Macrophage Membrane-Coated Nanoparticles Sensitize Glioblastoma to Radiation by Suppressing Proneural–Mesenchymal Transformation in Glioma Stem Cells

Radiotherapy is identified as a crucial treatment for patients with glioblastoma, but recurrence is inevitable. The efficacy of radiotherapy is severely hampered partially due to the tumor evolution. Growing evidence suggests that proneural glioma stem cells can acquire mesenchymal features coupled with increased radioresistance. Thus, a better understanding of mechanisms underlying tumor subclonal evolution may develop new strategies. Herein, data highlighting a positive correlation between the accumulation of macrophage in the glioblastoma microenvironment after irradiation and mesenchymal transdifferentiation in glioblastoma are presented. Mechanistically, elevated production of inflammatory cytokines released by macrophages promotes mesenchymal transition in an NF-κB-dependent manner. Hence, rationally designed macrophage membrane-coated porous mesoporous silica nanoparticles (MMNs) in which therapeutic anti-NF-κB peptides are loaded for enhancing radiotherapy of glioblastoma are constructed. The combination of MMNs and fractionated irradiation results in the blockage of tumor evolution and therapy resistance in glioblastoma-bearing mice. Intriguingly, the macrophage invasion across the blood-brain barrier is inhibited competitively by MMNs, suggesting that these nanoparticles can fundamentally halt the evolution of radioresistant clones. Taken together, the biomimetic MMNs represent a promising strategy that prevents mesenchymal transition and improves therapeutic response to irradiation as well as overall survival in patients with glioblastoma.     

An SPH-based mass transfer model for simulating hydraulic characteristics and mass transfer process of dammed rivers

Engineering with Computers - Aerated flow characterized by complex mass transfer processes with multiple hydraulic properties is a common enviro-hydraulics phenomenon, which have a variety of...     

Application of intelligent real-time image processing in fitness motion detection under internet of things

The purpose is to study the applicability of digital and intelligent real-time Image Processing (IP) in fitness motion detection under the environment of the Internet of Things (IoT). Given the absence of real-time training standards and possible workout injury problems during fitness activities, an intelligent fitness real-time IP system based on Deep Learning (DL) is implemented. Specifically, the keyframes of the real-time images are collected from the fitness monitoring video, and the DL algorithm is introduced to analyze the fitness motions. Afterward, the performance of the proposed system is evaluated through simulation. Subsequently, the Noise Reduction (NR) performance of the proposed algorithm is evaluated from the Peak Signal-to-Noise Ratio (PSNR), which remains above 20 dB for seriously noisy images (with a noise density reaching up to 90%). By comparison, the PSNR of the Standard Median Filter (SMF) and Ranked-order Based Adaptive Median Filter (RAMF) algorithms are not higher than 10 dB. Meanwhile, the proposed algorithm outperforms other DL algorithms by over 2.24% with a detection accuracy of 97.80%; the proposed system can adaptively detect the fitness motion, with a transmission delay no larger than 1 s given a maximum of 750 keyframes. Therefore, the proposed DL-based intelligent fitness real-time IP algorithm has strong robustness, high detection accuracy, and excellent real-time image diagnosis and processing effect, thus providing an experimental reference for sports digitalization and intellectualization.

     

Tensile Properties and Failure Mechanism of a New 3D Nonorthogonal Woven Composite Material

Tensile properties and failure mechanism of a newly developed three-dimensional (3D) woven composite material named 3D nonorthogonal woven composite are investigated in this paper. The microstructure of the composite is studied and the tensile properties are obtained by quasi-static tensile tests. The failure mechanism of specimen is discussed based on observation of the fracture surfaces via electron microscope. It is found that the specimens always split along the oblique yarns and produce typical v-shaped fracture surfaces. The representative volume cell (RVC) is established based on the microstructure. A finite element analysis is conducted with periodical boundary conditions. The finite element simulation results agree well with the experimental data. By analyzing deformation and stress distribution under different loading conditions, it is demonstrated that finite element model based on RVC is valid in predicting tensile properties of 3D nonorthogonal woven composites. Stress distribution shows that the oblique yarns and warp yarns oriented along the x direction carry primary load under x tension and that warp yarns bear primary load under y tension.     

Reliability analysis on systems with self-healing and self-repairing under different environments and shock models

Inspired by the process of self-healing of biological damage, high technology materials with self-healing and self-repairing mechanisms have been developed for high reliability and long lifetime. Therefore, the reliability modeling on intelligent systems with healing performance has become a research hotspot. Based on the diversity of healing mechanisms, this paper proposes a two-phase reliability model method on self-healing and self-repairing systems. Impacts of environments, shock loads, self-healing, and self-repairing mechanisms are taken into account in this novel model. Besides, system lifetime and some reliability indexes under two shock models are derived, respectively. Moreover, Monte Carlo simulations are conducted to verify the accuracy of reliability under two models. Finally, an engineering case of metallized film capacitor is provided to illustrate the effectiveness and applicability of the proposed models by comparing numerical results and simulation results.     

Comparison of approaches based on optimization and algebraic iteration for binary tomography

Binary tomography represents a special category of tomographic problems, in which only two values are possible for the sought image pixels. The binary nature of the problems can potentially lead to a significant reduction in the number of view angles required for a satisfactory reconstruction, thusly enabling many interesting applications. However, the limited view angles result in a severely underdetermined system of equations, which is challenging to solve. Various approaches have been proposed to address such a challenge, and two categories of approaches include those based on optimization and those based on algebraic iteration. However, the relative strengths, limitations, and applicable ranges of these approaches have not been clearly defined in the past. Therefore, it is the main objective of this work to conduct a systematic comparison of approaches from each category. This comparison suggested that the approaches based on algebraic iteration offered both superior reconstruction fidelity and computation efficiency at low (two or three) view angles, and these advantages diminished at high view angles. Meanwhile, this work also investigated the application of regularization techniques, the selection of optimal regularization parameter, and the use of a local search technique for binary problems. We expect the results and conclusions reported in this work to provide valuable guidance for the design and development of algorithms for binary tomography problems.     

Direct raycasting of unstructured cell-centered data by discontinuity Roe-average computation

In the field of computational fluid dynamics (CFD), the upwind finite volume method (FVM) is widely applied to solve 3D flows with discontinuity phenomena (e.g., shock waves). It produces unstructured data at the center of each cell (cell-centered data) with the flow discontinuity constraint on the inner-face between face-neighboring cells. For visualization, existing approaches with interpolation usually pre-extrapolate cell-centered data into cell-vertexed data (data values given at cell vertices) and only handle cell-vertexed data during actual rendering, which unconsciously depress the rendering accuracy and violate the discontinuity constraint. In this paper, we propose a novel method to visualize cell-centered data directly avoiding extrapolation and keep the discontinuity in the rendering data on the framework of multi-pass raycasting. During resampling, the field is reconstructed using the original cell-centered data value and the cell-gradient estimated by Green–Gauss theorem. To keep the discontinuity, we reconstruct the field at an inner-face resampled point using both the face-adjacencies and get two discontinuous field values. Then the field is obtained by computing Roe-average of the two. The analysis and experiments demonstrate that our approach gains a high-accuracy reconstruction and leads to a high-quality image.