MicroRNAs (miRNAs) are short, highly conserved small non-coding RNA molecules, which post-transcriptionally regulate genes expression and play crucial roles in diverse biological processes. Recent studies have shown that dysregulation of miRNAs might modulate the resistance of cancer cells to chemotherapeutic agents. To investigate the possible role of miR-130a in the development of cisplatin resistance in human ovarian cancer cell line A2780, we evaluated the expression of microRNA-130a (miR-130a) in the cells by the quantitative real-time reverse transcriptionpolymerase chain reaction. The results showed that miR130a was significantly down-regulated in cisplatin-resistant ovarian cancer cells. MTT assay and flow cytometry (FCM) results showed that over-expression of miR-130a regulated apoptotic activity, and thereby cisplatin chemosensitivity, in ovarian cancer cells. Furthermore, we found that miR-130a can directly target XIAP, and participate in the regulation of apoptosis. The up-regulation of miR-130a led to a significant decrease in the XIAP mRNA levels and protein levels. XIAP plays an important role in cisplatin resistance in ovarian cancer cell line A2780. Our findings suggested that miR-130a could play a role in the development of cisplatin resistance in ovarian cancer cell line A2780, at least in part by modulation of apoptosis via targeting XIAP.

This paper summarizes the main flash flood early-warning systems of America, Europe, Japan, and Taiwan China and discusses their advantages and disadvantages. The latest development in flash flood prevention is also presented. China's flash flood prevention system involves three stages. Herein, the warning methods and achievements in the first two stages are introduced in detail. Based on the worldwide experience of flash flood early-warning systems, the general research idea of the third stage is proposed from the viewpoint of requirements for flash flood prevention and construction progress of the next stage in China. Real-time dynamic warning systems can be applied to the early-warning platform at four levels (central level, provincial level, municipal level, and county level) . Through this, soil moisture, peak flow, and water level can be calculated in real-time using distributed hydrological models, and then flash flood warning indexes can be computed based on defined thresholds of runoff and water level. A compound warning index (CWI) can be applied to regions where rainfall and water level are measured by simple equipment. In this manner, flash-flood-related factors such as rainfall intensity and antecedent and cumulative rainfall depths can be determined using the CWI method. The proposed methodology for the third stage could support flash flood prevention measures in the 13th (2016)(2017)(2018)(2019)(2020). The research achievements will serve as a guidance for flash flood monitoring and warning as well as flood warning in medium and small rivers.

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Abstract. Deriving large-scale and high-quality precipitation products from satellite remote-sensing spectral data is always challenging in quantitative precipitation estimation (QPE), and limited studies have been conducted even using China's latest Fengyun-4A (FY-4A) geostationary satellite. Taking three rainstorm events over South China as examples, a machine-learning-based regression model was established using the random forest (RF) method to derive QPE from FY-4A observations, in conjunction with cloud parameters and physical quantities. The cross-validation results indicate that both daytime (DQPE) and nighttime (NQPE) RF algorithms performed well in estimating QPE, with the bias score, correlation coefficient and root-mean-square error of DQPE (NQPE) of 2.17 (2.42), 0.79 (0.83) and 1.77 mm h−1 (2.31 mm h−1), respectively. Overall, the algorithm has a high accuracy in estimating precipitation under the heavy-rain level or below. Nevertheless, the positive bias still implies an overestimation of precipitation by the QPE algorithm, in addition to certain misjudgements from non-precipitation pixels to precipitation events. Also, the QPE algorithm tends to underestimate the precipitation at the rainstorm or even above levels. Compared to single-sensor algorithms, the developed QPE algorithm can better capture the spatial distribution of land-surface precipitation, especially the centre of strong precipitation. Marginal difference between the data accuracy over sites in urban and rural areas indicate that the model performs well over space and has no evident dependence on landscape. In general, our proposed FY-4A QPE algorithm has advantages for quantitative estimation of summer precipitation over East Asia.