Genetic algorithm based approach to optimize phenotypical traits of virtual rice.

How to select and combine good traits of rice to get high-production individuals is one of the key points in developing crop ideotype cultivation technologies. Existing cultivation methods for producing ideal plants, such as field trials and crop modeling, have some limits. In this paper, we propose a method based on a genetic algorithm (GA) and a functional-structural plant model (FSPM) to optimize plant types of virtual rice by dynamically adjusting phenotypical traits. In this algorithm, phenotypical traits such as leaf angles, plant heights, the maximum number of tiller, and the angle of tiller are considered as input parameters of our virtual rice model. We evaluate the photosynthetic output as a function of these parameters, and optimized them using a GA. This method has been implemented on GroIMP using the modeling language XL (eXtended L-System) and RGG (Relational Growth Grammar). A double haploid population of rice is adopted as test material in a case study. Our experimental results show that our method can not only optimize the parameters of rice plant type and increase the amount of light absorption, but can also significantly increase crop yield.

A calculation method of plant similarity giving consideration to different plant features.

A method to compute the similarity between different plants is proposed, using features of a plant׳s topological structure and peripheral contour, as well as its geometry. The topological structures are described using tree graphs, and their similarity can be calculated based on the edit distance of these graphs. The peripheral contour of a plant is abstracted by its three-dimensional convex hull, which is projected in several directions. The similarity of the different projections is calculated by an algorithm to compute the similarity of two-dimensional shapes. The similarity of the geometrical detail is computed by considering the geometrical properties of different level branches. Finally the overall similarity between different plants is calculated by combining these different similarity measures. The validity of proposed method is evaluated by detailed experiments.

Structured Light-Based 3D Reconstruction System for Plants.

Camera-based 3D reconstruction of physical objects is one of the most popular computer vision trends in recent years. Many systems have been built to model different real-world subjects, but there is lack of a completely robust system for plants. This paper presents a full 3D reconstruction system that incorporates both hardware structures (including the proposed structured light system to enhance textures on object surfaces) and software algorithms (including the proposed 3D point cloud registration and plant feature measurement). This paper demonstrates the ability to produce 3D models of whole plants created from multiple pairs of stereo images taken at different viewing angles, without the need to destructively cut away any parts of a plant. The ability to accurately predict phenotyping features, such as the number of leaves, plant height, leaf size and internode distances, is also demonstrated. Experimental results show that, for plants having a range of leaf sizes and a distance between leaves appropriate for the hardware design, the algorithms successfully predict phenotyping features in the target crops, with a recall of 0.97 and a precision of 0.89 for leaf detection and less than a 13-mm error for plant size, leaf size and internode distance.

Creating supersecondary structures with BuildBeta.

BuildBeta is a feature of the ProteinShop software designed to thoroughly sample a protein conformational space given the protein's sequence of amino acids and secondary structure predictions. It targets proteins with beta sheets because they are particularly challenging to predict due to the complexity of sampling long-range strand pairings. Here we discuss some of the most difficult targets in the recent 9th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP9) and show how BuildBeta can leverage some of the most successful methods in the category "template-free modeling" by augmenting their sampling capabilities. We also discuss ongoing efforts to improve the quality of the supersecondary structures it generates.

PackHelix: a tool for helix-sheet packing during protein structure prediction.

The three-dimensional structure of a protein is organized around the packing of its secondary structure elements. Predicting the topology and constructing the geometry of structural motifs involving α-helices and/or ß-strands are therefore key steps for accurate prediction of protein structure. While many efforts have focused on how to pack helices and on how to sample exhaustively the topologies and geometries of multiple strands forming a ß-sheet in a protein, there has been little progress on generating native-like packings of helices on sheets. We describe a method that can generate the packing of multiple helices on a given ß-sheet for αßα sandwich type protein folds. This method mines the results of a statistical analysis of the conformations of αß(2) motifs in protein structures to provide input values for the geometric attributes of the packing of a helix on a sheet. It then proceeds with a geometric builder that generates multiple arrangements of the helices on the sheet of interest by sampling through these values and performing consistency checks that guarantee proper loop geometry between the helices and the strands, minimal number of collisions between the helices, and proper formation of a hydrophobic core. The method is implemented as a module of ProteinShop. Our results show that it produces structures that are within 4-6 Å RMSD of the native one, regardless of the number of helices that need to be packed, though this number may increase if the protein has several helices between two consecutive strands in the sequence that pack on the sheet formed by these two strands.

Subdivision analysis of the trilinear interpolant.

Isosurfaces are fundamental volumetric visualization tools and are generated by approximating contours of trilinearly interpolated scalar fields. While a complete set of cases has recently been published by Nielson, the formal proof that these cases are the only ones possible and that they are topologically correct is difficult to follow. We present a more straightforward proof of the correctness and completeness of these cases based on a variation of the Dividing Cubes algorithm. Since this proof is based on topological arguments and a divide-and-conquer approach, this also sets the stage for developing tessellation cases for higher order interpolants and the quadrilinear interpolant in four dimensions. We also demonstrate that apart from degenerate cases, Nielson's cases are, in fact, subsets of two basic configurations of the trilinear interpolant.

BuildBeta--a system for automatically constructing beta sheets.

We describe a method that can thoroughly sample a protein conformational space given the protein primary sequence of amino acids and secondary structure predictions. Specifically, we target proteins with beta-sheets because they are particularly challenging for ab initio protein structure prediction because of the complexity of sampling long-range strand pairings. Using some basic packing principles, inverse kinematics (IK), and beta-pairing scores, this method creates all possible beta-sheet arrangements including those that have the correct packing of beta-strands. It uses the IK algorithms of ProteinShop to move alpha-helices and beta-strands as rigid bodies by rotating the dihedral angles in the coil regions. Our results show that our approach produces structures that are within 4-6 A RMSD of the native one regardless of the protein size and beta-sheet topology although this number may increase if the protein has long loops or complex alpha-helical regions.

ProteinShop: a tool for interactive protein manipulation and steering.

We describe ProteinShop, a new visualization tool that streamlines and simplifies the process of determining optimal protein folds. ProteinShop may be used at different stages of a protein structure prediction process. First, it can create protein configurations containing secondary structures specified by the user. Second, it can interactively manipulate protein fragments to achieve desired folds by adjusting the dihedral angles of selected coil regions using an Inverse Kinematics method. Last, it serves as a visual framework to monitor and steer a protein structure prediction process that may be running on a remote machine. ProteinShop was used to create initial configurations for a protein structure prediction method developed by a team that competed in CASP5. ProteinShop's use accelerated the process of generating initial configurations, reducing the time required from days to hours. This paper describes the structure of ProteinShop and discusses its main features.

Image-space visibility ordering for cell projection volume rendering of unstructured data.

Projection methods for volume rendering unstructured data work by projecting, in visibility order, the polyhedral cells of the mesh onto the image plane, and incrementally compositing each cell's color and opacity into the final image. Normally, such methods require an algorithm to determine a visibility order of the cells. The Meshed Polyhedra Visibility Order (MPVO) algorithm can provide such an order for convex meshes by considering the implications of local ordering relations between cells sharing a common face. However, in nonconvex meshes, one must also consider ordering relations along viewing rays which cross empty space between cells. In order to include these relations, the algorithm described in this paper, the scanning exact meshed polyhedra visibility ordering (SXMPVO) algorithm, scan-converts the exterior faces of the mesh and saves the ray-face intersections in an A-Buffer data structure which is then used for retrieving the extra ordering relations. The image which SXMPVO produces is the same as would be produced by ordering the cells exactly, even though SXMPVO does not compute an exact visibility ordering. This is because the image resolution used for computing the visibility ordering relations is the same as that which is used for the actual volume rendering and we choose our A-Buffer rays at the same sample points that are used to establish a polygon's pixel coverage during hardware scan conversion. Thus, the algorithm is image-space correct. The SXMPVO algorithm has several desirable features; among them are speed, simplicity of implementation, and no extra (i.e., with respect to MPVO) preprocessing.

Hierarchical molecular modelling with ellipsoids.

Protein and DNA structures are represented at varying levels of details using ellipsoidal RGBA textured splats. The splat texture at each level is generated by rendering its children in a hierarchical model, from a distribution of viewing directions, and averaging the result. For rendering, the ellipsoids to be used are chosen adaptively, depending on the distance to the viewpoint. This technique is applied to visualize DNA coiling around nucleosomes in chromosomes.