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Pu isotopes and (137)Cs were analyzed using sector field ICP-MS and γ spectrometry, respectively, in surface sediment and core sediment samples from the Yangtze River estuary. (239+240)Pu activity and (240)Pu/(239)Pu atom ratios (>0.18) shows a generally increasing trend from land to sea and from north to south in the estuary. This spatial distribution pattern indicates that the Pacific Proving Grounds (PPG) source Pu transported by ocean currents was intensively scavenged into the suspended sediment under favorable conditions, and mixed with riverine sediment as the water circulated in the estuary. This process is the main control for the distribution of Pu in the estuary. Moreover, Pu is also an important indicator for monitoring the changes of environmental radioactivity in the estuary as the river basin is currently the site of extensive human activities and the sea level is rising because of global climate changes. For core sediment samples the maximum peak of (239+240)Pu activity was observed at a depth of 172 cm. The sedimentation rate was estimated on the basis of the Pu maximum deposition peak in 1963-1964 to be 4.1 cm/a. The contributions of the PPG close-in fallout Pu (44%) and the riverine Pu (45%) in Yangtze River estuary sediments are equally important for the total Pu deposition in the estuary, which challenges the current hypothesis that the riverine Pu input was the major source of Pu budget in this area.

Colorectal cancer (CRC) is the third leading cause of cancer-related death in the western world. In this study, we evaluated the expression of matrix metalloproteinase 2 gene (MMP2) in CRC and analyzed its correlation with clinicopathological features. We found that the expression of MMP2 was significantly higher in CRC tissues than in the colorectal tissues. In addition, high levels of MMP2 protein were positively correlated with the status of tumor size, lymph node metastasis, distant metastasis, Dukes' stage, and tumor invasion. Moreover, patients with higher MMP2 levels had markedly shorter overall survivals than those with low MMP2 levels. Multivariate analysis results suggested that the level of MMP2 expression is an independent prognostic indicator for the survival of patients with CRC. Silencing MMP2 expression in CRC cell lines with lentiviral-mediated shRNA markedly suppressed cell proliferation, colony formation, and invasion. Furthermore, we observed that vascular endothelial growth factor (VEGF) and membrane type 1 (MT1)-MMP protein levels were decreased in MMP2-down-regulated colorectal cells. Therefore, our study demonstrated that MMP2 is an important factor related to carcinogenesis and metastasis of CRC, and MMP2 promotes CRC cell growth and invasion by up-regulating VEGF and MT1-MMP expression, which makes this pathway a potential target for cancer treatment.

In this study, we proposed an ensemble learning method, simultaneously integrating a low-rank matrix completion model and a ridge regression model to predict anticancer drug response on cancer cell lines. The model was applied to two benchmark datasets, including the Cancer Cell Line Encyclopedia (CCLE) and the Genomics of Drug Sensitivity in Cancer (GDSC). As previous studies suggest, the dual-layer integrated cell line-drug network model was one of the best models by far and outperformed most state-of-the-art models. Thus, we performed a head-to-head comparison between the dual-layer integrated cell line-drug network model and our model by a 10-fold crossvalidation study. For the CCLE dataset, our model has a higher Pearson correlation coefficient between predicted and observed drug responses than that of the dual-layer integrated cell line-drug network model in 18 out of 23 drugs. For the GDSC dataset, our model is better in 26 out of 28 drugs in the phosphatidylinositol 3-kinase (PI3K) pathway and 26 out of 30 drugs in the extracellular signal-regulated kinase (ERK) signaling pathway, respectively. Based on the prediction results, we carried out two types of case studies, which further verified the effectiveness of the proposed model on the drug-response prediction. In addition, our model is more biologically interpretable than the compared method, since it explicitly outputs the genes involved in the prediction, which are enriched in functions, like transcription, Src homology 2/3 (SH2/3) domain, cell cycle, ATP binding, and zinc finger.