An agent-based hybrid system for microarray data analysis

This article reports our experience in agent-based hybrid construction for microarray data analysis. The contributions are twofold: We demonstrate that agent-based approaches are suitable for building hybrid systems in general, and that a genetic ensemble system is appropriate for microarray data analysis in particular. Created using an agent-based framework, this genetic ensemble system for microarray data analysis excels in both sample classification accuracy and gene selection reproducibility.<br /

Buddleoside inhibits TLR4-related pathway in a mouse model of acute liver failure, promotes autophagy, and inhibits inflammation

Purpose: To study the inhibitory influence of buddleoside on TLR4-associated pathway, autophagy and inflammation in a mouse model of acute liver failure (ALF).Methods: Sixty male C57BL/6 mice were assigned to 5 groups: control, model, and three dose-groups of buddleoside, with 12 mice per group. Levels of interleukin (IL)-1, IL-6, TLR4 pathway-associated proteins, and autophagy-related proteins in each group were determined; cell adhesion in each group was also analyzed.Results: Levels of TLR4, MAPK and NF-кB-related pathways in model mice were significantly upregulated, relative to control mice, but they were more down-regulated in the 3 anthocyanin groups than in model group (p &lt; 0.05). There were significantly higher levels of TNF-α, IL- and IL-6 in model mice than in the control group, but they were down-regulated in high-, medium- and low-dose mice, relative to model mice. The population of adherent cells was significantly higher in ALF mice than in controls, butthere were markedly lower numbers of these cells in the 3 anthocyanin-treated mice than in model mice (p &lt; 0.05).Conclusion: Buddleoside mitigates ALF in mice by down-regulating inflammatory factors, reducing serum levels of ALT and AST, and up-regulating autophagy-related protein expressions by activating TLR4/MAPK/NF-кB signaling pathway. Thus, buddleoside may be useful in the treatment of acute liver failure, but this has to be curtained through clinical trials

Sub-GMN: The Neural Subgraph Matching Network Model

As one of the most fundamental tasks in graph theory, subgraph matching is a crucial task in many fields, ranging from information retrieval, computer vision, biology, chemistry and natural language processing. Yet subgraph matching problem remains to be an NP-complete problem. This study proposes an end-to-end learning-based approximate method for subgraph matching task, called subgraph matching network (Sub-GMN). The proposed Sub-GMN firstly uses graph representation learning to map nodes to node-level embedding. It then combines metric learning and attention mechanisms to model the relationship between matched nodes in the data graph and query graph. To test the performance of the proposed method, we applied our method on two databases. We used two existing methods, GNN and FGNN as baseline for comparison. Our experiment shows that, on dataset 1, on average the accuracy of Sub-GMN are 12.21\% and 3.2\% higher than that of GNN and FGNN respectively. On average running time Sub-GMN runs 20-40 times faster than FGNN. In addition, the average F1-score of Sub-GMN on all experiments with dataset 2 reached 0.95, which demonstrates that Sub-GMN outputs more correct node-to-node matches. Comparing with the previous GNNs-based methods for subgraph matching task, our proposed Sub-GMN allows varying query and data graphes in the test/application stage, while most previous GNNs-based methods can only find a matched subgraph in the data graph during the test/application for the same query graph used in the training stage. Another advantage of our proposed Sub-GMN is that it can output a list of node-to-node matches, while most existing end-to-end GNNs based methods cannot provide the matched node pairs

Output Voltage Response Improvement and Ripple Reduction Control for Input-parallel Output-parallel High-Power DC Supply

A three-phase isolated AC-DC-DC power supply is widely used in the industrial field due to its attractive features such as high-power density, modularity for easy expansion and electrical isolation. In high-power application scenarios, it can be realized by multiple AC-DC-DC modules with Input-Parallel Output-Parallel (IPOP) mode. However, it has the problems of slow output voltage response and large ripple in some special applications, such as electrophoresis and electroplating. This paper investigates an improved Adaptive Linear Active Disturbance Rejection Control (A-LADRC) with flexible adjustment capability of the bandwidth parameter value for the high-power DC supply to improve the output voltage response speed. To reduce the DC supply ripple, a control strategy is designed for a single module to adaptively adjust the duty cycle compensation according to the output feedback value. When multiple modules are connected in parallel, a Hierarchical Delay Current Sharing Control (HDCSC) strategy for centralized controllers is proposed to make the peaks and valleys of different modules offset each other. Finally, the proposed method is verified by designing a 42V/12000A high-power DC supply, and the results demonstrate that the proposed method is effective in improving the system output voltage response speed and reducing the voltage ripple, which has significant practical engineering application value.Comment: Accepted by IEEE Transactions on Power Electronic

Balanced Order Batching with Task-Oriented Graph Clustering

Balanced order batching problem (BOBP) arises from the process of warehouse picking in Cainiao, the largest logistics platform in China. Batching orders together in the picking process to form a single picking route, reduces travel distance. The reason for its importance is that order picking is a labor intensive process and, by using good batching methods, substantial savings can be obtained. The BOBP is a NP-hard combinational optimization problem and designing a good problem-specific heuristic under the quasi-real-time system response requirement is non-trivial. In this paper, rather than designing heuristics, we propose an end-to-end learning and optimization framework named Balanced Task-orientated Graph Clustering Network (BTOGCN) to solve the BOBP by reducing it to balanced graph clustering optimization problem. In BTOGCN, a task-oriented estimator network is introduced to guide the type-aware heterogeneous graph clustering networks to find a better clustering result related to the BOBP objective. Through comprehensive experiments on single-graph and multi-graphs, we show: 1) our balanced task-oriented graph clustering network can directly utilize the guidance of target signal and outperforms the two-stage deep embedding and deep clustering method; 2) our method obtains an average 4.57m and 0.13m picking distance ("m" is the abbreviation of the meter (the SI base unit of length)) reduction than the expert-designed algorithm on single and multi-graph set and has a good generalization ability to apply in practical scenario.Comment: 10 pages, 6 figure

Targeted suppression of heme oxygenase-1 by small interference RNAs inhibits the production of bilirubin in neonatal rat with hyperbilirubinemia

<p>Abstract</p> <p>Background</p> <p>Excessive accumulation of bilirubin contributes to neonatal hyperbilirubinemia in rats. Heme oxygenase (HO) is one of the rate-limiting enzymes in catabolizing heme to bilirubin. In the present study, we investigated whether suppression of rat HO-1 (rHO-1) expression by small interference RNAs (siRNAs) reduces bilirubin levels in hyperbilirubinemic rats.</p> <p>Results</p> <p>Four pairs of siRNA targeting rHO-1 mRNA were introduced into BRL cells and compared for their inhibitory effect on the expression of <it>rHO-1 </it>gene and production of rHO-1 protein. The siRNA exhibiting the most potent effect on HO-1 expression and activity was then administered intraperitoneally to 7 to 9-day-old rats with hyperbilirubinemia. The siRNA distributed mostly in the liver and spleen of neonatal rat. Serum bilirubin levels and hepatic HO-1 expression were further evaluated. Systemic treatment of siRNA targeting rHO-1 reduced hepatic HO-1 expression and decreased the serum bilirubin levels in a time- and dose-dependent manner, and siRNA decreased the indirect bilirubin levels more effectively than Sn-protoporphyrin (SnPP), an HO-1 inhibitor.</p> <p>Conclusion</p> <p>siRNA targeting rHO-l attenuates hepatic HO-1 expression and serum bilirubin levels. Thus this study provides a novel therapeutic rationale for the prevention and treatment of neonatal hyperbilirubinemia.</p