Antiplatelet Effect and Selective Binding to Cyclooxygenase (COX) by Molecular Docking Analysis of Flavonoids and Lignans

The known flavonoids ginkgetin (1), taiwanhomoflavone A (2), taiwanhomoflavone B (3), and taiwanhomoflavone C (4) and eight known lignans: justicidin B (9), justicidin C (10), justicidin D (11), chinensinaphthol methyl ether (12), procumphthalide A (13), procumbenoside A (15), and ciliatosides A (16) and B (17) were isolated from Cephalotaxus wilsoniana and Justicia species, respectively. The antiplatelet effects of the above constituents on human platelet-rich plasma (PRP) were evaluated. Of the compounds tested on human PRP, compounds 1, 4, 9, and 11 showed inhibition of secondary aggregation induced by adrenaline. Compound 1 had an inhibitory effect on cyclooxygenase-1 (COX-1). Molecular docking studies revealed that 1 and the related compounds apigenin (5), cycloheterophyllin (6), broussoflavone F (7), and quercetin (8) were docked near the gate of active site of COX-1. It indicated that the antiplatelet effect of 1, 4, 9, and 11 is partially owed to suppression of COX-1 activity and reduced thromboxane formation. Flavonoids, 1, 5, 6, 7, and 8 may block the gate of the active site of COX-1 and interfere the conversion of arachidonic acid to prostaglandin (PG) H₂ in the COX-1 active site.     

Application classification using packet size distribution and port association

Traffic classification is an essential part in common network management applications such as intrusion detection and network monitoring. Identifying traffic by looking at port numbers is only suitable to well-known applications, while signature-based classification is not applicable to encrypted messages. Our preliminary observation shows that each application has distinct packet size distribution (PSD) of the connections. Therefore, it is feasible to classify traffic by analyzing the variances of packet sizes of the connections without analyzing packet payload. In this work, each connection is first transformed into a point in a multi-dimensional space according to its PSD. Then it is compared with the representative points of pre-defined applications and recognized as the application having a minimum distance. Once a connection is identified as a specific application, port association is used to accelerate the classification by combining it with the other connections of the same session because applications usually use consecutive ports during a session. Using the proposed techniques, packet size distribution and port association, a high accuracy rate, 96% on average, and low false positive and false negative rates, 4–5%, are achieved. Our proposed method not only works well for encrypted traffic but also can be easily incorporated with a signature-based method to provide better accuracy.     

Reconfigurable Web wrapper agents

Web wrapper agents exploit online Web data sources, facilitating information integration and reuse. With the DeepSpot Agent Toolbox, users can automate virtually all types of Web browsing sessions simply by browsing the target Web sites.     

Discovering Robust Knowledge from Databases that Change

Many applications of knowledge discovery and data mining such as rule discovery for semantic query optimization, database integration and decision support, require the knowledge to be consistent with the data. However, databases usually change over time and make machine-discovered knowledge inconsistent. Useful knowledge should be robust against database changes so that it is unlikely to become inconsistent after database updates. This paper defines this notion of robustness in the context of relational databases and describes how robustness of first-order Horn-clause rules can be estimated. Experimental results show that our estimation approach can accurately identify robust rules. We also present a rule antecedent pruning algorithm that improves the robustness and applicability of machine discovered rules to demonstrate the usefulness of robustness estimation.     

Mining Skewed and Sparse Transaction Data for Personalized Shopping Recommendation

A good shopping recommender system can boost sales in a retailer store. To provide accurate recommendation, the recommender needs to accurately predict a customer's preference, an ability difficult to acquire. Conventional data mining techniques, such as association rule mining and collaborative filtering, can generally be applied to this problem, but rarely produce satisfying results due to the skewness and sparsity of transaction data. In this paper, we report the lessons that we learned in two real-world data mining applications for personalized shopping recommendation. We learned that extending a collaborative filtering method based on ratings (e.g., GroupLens) to perform personalized shopping recommendation is not trivial and that it is not appropriate to apply association-rule based methods (e.g., the IBM SmartPad system) for large scale prediction of customers' shopping preferences. Instead, a probabilistic graphical model can be more effective in handling skewed and sparse data. By casting collaborative filtering algorithms in a probabilistic framework, we derived HyPAM (Hybrid Poisson Aspect Modelling), a novel probabilistic graphical model for personalized shopping recommendation. Experimental results show that HyPAM outperforms GroupLens and the IBM method by generating much more accurate predictions of what items a customer will actually purchase in the unseen test data. The data sets and the results are made available for download at http://chunnan.iis.sinica.edu.tw/hypam/HyPAM.html.     

The ANNIGMA-wrapper approach to fast feature selection for neuralnets

This paper presents a novel feature selection approach for backpropagation neural networks (NNs). Previously, a feature selection technique known as the wrapper model was shown effective for decision trees induction. However, it is prohibitively expensive when applied to real-world neural net training characterized by large volumes of data and many feature choices. Our approach incorporates a weight analysis-based heuristic called artificial neural net input gain measurement approximation (ANNIGMA) to direct the search in the wrapper model and allows effective feature selection feasible for neural net applications. Experimental results on standard datasets show that this approach can efficiently reduce the number of features while maintaining or even improving the accuracy. We also report two successful applications of our approach in the helicopter maintenance applications.     

Micellar layer-by-layer synthesis of TiO2/Ag hybrid particles for bactericidal and photocatalytic activities

Silver nanoparticles prepared by a reverse micelle process were sequentially deposited on rutile-structured TiO₂ particles via an electrostatic layer-by-layer (LbL) deposition together with a hydrophilic/hydrophobic interaction. The TiO₂ surface was first mediated by a preferential adsorption of poly(allylamine hydrochloride) (PAH) cationic molecules, before being mixed with the Ag nanoparticles encapsulated in reverse micelles consisting of anionic surfactant of sodium bis(2-ethylhexyl) sulfosuccinate (AOT) in isooctane. The deposition of Ag nanoparticles was not of a uniform coverage on the TiO₂ surface, but of a heterogeneous growth of the Ag particles on the TiO₂ surface. Antibacterial activity of the composites against gram-negative bacteria, i.e., Escherichia coli (E. coli), was found to increase with the deposition cycle, resulted mainly from the increased Ag concentration. The bactericidity is persistent in the absence of ultraviolet (UV) light. Over the concentration range of Ag examined, i.e., Ag/Ti atomic ratio varies from 0.28% to 0.53%, photocatalytic efficiency of the composites against methylene blue (MB) dye in an aqueous solution also improved pronouncedly with the silver concentration under UV exposure.     

Grouped signed power-of-two algorithms for low-complexity adaptive equalization

With increasing demand for higher data rate, modern communication systems have grown more complex. Equalization has become more and more important as it is effective in mitigating the multipath fading often occurred in high-data-rate communication systems. However, the implementation complexity of adaptive equalizers is usually too high for mobile communication applications. In this paper, a novel adaptive equalization algorithm and its low-complexity architecture are proposed. This algorithm employs a new grouped signed power-of-two (GSPT) number representation. The GSPT algorithm and several enhanced versions are simulated as adaptive equalizers in a phase-shift keying communication receiver for several practical channels and the GSPT-based equalizers perform as well as the least mean square (LMS) equalizer. Moreover, for comparison, two GSPT-based equalizers and two other equalizers are implemented in field-programmable gate arrays. The GSPT-based equalizers require only about 25%-30% of the hardware resources needed in the LMS equalizer. Also the GSPT-based equalizers are more than twice as fast as the LMS equalizer.     

A Low-Latency Multi-layer Prefix Grouping Technique for Parallel Huffman Decoding of Multimedia Standards

Huffman coding is a popular and important lossless compression scheme for various multimedia applications. This paper presents a low-latency parallel Huffman decoding technique with efficient memory usage for multimedia standards. First, the multi-layer prefix grouping technique is proposed for sub-group partition. It exploits the prefix characteristic in Huffman codewords to solve the problem of table size explosion. Second, a two-level table lookup approach is introduced which can promptly branch to the correct sub-group by level-1 table lookup and decode the symbols by level-2 table lookup. Third, two optimization approaches are developed; one is to reduce the branch cycles and the other is parallel processing between two-level table lookup and direct table lookup approaches to fully utilize the advantage of VLIW parallel processing. An AAC Huffman decoding example is realized on the Parallel Architecture Core DSP (PAC DSP) processor. The simulation results show that the proposed method can further improve about 89% of decoding cycles and 33% of table size comparing to the linear search method.