Additive noise removal using a novel fuzzy-based filter

The paper proposes a new fuzzy-based two-step filter for restoring images corrupted with additive noise. The goal of the first step is to compute the difference between the central pixel and its neighborhood in a selected window and to compute a fuzzy membership degree for each difference value using a Gaussian membership function. Computed fuzzy membership values are appropriately utilized as weights for each pixel and then computes the weighted average representing the modified value for the current central pixel. The second step is used as an augmented step to the first one and its goal is to improve the result obtained in the first step by reducing the noise in the color component differences without destroying the fine details of the image. The experimental analysis shows that the proposed method gives better results compared to existing advanced filters for additive noise reduction. Both visual, quantitative and qualitative analysis have been done to prove the efficiency and effectiveness of the proposed method.     

Method for prediction of protein-protein interactions in yeast using genomics/proteomics information and feature selection

Protein-protein interaction (PPI) prediction is one of the main goals in the current Proteomics. This work presents a method for prediction of protein-protein interactions through a classification technique known as support vector machines. The dataset considered is a set of positive and negative examples taken from a high reliability source, from which we extracted a set of genomic features, proposing a similarity measure. From this dataset we extracted 26 proteomics/genomics features using well-known databases and datasets. Feature selection was performed to obtain the most relevant variables through a modified method derived from other feature selection methods for classification. Using the selected subset of features, we constructed a support vector classifier that obtains values of specificity and sensitivity higher than 90% in prediction of PPIs, and also providing a confidence score in interaction prediction of each pair of proteins.     

Fostering continuous innovation in design with an integrated knowledge management approach

In the global competition, companies are propelled by an immense pressure to innovate. The trend to produce more new knowledge-intensive products or services and the rapid progress of information technologies arouse huge interest on knowledge management for innovation. However the strategy of knowledge management is not widely adopted for innovation in industries due to a lack of an effective approach of their integration. This study aims to help the designers to innovate more efficiently based on an integrated approach of knowledge management. Based on this integrated approach, a prototype of distributed knowledge management system for innovation is developed. An industrial application is presented and its initial results indicate the applicability of the approach and the prototype in practice.     

Detection of EGFR on living human gastric cancer BGC823 cells using surface plasmon resonance phase sensing

Label-free and real-time information acquisition of molecular phenotype and its function on living cells plays a significant role in disease diagnosis and drug development. In this paper, SPR phase sensing was applied to monitor the interactions between EGFR antibody, EGFR1, and membrane proteins EGFR on living human gastric cancer BGC823 cells. When 50 μg/mL EGFR1 was added onto the fixed cells chip and the living cells chip, a significant difference in the binding amount could be observed from the immunofluorescence images. Quantitative results were obtained by following SPR detection, which were 722 RU and 438 RU, respectively. On the same living cells chip, SPR detection also showed markedly different results of cellular responses when it was stimulated by EGFR1 at different concentrations, such as adhesion and/or morphology variation, revealing the EGFR1's cytotoxic effect on the BGC823 cells. The results demonstrate SPR phase sensing is capable of real-time detection of molecular interactions and cellular responses on living cells, and suggest that further studies on the mechanism and the technique may allow SPR sensing become a powerful tool not only for the basic research of cell biology, but also for medical diagnosis and drug development.