Junjie Hu

BACKGROUND: Melatonin is an amine hormone that plays an important role in regulating mammalian reproduction. This study aimed to investigate the expression pattern of melatonin synthesis enzymes AANAT and HIOMT and melatonin receptors MT1 and MT2 in sheep cumulus-oocyte complexes (COCs) as well as the change of melatonin level in follicular fluid (FF) during antral follicle development. In this research, we also study the effect of β-estradiol (E2) on MT1 and MT2 expression as well as melatonin synthesis in COCs so as to lay the foundation for further exploration of the regulation mechanism of melatonin synthesis in the ovary. METHODS: COCs and FF were collected from different size (large follicles (diameter ≥ 5 mm), medium follicles (diameter 2-5 mm), and small follicles (diameter ≤ 2 mm)) of antral follicles in sheep ovaries. To assess whether E2 regulates melatonin synthase and its receptors expression in sheep COCs and whether it is mediated through estrogen receptor (ER) pathway. The collected COCs were cultured in vitro for 24 h and then treat with 1 μM E2 and/or 1 μM ICI182780 (non-selective ER antagonist). The expression of AANAT, HIOMT, MT1 and MT2 mRNA and protein were determined by qRT-PCR and western blot. The melatonin level was determined by ELISA. RESULTS: The expression of AANAT, HIOMT, MT1 and MT2 were significantly higher expression in the COCs of small follicles than in those of large follicles (P < 0.05). However, the melatonin level was significantly higher in large follicle FF than in small follicle FF (P < 0.05). Further, the expression of AANAT, HIOMT, MT1, and MT2 and melatonin production were decreased by E2 treatment (P < 0.05), but when ICI182780 was added, the expression of AANAT, HIOMT, MT1, and MT2 and melatonin production recovered (P < 0.05). CONCLUSIONS: We suggest that sheep COCs can synthesize melatonin, but this ability is decreased with increasing follicle diameter. Furthermore, E2 play an important role in regulated the expression of MT1 and MT2 as well as melatonin synthesis in sheep COCs through the ER pathway.

In China, Baitouweng Tang (BTWT) is a commonly prescribed remedy for the treatment of ulcerative colitis (UC). Herein, the present study aims to assess the anti-colitis activity of BTWT and its underlying mechanisms in UC BALB/c mice. Induction of UC in BALB/c mice was carried out by adding 3.5% DSS in the drinking water of underlined mice. After UC induction, the mice were administrated with BTWT for 7 days. Clinical symptoms were assessed, followed by analyzing the bile acids (BAs) in serum, liver, colon, bile, and feces of UC mice through UPLC-MS/MS. The modified 16S rDNA high-throughput sequencing was carried out to examine the gut microbiota of feces. BTWT significantly improved the clinical symptoms such as and histological injury and colon shortening in UC induced mice. Furthermore, BTWT remarkably ameliorated colonic inflammatory response. After BTWT treatment, the increased concentrations of UDCA, HDCA, αMCA, βMCA, CA, and GLCA in UC were decreased, and the levels of some BAs, especially CA, αMCA, and βMCA were normalized. Moreover, the relative species abundance and gut microbiota diversity in the BTWT-exposed groups were found to be considerably elevated than those in the DSS-treated group. BTWT increased the relative abundance of Firmicutes, Proteobacteria, Actinobacteria, Tenericutes, and TM7, which were statistically lower in the fecal microbiota of UC mice. The relative abundance of Bacteroidetes was found to be elevated in the DSS group and normalized after BTWT treatment. BTWT increased the expression of FXR and TGR5 in the liver. BTWT administration improved DSS-induced mice signs by increasing the TGR5 and FXR expression levels. This result was achieved by the regulation of the BAs and gut microbiota.

Classifying binary imbalanced streaming data is a significant task in both machine learning and data mining. Previously, online area under the receiver operating characteristic (ROC) curve (AUC) maximization has been proposed to seek a linear classifier. However, it is not well suited for handling nonlinearity and heterogeneity of the data. In this paper, we propose the kernelized online imbalanced learning (KOIL) algorithm, which produces a nonlinear classifier for the data by maximizing the AUC score while minimizing a functional regularizer. We address four major challenges that arise from our approach. First, to control the number of support vectors without sacrificing the model performance, we introduce two buffers with fixed budgets to capture the global information on the decision boundary by storing the corresponding learned support vectors. Second, to restrict the fluctuation of the learned decision function and achieve smooth updating, we confine the influence on a new support vector to its -nearest opposite support vectors. Third, to avoid information loss, we propose an effective compensation scheme after the replacement is conducted when either buffer is full. With such a compensation scheme, the performance of the learned model is comparable to the one learned with infinite budgets. Fourth, to determine good kernels for data similarity representation, we exploit the multiple kernel learning framework to automatically learn a set of kernels. Extensive experiments on both synthetic and real-world benchmark data sets demonstrate the efficacy of our proposed approach.