Osteopontin expression correlates with melanoma invasion

Melanoma is one of the most aggressive cancers affecting humans. Although early melanomas are curable with surgical excision, metastatic melanomas are associated with high mortality. The mechanism of melanoma development, progression, and metastasis is largely unknown. In order to uncover genes unique to melanoma cells, we used high-density DNA microarrays to examine the gene expression profiles of metastatic melanoma nodules using benign nevi as controls. Over 190 genes were significantly overexpressed in metastatic melanomas compared with normal nevi by at least 2-fold. One of the most abundantly expressed genes in metastatic melanoma nodules is osteopontin (OPN). Immunohistochemistry staining on tissue microarrays and individual skin biopsies representing different stages of melanoma progression revealed that OPN expression is first acquired at the step of melanoma tissue invasion. In addition, blocking of OPN expression by RNA interference reduced melanoma cell numbers in vitro. Our observations suggest that OPN may be acquired early in melanoma development and progression, and may enhance tumor cell growth in invasive melanoma.

A Two-stage information filtering based on rough decision rule and pattern mining

Information Overload and Mismatch are two fundamental problems affecting the effectiveness of information filtering systems. Even though both term-based and patternbased approaches have been proposed to address the problems of overload and mismatch, neither of these approaches alone can provide a satisfactory solution to address these problems. This paper presents a novel two-stage information filtering model which combines the merits of term-based and pattern-based approaches to effectively filter sheer volume of information. In particular, the first filtering stage is supported by a novel rough analysis model which efficiently removes a large number of irrelevant documents, thereby addressing the overload problem. The second filtering stage is empowered by a semantically rich pattern taxonomy mining model which effectively fetches incoming documents according to the specific information needs of a user, thereby addressing the mismatch problem. The experimental results based on the RCV1 corpus show that the proposed twostage filtering model significantly outperforms the both termbased and pattern-based information filtering models.

A participant’s response : recreating the circle with We Al-Li: a program for sharing and regeneration

Personal reflections on the We Al-Li Program

Rough sets based reasoning and pattern mining for a two-stage information filtering system

This paper presents a novel two-stage information filtering model which combines the merits of term-based and pattern- based approaches to effectively filter sheer volume of information. In particular, the first filtering stage is supported by a novel rough analysis model which efficiently removes a large number of irrelevant documents, thereby addressing the overload problem. The second filtering stage is empowered by a semantically rich pattern taxonomy mining model which effectively fetches incoming documents according to the specific information needs of a user, thereby addressing the mismatch problem. The experiments have been conducted to compare the proposed two-stage filtering (T-SM) model with other possible "term-based + pattern-based" or "term-based + term-based" IF models. The results based on the RCV1 corpus show that the T-SM model significantly outperforms other types of "two-stage" IF models.

Approximate Membership Localization within a Web-Based Join Framework

In this paper, we propose a search-based approach to join two tables in the absence of clean join attributes. Non-structured documents from the web are used to express the correlations between a given query and a reference list. To implement this approach, a major challenge we meet is how to efficiently determine the number of times and the locations of each clean reference from the reference list that is approximately mentioned in the retrieved documents. We formalize the Approximate Membership Localization (AML) problem and propose an efficient partial pruning algorithm to solve it. A study using real-word data sets demonstrates the effectiveness of our search-based approach, and the efficiency of our AML algorithm.

A two-stage decision model for information filtering

Information mismatch and overload are two fundamental issues influencing the effectiveness of information filtering systems. Even though both term-based and pattern-based approaches have been proposed to address the issues, neither of these approaches alone can provide a satisfactory decision for determining the relevant information. This paper presents a novel two-stage decision model for solving the issues. The first stage is a novel rough analysis model to address the overload problem. The second stage is a pattern taxonomy mining model to address the mismatch problem. The experimental results on RCV1 and TREC filtering topics show that the proposed model significantly outperforms the state-of-the-art filtering systems.

The cementogenic differentiation of periodontal ligament cells via the activation of Wnt/β-catenin signalling pathway by Li+ ions released from bioactive scaffolds

Lithium (Li) has been widely used as a long-term mood stabilizer in the treatment of bipolar and depressive disorders. Li+ ions are thought to enhance the remyelination of peripheral nerves and also stimulate the proliferation of neural progenitor cells and retinoblastoma cells via activation of the Wnt/β-catenin signalling pathway. Until now there have been no studies reporting the biological effects of released Li+ in bioactive scaffolds on cemetogenesis in periodontal tissue engineering applications. In this study, we incorporated parts of Li+ ions into the mesoporous bioactive glass (MBG) scaffolds and showed that this approach yielded scaffolds with a favourable composition, microstructure and mesopore properties for cell attachment, proliferation, and cementogenic differentiation of human periodontal ligament-derived cells (hPDLCs). We went on to investigate the biological effects of Li+ ions themselves on cell proliferation and cementogenic differentiation. The results showed that 5% Li+ ions incorporated into MBG scaffolds enhanced the proliferation and cementogenic differentiation of hPDLCs on scaffolds, most likely via activation of Wnt/β-catenin signalling pathway. Further study demonstrated that Li+ ions by themselves significantly enhanced the proliferation, differentiation and cementogenic gene expression of PDLCs. Our results indicate that incorporation of Li+ ions into bioactive scaffolds is a viable means of enhancing the Wnt canonical signalling pathway to stimulate cementogenic differentiation of PDLCs.

A high sensitive fiber Bragg grating strain sensor with automatic temperature compensation

A high sensitive fiber Bragg grating (FBG) strain sensor with automatic temperature compensation is demonstrated. FBG is axially linked with a stick and their free ends are fixed to the measured object. When the measured strain changes, the stick does not change in length, but the FBG does. When the temperature changes, the stick changes in length to pull the FBG to realize temperature compensation. In experiments, 1.45 times strain sensitivity of bare FBG with temperature compensation of less than 0.1 nm Bragg wavelength drift over 100 ◦C shift is achieved.