Integrated Microarray to Identify the Hub miRNAs and Constructed miRNA–mRNA Network in Neuroblastoma Via Bioinformatics Analysis

Neurochemical Research - Neuroblastomas (NB) are childhood malignant tumors originating in the sympathetic nervous system. MicroRNAs (miRNAs) play an essential regulatory role in tumorigenesis and...     

NLRP3 regulates alveolar bone loss in ligature‐induced periodontitis by promoting osteoclastic differentiation

ObjectivesNLRP3 inflammasome is a critical part of the innate immune system and plays an important role in a variety of inflammatory diseases. However, the effects of NLRP3 inflammasome on periodontitis have not been fully studied.Materials and methodsWe used ligature‐induced periodontitis models of NLRP3 knockout mice (NLRP3^KO) and their wildtype (WT) littermates to compare their alveolar bone phenotypes. We further used Lysm‐Cre/Rosa^nTnG mouse to trace the changes of Lysm‐Cre⁺ osteoclast precursors in ligature‐induced periodontitis with or without MCC950 treatment. At last, we explored MCC950 as a potential drug for the treatment of periodontitis in vivo and in vitro.ResultsHere, we showed that the number of osteoclast precursors, osteoclast differentiation and alveolar bone loss were reduced in NLRP3^KO mice compared with WT littermates, by using ligature‐induced periodontitis model. Next, MCC950, a specific inhibitor of the NLRP3 inflammasome, was used to inhibit osteoclast precursors differentiation into osteoclast. Further, we used Lysm‐Cre/Rosa^nTnG mice to demonstrate that MCC950 decreases the number of Lysm‐Cre⁺ osteoclast precursors in ligature‐induced periodontitis. At last, treatment with MCC950 significantly suppressed alveolar bone loss with reduced IL‐1β activation and osteoclast differentiation in ligature‐induced periodontitis.ConclusionOur findings reveal that NLRP3 regulates alveolar bone loss in ligature‐induced periodontitis by promoting osteoclastic differentiation.     

Application of plant metabarcoding to identify diverse honeybee pollen forage along an urban–agricultural gradient

Understanding animal foraging ecology requires large sample sizes spanning broad environmental and temporal gradients. For pollinators, this has been hampered by the laborious nature of morphologically identifying pollen. Identifying pollen from urban environments is particularly difficult due to the presence of diverse ornamental species associated with consumer horticulture. Metagenetic pollen analysis represents a potential solution to this issue. Building upon prior laboratory and bioinformatic methods, we applied quantitative multilocus metabarcoding to characterize the foraging ecology of honeybee colonies situated in urban, suburban, mixed suburban–agricultural and rural agricultural sites in central Ohio, USA. In cross‐validating a subset of our metabarcoding results using microscopic palynology, we find strong concordance between the molecular and microscopic methods. Our results suggest that forage from the agricultural site exhibited decreased taxonomic diversity and temporal turnover relative to the urban and suburban sites, though the generalization of this observation will require replication across additional sites and cities. Our work demonstrates the power of honeybees as environmental samplers of floral community composition at large spatial scales, aiding in the distinction of taxa characteristically associated with urban or agricultural land use from those distributed ubiquitously across the sampled landscapes. Observed patterns of high forage diversity and compositional turnover in our more urban sites are likely reflective of the fine‐grain heterogeneity and high beta diversity of urban floral landscapes at the scale of honeybee foraging. This provides guidance for future studies investigating how relationships between urbanization and measures of pollinator health are mediated by variation in floral resource dynamics across landscapes.     

基于多尺度标记优化分水岭方法的油茶冠幅提取

针对尺度对地物空间结构的限制以及传统分水岭分割易产生树冠过分割等问题,选择长沙县明月村油茶基地为研究区,提出一种基于多尺度标记优化分水岭分割油茶树冠的方法。首先使用高分辨率无人机影像采集图像,分析影像特征,构建油茶分类体系,提取油茶林分布区域。其次,运用多尺度区域迭代增长方法提取树冠标记,将标记应用于多阈值尺度的分水岭变换,并结合Johnson指数选取树冠标记增长和分水岭阈值的最优尺度,实现油茶单木的准确识别。结果表明:多尺度标记优化分水岭方法在分离油茶单木时,树冠面积提取值与目视解译参考值的相对误差为9.4%;单木总体识别精度为89.4%,相对于传统的分水岭分割方法精度提高了34.8%;通过Johnson指数确定的最优迭代增长尺度为20,分水岭分割阈值尺度为85,对比不同尺度组合下的油茶冠幅提取结果,最优尺度下的油茶冠幅提取精度最高(R²=0.75)。多尺度标记优化分水岭方法能较准确地分离油茶树冠,将该方法应用于无人机影像树冠分割,可有效提高经济林调查的效率。