ObjectivePeriodontitis is characterized by alveolar bone injury and absorption, with high incidence and poor treatment effect. Proliferation, migration, differentiation and apoptosis of osteoblasts are identified as key factors during the regeneration of alveolar bone tissue processes. Periodontal ligament stem cells (PDLSCs) have been proved to be a possible candidate for the treatment of periodontitis due to its multiple advantages, such as increasing the regenerative capacity of bone tissue. However, the effect of exosomes derived from PDLSCs (PDLSC‐Exo) on osteoblasts remains to be further studied.Methods and MaterialsIn this work, cell proliferation, migration, osteogenic differentiation, and H2O2‐induced apoptosis were detected after cells were exposed to PDLSC‐Exo by CCK‐8, scratch wound assay, alizarin red S and alkaline phosphatase staining, real‐time PCR, flow cytometry, tunel assay, and so on. Moreover, the activation of PI3K/AKT and MEK/ERK signaling pathways was evaluated by western blotting.ResultsWe found that PDLSC‐Exo are capable of promoting hFOB1.19 cell proliferation, migration and osteogenic differentiation, inhibiting H2O2‐induced apoptosis, and activating the PI3K/AKT and MEK/ERK signaling pathways.ConclusionThese results suggest that PDLSC‐Exo may be a promising therapeutic for osteoblastic damage.

Root zone heating can solve the problems associated with the yield and decline in the quality caused by low-temperature stress in cucumber during winter and early spring. An experiment was performed to investigate the effects of different heating methods on the root zone temperature, growth and photosynthetic characteristics, fruit quality, and yield of cucumber. Using traditional soil cultivation (CK1) and sand cultivation (CK2) in a greenhouse as the controls, four heating treatments were set up: soil-ridge sand-embedded cultivation (T1), water-heated soil cultivation (T2), water-heated sand cultivation (T3), and water-curtain and floor-heating cultivation (T4). The results indicated that heating treatments T2 and T4 had better warming and insulation effects than the other treatments during both day and night, with an average temperature increase throughout the day of 0.8–1.2 °C compared with CK1. The chlorophyll content of leaves under the T2 and T4 treatments increased, and the photosynthetic rate and the overall plant growth were significantly higher than in the other treatments. Compared with the control, the fruit yield increased most significantly under the T2 and T4; the soluble sugar, soluble solids, and Vc contents in the fruit increased; while the nitrate content in the fruit decreased, effectively improving the fruit’s quality and yield. It was finally determined that the T2 and T4 heating treatments are the most effective in solving the low-temperature problem. Moreover, as T2 consumed relatively more electricity, the use of a water-curtain and floor-heating system in winter and spring should be considered in order to boost the yield and improve the quality.

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Unlike 2D object detection where all RoI features come from grid pixels, the RoI feature extraction of 3D point cloud object detection is more diverse. In this paper, we first compare and analyze the differences in structure and performance between the two state-of-the-art models PV-RCNN and Voxel-RCNN. Then, we find that the performance gap between the two models does not come from point information, but structural information. The voxel features contain more structural information because they do quantization instead of downsampling to point cloud so that they can contain basically the complete information of the whole point cloud. The stronger structural information in voxel features makes the detector have higher performance in our experiments even if the voxel features don't have accurate location information. Then, we propose that structural information is the key to 3D object detection. Based on the above conclusion, we propose a Self-Attention RoI Feature Extractor (SARFE) to enhance structural information of the feature extracted from 3D proposals. SARFE is a plug-and-play module that can be easily used on existing 3D detectors. Our SARFE is evaluated on both KITTI dataset and Waymo Open dataset. With the newly introduced SARFE, we improve the performance of the state-ofthe-art 3D detectors by a large margin in cyclist on KITTI dataset while keeping real-time capability. The code will be released at https://github.com/Poley97/SARFE.