Interactive Vector Field Feature Identification

We introduce a flexible technique for interactive exploration of vector field data through classification derived from user-specified feature templates. Our method is founded on the observation that, while similar features within the vector field may be spatially disparate, they share similar neighborhood characteristics. Users generate feature-based visualizations by interactively highlighting well-accepted and domain specific representative feature points. Feature exploration begins with the computation of attributes that describe the neighborhood of each sample within the input vector field. Compilation of these attributes forms a representation of the vector field samples in the attribute space. We project the attribute points onto the canonical 2D plane to enable interactive exploration of the vector field using a painting interface. The projection encodes the similarities between vector field points within the distances computed between their associated attribute points. The proposed method is performed at interactive rates for enhanced user experience and is completely flexible as showcased by the simultaneous identification of diverse feature types.

Structure-Based Approach for the Study of Estrogen Receptor Binding Affinity and Subtype Selectivity

Estrogens exert important physiological effects through the modulation of two human estrogen receptor (hER) subtypes, alpa (hER alpha) and beta (hER beta). Because the levels and relative proportion of hER alpha and hER beta differ significantly in different target cells, selective hER ligands could target specific tissues or pathways regulated by one receptor subtype without affecting the other. To understand the structural and chemical basis by which small molecule modulators are able to discriminate between the two subtypes, we have applied three-dimensional target-based approaches employing a series of potent hER-ligands. Comparative molecular field analysis (CoMFA) studies were applied to a data set of 81 hER modulators, for which binding affinity values were collected for both hER alpha and hER beta. Significant statistical coefficients were obtained (hER alpha, q(2) = 0.76; hER beta, q(2) = 0.70), indicating the internal consistency of the models. The generated models were validated using external test sets, and the predicted values were in good agreement with the experimental results. Five hER crystal structures were used in GRID/PCA investigations to generate molecular interaction fields (MIF) maps. hER alpha and hER beta were separated using one factor. The resulting 3D information was integrated with the aim of revealing the most relevant structural features involved in hER subtype selectivity. The final QSAR and GRID/PCA models and the information gathered from 3D contour maps should be useful for the design or novel hER modulators with improved selectivity.

Modularity and robustness of bone networks

Cortical bones, essential for mechanical support and structure in many animals, involve a large number of canals organized in intricate fashion. By using state-of-the art image analysis and computer graphics, the 3D reconstruction of a whole bone (phalange) of a young chicken was obtained and represented in terms of a complex network where each canal was associated to an edge and every confluence of three or more canals yielded a respective node. The representation of the bone canal structure as a complex network has allowed several methods to be applied in order to characterize and analyze the canal system organization and the robustness. First, the distribution of the node degrees (i.e. the number of canals connected to each node) confirmed previous indications that bone canal networks follow a power law, and therefore present some highly connected nodes (hubs). The bone network was also found to be partitioned into communities or modules, i.e. groups of nodes which are more intensely connected to one another than with the rest of the network. We verified that each community exhibited distinct topological properties that are possibly linked with their specific function. In order to better understand the organization of the bone network, its resilience to two types of failures (random attack and cascaded failures) was also quantified comparatively to randomized and regular counterparts. The results indicate that the modular structure improves the robustness of the bone network when compared to a regular network with the same average degree and number of nodes. The effects of disease processes (e. g., osteoporosis) and mutations in genes (e.g., BMP4) that occur at the molecular level can now be investigated at the mesoscopic level by using network based approaches.