A New Dimension of Knowledge Visualization for Reconstructing Thinking Process

Knowledge visualization (KV) is an emerging field, which is firstly disciplinary proposed by Eppler in 2004. The main concern of KV research is not the mere convey of facts, but the transfer of people's insights, experiences, attitudes, values, expectations, perspectives, opinions and predictions, and enables someone else to re-construct, remember and apply these insights correctly. In the collaborative problem-solving process, to achieve smooth and efficient communication among the participants, knowing how others arrive at the conclusion is even more important than the conclusion itself. Several related KV methods of helping express people's thinking are introduced in this paper, and based on the discussion of these methods' limitations, a TPO (Thinking Process Organization) framework for reconstructing people's thinking process is proposed from a new dimension.The original publication is available at JAIST Press http://www.jaist.ac.jp/library/jaist-press/index.htmlProceedings of KSS'2007 : The Eighth International Symposium on Knowledge and Systems Sciences : November 5-7, 2007, [Ishikawa High-Tech Conference Center, Nomi, Ishikawa, JAPAN]Organized by: Japan Advanced Institute of Science and Technolog

A Photoactivated Sorafenib-Ruthenium(II) Prodrug for Resistant Hepatocellular Carcinoma Therapy through Ferroptosis and Purine Metabolism Disruption

The curative effect of sorafenib in hepatocellular carcinoma (HCC) is limited and sorafenib resistance remains a major obstacle for HCC. To overcome this obstacle, a new photoactive sorafenib-Ru(II) complex Ru-Sora has been designed. Upon irradiation (λ = 465 nm), Ru-Sora rapidly releases sorafenib and generates reactive oxygen species, which can oxidize intracellular substances such as GSH. Cellular experiments show that irradiated Ru-Sora is highly cytotoxic toward Hep-G2 cells, including sorafenib-resistant Hep-G2-SR cells. Compared to sorafenib, Ru-Sora has a significant photoactivated chemotherapeutic effect against Hep-G2-SR cancer cells and 3D Hep-G2 multicellular tumor spheroids. Furthermore, Ru-Sora inducing apoptosis and ferroptosis is proved by GSH depletion, GPX4 downregulation, and lipid peroxide accumulation. Metabolomics results suggest that Ru-Sora exerts photocytotoxicity by disrupting the purine metabolism, which is expected to inhibit tumor development. This study provides a promising strategy for enhancing chemotherapy and combating drug-resistant HCC disease

A Photoactivated Sorafenib-Ruthenium(II) Prodrug for Resistant Hepatocellular Carcinoma Therapy through Ferroptosis and Purine Metabolism Disruption

The curative effect of sorafenib in hepatocellular carcinoma (HCC) is limited and sorafenib resistance remains a major obstacle for HCC. To overcome this obstacle, a new photoactive sorafenib-Ru(II) complex Ru-Sora has been designed. Upon irradiation (λ = 465 nm), Ru-Sora rapidly releases sorafenib and generates reactive oxygen species, which can oxidize intracellular substances such as GSH. Cellular experiments show that irradiated Ru-Sora is highly cytotoxic toward Hep-G2 cells, including sorafenib-resistant Hep-G2-SR cells. Compared to sorafenib, Ru-Sora has a significant photoactivated chemotherapeutic effect against Hep-G2-SR cancer cells and 3D Hep-G2 multicellular tumor spheroids. Furthermore, Ru-Sora inducing apoptosis and ferroptosis is proved by GSH depletion, GPX4 downregulation, and lipid peroxide accumulation. Metabolomics results suggest that Ru-Sora exerts photocytotoxicity by disrupting the purine metabolism, which is expected to inhibit tumor development. This study provides a promising strategy for enhancing chemotherapy and combating drug-resistant HCC disease

Fast Initial Model Design for Electrical Resistivity Inversion by Using Broad Learning Framework

The electrical resistivity method is widely used in near-surface mineral exploration. At present, the deterministic algorithm is commonly employed in three-dimensional (3-D) electrical resistivity inversion to obtain subsurface electrical structures. However, the accuracy and efficiency of deterministic inversion rely on the initial model. In practice, obtaining an initial model that approximates the true subsurface electrical structures remains challenging. To address this issue, we introduce a broad learning (BL) network to determine the initial model and utilize the limited memory quasi-Newton (L-BFGS) algorithm to conduct the 3-D electrical resistivity inversion task. The powerful mapping capability of the BL network enables one to find the model that elucidates the actual observed data. The single-layer BL network makes it efficient and easy to realize, leading to much faster network training compared to that using the deep learning network. Both the synthetic and field experiments suggest that the BL framework could effectively obtain the initial model based on observed data. Furthermore, in comparison to using a homogeneous medium as the initial model, the L-BFGS inversion with the BL framework-designed initial model improves the inversion accuracy of subsurface electrical structures and expedites the convergence speed of the iteration. This study provides an effective approach for fast initial model design in a data-driven manner when the prior information is unavailable. The proposed method can be useful in high-precision imaging of near-surface mineral electrical structures