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

LE-SSL-MOS: Self-Supervised Learning MOS Prediction with Listener Enhancement

Recently, researchers have shown an increasing interest in automatically predicting the subjective evaluation for speech synthesis systems. This prediction is a challenging task, especially on the out-of-domain test set. In this paper, we proposed a novel fusion model for MOS prediction that combines supervised and unsupervised approaches. In the supervised aspect, we developed an SSL-based predictor called LE-SSL-MOS. The LE-SSL-MOS utilizes pre-trained self-supervised learning models and further improves prediction accuracy by utilizing the opinion scores of each utterance in the listener enhancement branch. In the unsupervised aspect, two steps are contained: we fine-tuned the unit language model (ULM) using highly intelligible domain data to improve the correlation of an unsupervised metric - SpeechLMScore. Another is that we utilized ASR confidence as a new metric with the help of ensemble learning. To our knowledge, this is the first architecture that fuses supervised and unsupervised methods for MOS prediction. With these approaches, our experimental results on the VoiceMOS Challenge 2023 show that LE-SSL-MOS performs better than the baseline. Our fusion system achieved an absolute improvement of 13% over LE-SSL-MOS on the noisy and enhanced speech track. Our system ranked 1st and 2nd, respectively, in the French speech synthesis track and the challenge's noisy and enhanced speech track.Comment: accepted in IEEE-ASRU202

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

Nano Catalysts for Diesel Engine Emission Remediation

The objective of this project was to develop durable zeolite nanocatalysts with broader operating temperature windows to treat diesel engine emissions to enable diesel engine based equipment and vehicles to meet future regulatory requirements. A second objective was to improve hydrothermal durability of zeolite catalysts to at least 675 C. The results presented in this report show that we have successfully achieved both objectives. Since it is accepted that the first step in NO{sub x} conversion under SCR (selective catalytic reduction) conditions involves NO oxidation to NO{sub 2}, we reasoned that catalyst modification that can enhance NO oxidation at low-temperatures should facilitate NO{sub x} reduction at low temperatures. Considering that Cu-ZSM-5 is a more efficient catalyst than Fe-ZSM-5 at low-temperature, we chose to modify Cu-ZSM-5. It is important to point out that the poor low-temperature efficiency of Fe-ZSM-5 has been shown to be due to selective absorption of NH{sub 3} at low-temperatures rather than poor NO oxidation activity. In view of this, we also reasoned that an increased electron density on copper in Cu-ZSM-5 would inhibit any bonding with NH{sub 3} at low-temperatures. In addition to modified Cu-ZSM-5, we synthesized a series of new heterobimetallic zeolites, by incorporating a secondary metal cation M (Sc{sup 3+}, Fe{sup 3+}, In{sup 3+}, and La{sup 3+}) in Cu exchanged ZSM-5, zeolite-beta, and SSZ-13 zeolites under carefully controlled experimental conditions. Characterization by diffuse-reflectance ultra-violet-visible spectroscopy (UV-Vis), X-ray powder diffraction (XRD), extended X-ray absorption fine structure spectroscopy (EXAFS) and electron paramagnetic resonance spectroscopy (EPR) does not permit conclusive structural determination but supports the proposal that M{sup 3+} has been incorporated in the vicinity of Cu(II). The protocols for degreening catalysts, testing under various operating conditions, and accelerated aging conditions were provided by our collaborators at John Deere Power Systems. Among various zeolites reported here, CuFe-SSZ-13 offers the best NO{sub x} conversion activity in 150-650 C range and is hydrothermally stable when tested under accelerated aging conditions. It is important to note that Cu-SSZ-13 is now a commercial catalyst for NO{sub x} treatment on diesel passenger vehicles. Thus, our catalyst performs better than the commercial catalyst under fast SCR conditions. We initially focused on fast SCR tests to enable us to screen catalysts rapidly. Only the catalysts that exhibit high NO{sub x} conversion at low temperatures are selected for screening under varying NO{sub 2}:NO{sub x} ratio. The detailed tests of CuFe-SSZ-13 show that CuFe-SSZ-13 is more effective than commercial Cu-SSZ-13 even at NO{sub 2}:NO{sub x} ratio of 0.1. The mechanistic studies, employing stop-flow diffuse reflectance FTIR spectroscopy (DRIFTS), suggest that high concentration of NO{sup +}, generated by heterobimetallic zeolites, is probably responsible for their superior low temperature NO{sub x} activity. The results described in this report clearly show that we have successfully completed the first step in a new emission treatment catalyst which is synthesis and laboratory testing employing simulated exhaust. The next step in the catalyst development is engine testing. Efforts are in progress to obtain follow-on funding to carry out scale-up and engine testing to facilitate commercialization of this technology

Echinacea increases arginase activity and has anti-inflammatory properties in RAW 264.7 macrophage cells indicative of alternative macrophage activation

The genus Echinacea is a popular herbal immunomodulator. Recent reports indicate that Echinacea products inhibit nitric oxide (NO) production in activated macrophages. In the present study we determined the inhibitory effects of alcohol extracts and individual fractions of alcohol extracts of Echinacea on NO production, and explored the mechanism underlying the pharmacological anti-inflammatory activity. The alcohol extracts of three medicinal Echinacea species, E. angustifolia, E. pallida and E. purpurea, significantly inhibited NO production by lipopolysaccharide (LPS)-activated the RAW 264.7 macrophage cell line, among them E. pallida was the most active. The Echinacea-mediated decrease in NO production was unlikely due to a direct scavenging of NO because the extracts did not directly inhibit NO released from an NO donor, sodium nitroprusside. An immunoblotting assay demonstrated that the extract of E. pallida inhibited inducible nitric oxide synthase (iNOS) protein expression in LPS-treated macrophages. The enzymes iNOS and arginase metabolize a common substrate, L-arginine, but produce distinct biological effects. While iNOS is involved in inflammatory response and host defense, arginase participates actively in anti-inflammatory activation. Arginase activity of RAW 264.7 cells stimulated with 8- bromo-cAMP was significantly increased by alcohol extracts of all three Echinacea species. The polar fraction containing caffeic acid derivatives enhanced arginase activity, while the lipophilic fraction containing alkamides exhibited a potential of inhibiting NO production and iNOS expression. These results suggest that the anti-inflammatory activity of Echinacea might be due to multiple active metabolites, which work together to switch macrophage activation from classical activation towards alternative activation