• 한국약용작물학회지
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• 제13권2호
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• pp.75-79
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• 2005

Five coumarins, psoralen (1), scopoletin (2), isoimperatorin (4), (+)-marmesin (5) and xanthotoxin (6), three chromones, cimifugin (3), hamaudol (7) and sec-O-glucosylhamaudol (10), one sterol, daucosterol (8) and one aliphatic alcohol, galactitol (9) were isolated from the root of Peucedanum japonicum. Their chemical structures were identified by the physicochemical and spectroscopic data by comparing literature values. Among them, compounds 9 and 10 were isolated for the first time from this plant. The anti-inflammatory effects of isolated compounds were examined on cyclooxygenase (COX), compounds 1, 2 and 7 showed inhibitory activity on COX-1 with $IC_{50}$ values of 0.88, 0.27 and 0.30 mM, respectively. In the test for COX-2 activity, only compound 7 showed significant inhibitory activity with the $IC_{50}$ value of 0.57 mM. The other compounds exhibited weak inhibitory or no inhibitory activity.

• Steel and Composite Structures
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• 제17권2호
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• pp.199-213
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• 2014

The damage characters of scarf repaired composite laminates subjected to low-velocity impact with various energy levels at different locations are studied experimentally. The results are compared with those of the original laminates which have no initial damage and don't need repair. The impact load-time history of the specimens, the velocity-time curves of the impactor, the post impact compressive strength of the specimens and the C-scan photographs of the damaged regions are obtained. The delamination threshold load and damage character of the specimen section at impact point are also studied. The results have shown that the impact response of a repaired composite laminate is sensitive to the location of the impact. The impact load and the delamination threshold load have shown different characters for specimens with different impact locations. The debonding characters of the adhesive and compressive strength after impact of the specimens are also influenced by impact locations.

• Molecules and Cells
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• 제43권9호
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• pp.793-803
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• 2020

Myeloid-derived suppressor cells (MDSCs) promote tumour progression by contributing to angiogenesis, immunosuppression, and immunotherapy resistance. Although recent studies have shown that microRNAs (miRNAs) can promote the expansion of MDSCs in the tumour environment, the mechanisms involved in this process are largely unknown. Here, we report that microRNA 449c (miR-449c) expression was upregulated in myeloid progenitor cells upon activation of C-X-C motif chemokine receptor 2 (CXCR2) under tumour conditions. MiR-449c upregulation increased the generation of monocytic MDSCs (mo-MDSCs). The increased expression of miR-449c could target STAT6 mRNA in myeloid progenitor cells to shift the differentiation balance of myeloid progenitor cells and lead to an enhancement of the mo-MDSCs population in the tumour environment. Thus, our results demonstrate that the miR-449c/STAT6 axis is involved in the expansion of mo-MDSCs from myeloid progenitor cells upon activation of CXCR2, and thus, inhibition of miR-449c/STAT6 signalling may help to attenuate tumour progression.

• Structural Engineering and Mechanics
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• 제83권3호
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• pp.293-304
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• 2022

In this work, a novel reliability approach for combined high and low cycle fatigue (CCF) estimation is developed by combining active learning strategy with least squares support vector machines (LS-SVM) (named as ALS-SVM) surrogate model to address the multi-resources uncertainties, including working loads, material properties and model itself. Initially, a new active learner function combining LS-SVM approach with Monte Carlo simulation (MCS) is presented to improve computational efficiency with fewer calls to the performance function. To consider the uncertainty of surrogate model at candidate sample points, the learning function employs k-fold cross validation method and introduces the predicted variance to sequentially select sampling. Following that, low cycle fatigue (LCF) loads and high cycle fatigue (HCF) loads are firstly estimated based on the training samples extracted from finite element (FE) simulations, and their simulated responses together with the sample points of model parameters in Coffin-Manson formula are selected as the MC samples to establish ALS-SVM model. In this analysis, the MC samples are substituted to predict the CCF reliability of turbine blades by using the built ALS-SVM model. Through the comparison of the two approaches, it is indicated that the reliability model by linear cumulative damage rule provides a non-conservative result compared with that by the proposed one. In addition, the results demonstrate that ALS-SVM is an effective analysis method holding high computational efficiency with small training samples to gain accurate fatigue reliability.