, we develop an algorithm that can quickly find a particular MMC by resorting to Karp's algorithm in the graph theory and construct an optimal switching control law based on state feedback. The time complexity analysis shows that our algorithm, albeit still running in exponential time, can outperform all the existing methods in terms of time efficiency. A 16-state-3-input signaling network in leukemia is used as a benchmark to test its effectiveness. Results show that the proposed graph-theoretical approach is much more computationally efficient and can reduce the running time dramatically: it runs hundreds or even thousands of times faster than the existing methods. The Python implementation of the algorithm is available at https://github.com/ShuhuaGao/sbcn_mmc .
The Bayesian inversion of electromagnetic data can obtain key information on the uncertainty of subsurface resistivity. However, due to its high computational cost, Bayesian inversion is largely ...limited to 1-D resistivity models. In this study, a fast Bayesian inversion method is implemented by introducing the spatial correlation as prior information. The contributions of this article mainly include: 1) explicitly introduce the expression of spatial correlation prior information and provide a method to determine the parameters in the expression through the variogram theory. The influence of parameters in the spatial correlation prior information on the inversion results is systematically analyzed with the 1-D synthetic model. 2) The information entropy theory of continuous functions is introduced to quantify the degrees of freedom (DOF) of the parameters of the spatial correlation prior model. The analysis shows that the DOF of model parameters are significantly smaller than the number of model parameters when spatial correlation prior information is introduced, which is the main reason for the rapid Bayesian inversion. 3) Introducing the Sengpiel fast imaging algorithm, combined with the variogram theory, realized the direct acquisition of spatial correlation prior information from the observation data, minimizing the dependence on other information. The inversion results of 1-D and 2-D synthetic models and field datasets show that considering the spatial correlation prior information, hundreds of thousands of Markov chain Monte Carlo sampling steps are needed to enable the inversion of up to thousands of model parameters. This result provides a possible idea for future Bayesian inversion of complex 3-D models.
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