Cost-effective Big Data Mining in the Cloud: A Case Study with K-means

Mining big data often requires tremendous computationalresources. This has become a major obstacle to broad applicationsof big data analytics. Cloud computing allows data scientists to access computationalresources on-demand for building their big data analytics solutions in the cloud

Impact of the Storm Sewer Network Complexity on Flood Simulations According to the Stroke Scaling Method

For urban watersheds, the storm sewer network provides indispensable data for flood modeling but often needs to be simplified to balance the conflict between the large amount of data and current computing power. The sensitivity of a flood simulation to the data precision of a storm sewer network needs to be explored to develop reasonable generalization strategies. In this study, the impact of using the stroke scaling method to generalize a storm sewer network on a flood simulation was analyzed in terms of the total inflow of the outfalls and flood results. The results of the three study basins showed that different complexities of a sewer network did not have a significant effect on the outfall’s total inflow for an area with a single drainage system but did for an area with multiple drainage systems. In addition, serious flooding was mainly distributed at the backbone pipes, which can be identified with the simplified sewer network. Several effective generalization strategies were developed for sewer networks that consider the distribution characteristics of the drainage system and application requirements. This study is theoretically important for better understanding the data sensitivity of flood modeling and simulation and practically important for improving the modeling efficiency and the accuracy of urban flood simulation

An uncertainty investigation of RCM downscaling ratios in nonstationary extreme rainfall IDF curves

Designed for rainstorms and flooding, hydrosystems are largely based on local rainfall Intensity–Duration–Frequency (IDF) curves which include nonstationary components accounting for climate variability. IDF curves are commonly calculated using downscaling outputs from General Circulation Models (GCMs) or Regional Circulation Models (RCMs). However, the downscaling procedures used in most studies are based on one specific time scale (e.g., 1 h) and generally ignore scale-driven uncertainty. This study analyzes the uncertainties in IDF curves stemming from RCM downscaling ratios for four representative weather stations in the United Kingdom. We constructed a series of IDF curves using distribution-based scaling bias-correction technology and a statistical downscaling method to explore the scale-driven uncertainty of IDF curves. The results revealed considerable scale-induced uncertainty of IDF curves for short durations and long return periods; however, there was no clear correlation with the mean storm intensity of the IDF curves of different RCM ensemble members for each duration and return period. The scale-driven uncertainty of IDF curves, which may be propagated or enhanced through hydrometeorological applications, is critical and cannot be ignored in the hydrosystem design process; therefore, a multi-scale method to derive IDF curves must be developed

Catalase Enhances Viability of Human Chondrocytes in Culture by Reducing Reactive Oxygen Species and Counteracting Tumor Necrosis Factor-α-Induced Apoptosis

Background/Aims: Both physiologic remodeling and pathologic regeneration of cartilage tissue rely upon chondrocyte functions and are benefited from factors that promote viability and inhibit apoptosis of the cell, and associated mechanisms. High level of reactive oxygen species (ROS) and proinflammatory cytokines activate apoptosis signaling and initiate cell death, which can be attenuated by antioxidants. This study examined the effect of catalase (CAT) on ROS and tumor necrosis factor-α (TNF-α)-induced apoptosis in human C28/I2 chondrocytes cultured in monolayer. Methods: Chondrocytes were treated with diluted CAT in the presence or absence of TNF-α and compared to untreated cells. Levels of hydrogen peroxide (H2O2) and mitochondrial membrane potential (Δψm) were measured using fluorescent labeling, cell apoptosis was assayed by flow cytometry using Annexin V/propidium iodide (PI) staining, gene expression was detected by quantitative real time polymerase chain reaction (qRT-PCR) and the proteins were investigated by Western blotting. Results: CAT effectively reduced the intracellular ROS caused by the monolayer culture system, enhanced the Δψm depending on the presence of TNF-α and promoted morphological features at sub-cellular level. CAT also attenuated the TNF-α-upregulated expression of factors/mediators of extrinsic cell death cascade and apoptotic caspases, ultimately resulted in promoted cellular viability. Conclusion: The anti-apoptotic effect of CAT on chondrocytes via scavenging ROS and suppressing TNF-α-induced cell apoptosis by TNF/TNF receptor (TNFR) mediated death signaling pathway and potentiate CAT as a complementary agent beneficial to cartilage remodeling and regeneration in vivo, and cell-based therapies of cartilage repair demanding viable cells expanded ex vivo

Investigation of diverse bacteria encoding histidine decarboxylase gene in Sichuan-style sausages by culture-dependent techniques, polymerase chain reaction-denaturing gradient gel electrophoresis, and high-throughput sequencing

The diverse bacteria encoding histidine decarboxylase gene during the fermentation of Sichuan-style sausages were investigated by culture-dependent techniques, polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE), and high-throughput sequencing. All microbial indicators exhibited the advantages of mixed starter culture and the stability of microecosystem was more in the inoculation group than in the control group. DGGE and selected band sequencing were used to investigate the bacterial diversity of these sausages. Weissella were the main lactic acid bacteria (LAB) in the initial fermentation stage, whereas Weissella and Lactobacillus were the dominant bacteria in the later fermentation stage. After sequence alignment analysis, Enterobacter aerogenes and Citrobacter freundi were the two main bacteria encoding histidine decarboxylase gene and could produce histamine. These findings facilitate the better understanding of bacteria producing histidine decarboxylase during sausage fermentation and provide a theoretical basis for the control of histamine-producing bacteria in the process of fermented sausage processing.Peer reviewe

Long lead-time radar rainfall nowcasting method incorporating atmospheric conditions using long short-term memory networks

High-resolution radar rainfall data have great potential for rainfall predictions up to 6 h ahead (nowcasting); however, conventional extrapolation approaches based on in-built physical assumptions yield poor performance at longer lead times (3–6 h), which limits their operational utility. Moreover, atmospheric factors in radar estimate errors are often ignored. This study proposed a radar rainfall nowcasting method that attempts to achieve accurate nowcasting of 6 h using long short-term memory (LSTM) networks. Atmospheric conditions were considered to reduce radar estimate errors. To build radar nowcasting models based on LSTM networks (LSTM-RN), approximately 11 years of radar, gauge rainfall, and atmospheric data from the UK were obtained. Compared with the models built on optical flow (OF-RN) and random forest (RF-RN), LSTM-RN had the lowest root-mean-square errors (RMSE), highest correlation coefficients (COR), and mean bias errors closest to 0. Furthermore, LSTM-RN showed a growing advantage at longer lead times, with the RMSE decreasing by 17.99% and 7.17% compared with that of OF-RN and RF-RN, respectively. The results also revealed a strong relationship between LSTM-RN performance and weather conditions. This study provides an effective solution for nowcasting radar rainfall at long lead times, which enhances the forecast value and supports practical utility