The nanostructures and hydrophobic properties of cancer cell membranes are important for membrane fusion and cell adhesion. They are directly related to cancer cell biophysical properties, including aggressive growth and migration. Additionally, chemical component analysis of the cancer cell membrane could potentially be applied in clinical diagnosis of cancer by identification of specific biomarker receptors expressed on cancer cell surfaces. In the present work, a combined Raman microspectroscopy (RM) and atomic force microscopy (AFM) technique was applied to detect the difference in membrane chemical components and nanomechanics of three cancer cell lines: human lung adenocarcinoma epithelial cells (A549), and human breast cancer cells (MDA-MB-435 with and without BRMS1 metastasis suppressor). Raman spectral analysis indicated similar bands between the A549, 435 and 435/BRMS1 including ~720 cm(-1) (guanine band of DNA), 940 cm(-1) (skeletal mode polysaccharide), 1006 cm(-1) (symmetric ring breathing phenylalanine), and 1451 cm(-1) (CH deformation). The membrane surface adhesion forces for these cancer cells were measured by AFM in culture medium: 0.478 ± 0.091 nN for A549 cells, 0.253 ± 0.070 nN for 435 cells, and 1.114 ± 0.281 nN for 435/BRMS1 cells, and the cell spring constant was measured at 2.62 ± 0.682 mN m(-1) for A549 cells, 2.105 ± 0.691 mN m(-1) for 435 cells, and 5.448 ± 1.081 mN m(-1) for 435/BRMS1 cells.

We propose a novel weighted semantic manifold ranking system for content-based image retrieval. This manifold builds a more accurate intrinsic structure for the proper image space by combining visual and semantic relevance relations. Specifically, we apply the learning mechanism to capture users' semantic concepts in clusters and extract high-level semantic features for each database image. We then incorporate the reliability score, the fuzzy membership, and the composite low-level and high-level relation into the traditional affinity matrix to construct a weighted semantic manifold structure. We finally create an asymmetric relevance vector to propagate positive and negative labels via the proposed manifold structure to images with high similarities. Extensive experiments demonstrate our system outperforms other manifold systems and learning systems in the context of both correct and erroneous feedback.

This paper proposes a novel content-based image retrieval technique, which facilitates short-term (intra-query) and long-term (inter-query) learning processes by integrating accumulated users' historical relevance feedback-based semantic knowledge. The history is efficiently represented as a dynamic semantic feature of the images. As such, the high-level semantic similarity measure can be dynamically adapted based on the semantic relevance derived from the dynamic semantic features. The short-term relevance feedback technique can benefit from long-term learning. Our extensive experiments show that the proposed system outperforms three peer systems in the context of both correct and erroneous relevance feedback.

This paper presents an efficient content-based image retrieval system that captures users' semantic concepts in clusters. These semantically homogeneous clusters aid in the retrieval system to accurately measure the semantic similarity among images and therefore reduce the semantic gap. They also aid in the retrieval system to find matched images in a few candidate clusters and therefore reduce the search space. The extensive experiments demonstrate that the proposed retrieval system outperforms the peer systems to quickly retrieve the desired images in a few iterations.