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Nano-hydroxyapatite (nHA) has been widely applied as a tissue-engineering biomaterial and interacted with osteoblasts/stem cells to repair bone defects. In addition, T cells that coexist with osteoblasts/stem cells in the bone modulate the regulation of osteoimmunology by cytokine formation. However, the effects of nHA on T cells and the following regulatory interplay on osteogenic differentiation have been rarely examined. In this work, the physicochemical properties of needle-like nHA are characterized by field emission scanning electron microscopy, zeta potential, Fourier transform-infrared and X-ray diffraction. It is found that as the concentration of nHA increases, the proliferation of T cells gradually increases, and the proportion of apoptotic T cells decreases. The percentage of CD4+ T cells is higher than that of CD8+ T cells under the regulation of needle-like nHA. Furthermore, the supernatant of T cells co-cultured with nHA significantly inhibits the osteogenic differentiation of MC3T3-E1 by downregulating the formation of alkaline phosphatase and calcium nodule compared with the supernatant of nHA. Thus, our findings provide new insight into the nHA-mediated T cell and osteoblast interactions.

Oral cancer is the sixth most common malignant cancer, affecting the health of people with an unacceptably high mortality rate. Despite numerous clinical methods in the diagnosis and therapy of oral cancer (e.g., magnetic resonance imaging, computed tomography, surgery, and chemoradiotherapy), they still remain far from optimal. Therefore, an urgent need exists for effective and practical techniques of early diagnosis and effective therapy of oral cancer. Currently, various types of nanoparticles have aroused wide public concern, representing a promising tool for diagnostic probes and therapeutic devices. Their inherent physicochemical features, including ultrasmall size, high reactivity, and tunable surface modification, enable them to overcome some of the limitations and achieve the expected diagnostic and therapeutic effect. In this review, we introduce different types of nanoparticles that emerged for the diagnosis and therapy of oral cancers. Then, the challenges and future perspectives for nanoparticles applied in oral cancer diagnosis and therapy are presented. The objective of this review is to help researchers better understand the effect of nanoparticles on oral cancer diagnosis and therapy and may accelerate breakthroughs in this field.

The hot deformation behavior of as-extruded 2055 Al-Li alloy at T6 state has been investigated by uniaxial hot compression test in a Gleeble-3800 thermal simulation machine at the temperature of 320 oC, 380 oC, 440 oC, 500 oC with strain rate ranging from 0.001 s−1 to 1 s−1. Deformation instability occurred under the deformation condition of 380 oC/5 s−1 and at the deformation temperature of 500 oC via the shape of flow curve and the side view observation of the compressed samples. The serration in flow curve of the low strain rate suggests occurring of DSA. The constitutive equation based on Arrhenius equation were established with the deformation activation energy of 174.6 kJ mol−1. The microstructures of the compressed samples were analyzed by OM and TEM, the DRXed grains could be observed in the compressed samples at 440 oC with strain rate of 1 s−1 and 0.001 s−1. The dominated precipitates in the alloy were plate-shaped T1 phase and spherical β 1 phase. The precipitates-dislocations interaction was analyzed by HRTEM. The dislocations could be pinned by both the T1 phase and β 1 phase, and the precipitates and dislocations around them divided the grain into small zones, and the misorientation across the dislocation was about 0.8o.

Based on the advantage of deep learning in object extraction, in this paper we design a deep network that adds Batch-Normalization to the convolution layer. Batch-Normalization has three main advantages. Firstly, it normalizes the input data, which can speed up the fitting of parameters. Secondly, Batch-Normalization can reconstruct the distribution of the input data, so that the feature of input data will not be lost. Thirdly, Batch-Normalization is able to prevent over-fitting, so it can replace Dropout, Local Response Normalization to simplify the network. The network in this paper adopted region proposal to get region of interests. Training classification and position adjustment at the same time to improve accuracy. Comprehensive experimental results have demonstrated the efficacy of the proposed network for objects detection.

This paper investigates the non-linear relative kinetic model of the collinear 4-craft Coulomb formation in geostationary orbit. Considering the fact that the formation remains statically fixed in the radial direction, using the equilibrium conditions to linearize the dynamic model. To keep the radial static stability of the collinear 4-craft Coulomb formation, the LQR controller is designed based on the linear dynamic model, taking the Coulomb force as the only control force. Considering the unmodeled disturbance force and the model error, the improved LQR control law is designed to improve the robustness of the traditional LQR controller. Simulation is executed by Matlab/Simulink, and numerical simulation results demonstrate the effectiveness of the dynamic model and the proposed control strategy.