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MOTIVATION Proteins exhibit complex subcellular distributions, which may include localizing in more than one organelle and varying in location depending on the cell physiology. Estimating the amount of protein distributed in each subcellular location is essential for quantitative understanding and modeling of protein dynamics and how they affect cell(More)
Grain weight, an essential yield component, is under strong genetic control and markedly influenced by the environment. Here, by genome-wide association analysis with a panel of 94 elite common wheat varieties, 37 loci were found significantly associated with thousand-grain weight (TGW) in one or more environments differing in water and fertiliser levels.(More)
Many proteins or other biological macromolecules are localized to more than one subcellular structure. The fraction of a protein in different cellular compartments is often measured by colocalization with organelle-specific fluorescent markers, requiring availability of fluorescent probes for each compartment and acquisition of images for each in(More)
We describe a new approach for elucidating the nonlinear degrees of freedom in a distribution of shapes depicted in digital images. By combining a deformation-based method for measuring distances between two shape configurations together with multidimensional scaling, a method for determining the number of degrees of freedom in a shape distribution is(More)
Given the importance of subcellular location to protein function, computational simulations of cell behaviors will ultimately require the ability to model the distributions of proteins within organelles and other structures. Toward this end, statistical learning methods have previously been used to build models of sets of two-dimensional microscope images,(More)
Biological shape modeling is an essential task that is required for systems biology efforts to simulate complex cell behaviors. Statistical learning methods have been used to build generative shape models based on reconstructive shape parameters extracted from microscope image collections. However, such parametric modeling approaches are usually limited to(More)
Algorithms for on-line monitoring of micro-spheres in an optical tweezers-based assembly cell. Abstract Optical tweezers have emerged as a powerful tool for micro and nanomanipulation. Using optical tweezers to perform automated assembly requires on-line monitoring of components in the assembly workspace. This paper presents algorithms for estimating(More)
This paper describes a computational framework for constructing point clouds using digital projection patterns. The basic principle behind the approach is to project known patterns on the object using a digital projector. A digital camera is then used to take images of the object with the known projection patterns imposed on it. Due to the presence of 3-D(More)
This paper describes algorithms for generating 3-D point clouds from a set of digital images obtained from projecting phase-shifted sinusoidal fringe patterns onto object. In this paper, a mathematical model is introduced for describing the geometric relationship between the fringe patterns being projected, the image captured and the shape of the object(More)
We propose a novel method for detecting characteristic informative phenotype patterns from biomedical images. By building a metric space quantifying the difference between images, we learn the distributions of different classes, and then detect the characteristic regions using graph partition. We show that the detected regions are statistically significant.(More)