@article{2887, abstract = {Root system growth and development is highly plastic and is influenced by the surrounding environment. Roots frequently grow in heterogeneous environments that include interactions from neighboring plants and physical impediments in the rhizosphere. To investigate how planting density and physical objects affect root system growth, we grew rice in a transparent gel system in close proximity with another plant or a physical object. Root systems were imaged and reconstructed in three dimensions. Root-root interaction strength was calculated using quantitative metrics that characterize the extent towhich the reconstructed root systems overlap each other. Surprisingly, we found the overlap of root systems of the same genotype was significantly higher than that of root systems of different genotypes. Root systems of the same genotype tended to grow toward each other but those of different genotypes appeared to avoid each other. Shoot separation experiments excluded the possibility of aerial interactions, suggesting root communication. Staggered plantings indicated that interactions likely occur at root tips in close proximity. Recognition of obstacles also occurred through root tips, but through physical contact in a size-dependent manner. These results indicate that root systems use two different forms of communication to recognize objects and alter root architecture: root-root recognition, possibly mediated through root exudates, and root-object recognition mediated by physical contact at the root tips. This finding suggests that root tips act as local sensors that integrate rhizosphere information into global root architectural changes.}, author = {Fang, Suqin and Clark, Randy and Zheng, Ying and Iyer Pascuzzi, Anjali and Weitz, Joshua and Kochian, Leon and Edelsbrunner, Herbert and Liao, Hong and Benfey, Philip}, journal = {PNAS}, number = {7}, pages = {2670 -- 2675}, publisher = {National Academy of Sciences}, title = {{Genotypic recognition and spatial responses by rice roots}}, doi = {10.1073/pnas.1222821110}, volume = {110}, year = {2013}, } @inproceedings{2901, abstract = { We introduce the M-modes problem for graphical models: predicting the M label configurations of highest probability that are at the same time local maxima of the probability landscape. M-modes have multiple possible applications: because they are intrinsically diverse, they provide a principled alternative to non-maximum suppression techniques for structured prediction, they can act as codebook vectors for quantizing the configuration space, or they can form component centers for mixture model approximation. We present two algorithms for solving the M-modes problem. The first algorithm solves the problem in polynomial time when the underlying graphical model is a simple chain. The second algorithm solves the problem for junction chains. In synthetic and real dataset, we demonstrate how M-modes can improve the performance of prediction. We also use the generated modes as a tool to understand the topography of the probability distribution of configurations, for example with relation to the training set size and amount of noise in the data. }, author = {Chen, Chao and Kolmogorov, Vladimir and Yan, Zhu and Metaxas, Dimitris and Lampert, Christoph}, location = {Scottsdale, AZ, United States}, pages = {161 -- 169}, publisher = {JMLR}, title = {{Computing the M most probable modes of a graphical model}}, volume = {31}, year = {2013}, } @article{2900, author = {Azevedo, Ricardo B and Lohaus, Rolf and Tiago Paixao}, journal = {Evolution & Development}, number = {5}, pages = {514 -- 515}, publisher = {Wiley-Blackwell}, title = {{Networking networks}}, volume = {10}, year = {2013}, } @inproceedings{2906, abstract = {Motivated by an application in cell biology, we describe an extension of the kinetic data structures framework from Delaunay triangulations to fixed-radius alpha complexes. Our algorithm is implemented using CGAL, following the exact geometric computation paradigm. We report on several techniques to accelerate the computation that turn our implementation applicable to the underlying biological problem.}, author = {Kerber, Michael and Edelsbrunner, Herbert}, booktitle = {2013 Proceedings of the 15th Workshop on Algorithm Engineering and Experiments}, location = {New Orleans, LA, United States}, pages = {70 -- 77}, publisher = {Society of Industrial and Applied Mathematics}, title = {{3D kinetic alpha complexes and their implementation}}, doi = {10.1137/1.9781611972931.6}, year = {2013}, } @article{2910, abstract = {Coalescent simulation has become an indispensable tool in population genetics and many complex evolutionary scenarios have been incorporated into the basic algorithm. Despite many years of intense interest in spatial structure, however, there are no available methods to simulate the ancestry of a sample of genes that occupy a spatial continuum. This is mainly due to the severe technical problems encountered by the classical model of isolation by distance. A recently introduced model solves these technical problems and provides a solid theoretical basis for the study of populations evolving in continuous space. We present a detailed algorithm to simulate the coalescent process in this model, and provide an efficient implementation of a generalised version of this algorithm as a freely available Python module.}, author = {Kelleher, Jerome and Barton, Nicholas H and Etheridge, Alison}, journal = {Bioinformatics}, number = {7}, pages = {955 -- 956}, publisher = {Oxford University Press}, title = {{Coalescent simulation in continuous space}}, doi = {10.1093/bioinformatics/btt067}, volume = {29}, year = {2013}, }