Modeling vegetative vigour in grapevine: unraveling underlying mechanisms

Mechanistic modeling constitutes a powerful tool to unravel complex biological phenomena. This study describes the construction of a mechanistic, dynamic model for grapevine plant growth and canopy biomass (vigor). To parametrize and validate the model, the progeny from a cross of Ramsey (Vitis champinii) × Riparia Gloire (V. riparia) was evaluated. Plants with different vigor were grown in a greenhouse during the summer of 2014 and 2015. One set of plants was grafted with Cabernet Sauvignon. Shoot growth rate (b), leaf area (LA), dry biomass, whole plant and root specific hydraulic conductance (kH and Lpr), stomatal conductance (gs), and water potential (Ψ) were measured. Partitioning indices and specific leaf area (SLA) were calculated. The model includes an empirical fit of a purported seasonal pattern of bioactive GAs based on published seasonal evolutionary levels and reference values. The model provided a good fit of the experimental data, with R = 0.85. Simulation of single trait variations defined the individual effect of each variable on vigor determination. The model predicts, with acceptable accuracy, the vigor of a young plant through the measurement of Lpr and SLA. The model also permits further understanding of the functional traits that govern vigor, and, ultimately, could be considered useful for growers, breeders and those studying climate change.EEA MendozaFil: Hugalde, Ines Pilar. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Mendoza; Argentina. University of California at Davis. Department of Viticulture and Enology; Estados UnidosFil: Agüero, Cecilia B. University of California at Davis. Department of Viticulture and Enology; Estados UnidosFil: Barrios-Masias, Felipe H. University of California at Davis. Department of Viticulture and Enology; Estados Unidos. University of Nevada. Department of Agriculture, Veterinary and Rangeland Sciences; Estados UnidosFil: Romero, Nina. University of California at Davis. Department of Viticulture and Enology; Estados UnidosFil: Nguyen, Andy Viet. University of California at Davis. Department of Viticulture and Enology; Estados UnidosFil: Riaz, Summaira. University of California at Davis. Department of Viticulture and Enology; Estados UnidosFil: Piccoli, Patricia Noemí. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Biología Agrícola de Mendoza. Universidad Nacional de Cuyo. Facultad de Ciencias Agrarias. Instituto de Biología Agrícola de Mendoza; ArgentinaFil: McElrone, Andrew J. University of California at Davis. Department of Viticulture and Enology; Estados Unidos. US Department of Agriculture. ARS; Estados UnidosFil: Walker, M. Andrew. University of California at Davis. Department of Viticulture and Enology; Estados UnidosFil: Vila, Hernan Felix. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Mendoza; Argentin

A Graphical Method of Computing Offset Curves

Computation of offset curves is an operation critical to many computer-aided design and manufacturing (CAD/CAM) applications. Though simple on the surface, differences between the straightforward mathematical definition and the demands of CAD/CAM environment in the formulation and expression of an offset curve create a problem for which only complicated, approximate solutions are presently available. This thesis explores one of the newest methods of offset curve computation, using graphics hardware to directly compute the offset curve for arbitrary input geometry. Linear segments of the input curve are represented as meshes in 3D space, and the rendering process is used to create a field of depth values from which the offset curve is extracted as an isoline. This results in significant performance enhancements over previous, similar methods. Combined with a quantification of the errors involved in a graphical approach, these advances bring the technique closer to industrial readiness. Algorithm performance is shown to be linear with respect to geometric complexity of the input curve

Automated Morphology Analysis of Nanoparticles

The functional properties of nanoparticles highly depend on the surface morphology of the particles, so precise measurements of a particle's morphology enable reliable characterizing of the nanoparticle's properties. Obtaining the measurements requires image analysis of electron microscopic pictures of nanoparticles. Today's labor-intensive image analysis of electron micrographs of nanoparticles is a significant bottleneck for efficient material characterization. The objective of this dissertation is to develop automated morphology analysis methods. Morphology analysis is comprised of three tasks: separate individual particles from an agglomerate of overlapping nano-objects (image segmentation); infer the particle's missing contours (shape inference); and ultimately, classify the particles by shape based on their complete contours (shape classification). Two approaches are proposed in this dissertation: the divide-and-conquer approach and the convex shape analysis approach. The divide-and-conquer approach solves each task separately, taking less than one minute to complete the required analysis, even for the largest-sized micrograph. However, its separating capability of particle overlaps is limited, meaning that it is able to split only touching particles. The convex shape analysis approach solves shape inference and classification simultaneously for better accuracy, but it requires more computation time, ten minutes for the biggest-sized electron micrograph. However, with a little sacrifice of time efficiency, the second approach achieves far superior separation than the divide-and-conquer approach, and it handles the chain-linked structure of particle overlaps well. The capabilities of the two proposed methods cannot be substituted by generic image processing and bio-imaging methods. This is due to the unique features that the electron microscopic pictures of nanoparticles have, including special particle overlap structures, and large number of particles to be processed. The application of the proposed methods to real electron microscopic pictures showed that the two proposed methods were more capable of extracting the morphology information than the state-of-the-art methods. When nanoparticles do not have many overlaps, the divide-and-conquer approach performed adequately. When nanoparticles have many overlaps, forming chain-linked clusters, the convex shape analysis approach performed much better than the state-of-the-art alternatives in bio-imaging. The author believes that the capabilities of the proposed methods expedite the morphology characterization process of nanoparticles. The author further conjectures that the technical generality of the proposed methods could even be a competent alternative to the current methods analyzing general overlapping convex-shaped objects other than nanoparticles

Real-Time Path Planning for Automating Optical Tweezers based Particle Transport Operations

Optical tweezers (OT) have been developed to successfully trap, orient, and transport micro and nano scale components of many different sizes and shapes in a fluid medium. They can be viewed as robots made out of light. Components can be simply released from optical traps by switching off laser beams. By utilizing the principle of time sharing or holograms, multiple optical traps can perform several operations in parallel. These characteristics make optical tweezers a very promising technology for creating directed micro and nano scale assemblies. In the infra-red regime, they are useful in a large number of biological applications as well. This dissertation explores the problem of real-time path planning for autonomous OT based transport operations. Such operations pose interesting challenges as the environment is uncertain and dynamic due to the random Brownian motion of the particles and noise in the imaging based measurements. Silica microspheres having diameters between (1-20) µm are selected as model components. Offline simulations are performed to gather trapping probability data that serves as a measure of trap strength and reliability as a function of relative position of the particle under consideration with respect to the trap focus, and trap velocity. Simplified models are generated using Gaussian Radial Basis Functions to represent the data in a compact form. These metamodels can be queried at run-time to obtain estimated probability values accurately and efficiently. Simple trapping probability models are then utilized in a stochastic dynamic programming framework to compute optimum trap locations and velocities that minimizes the total, expected transport time by incorporating collision avoidance and recovery steps. A discrete version of an approximate partially observable Markov decision process algorithm, called the QMDP_NLTDV algorithm, is developed. Real-time performance is ensured by pruning the search space and enhancing convergence rates by introducing a non-linear value function. The algorithm is validated both using a simulator as well as a physical holographic tweezer set-up. Successful runs show that the automated planner is flexible, works well in reasonably crowded scenes, and is capable of transporting a specific particle to a given goal location by avoiding collisions either by circumventing or by trapping other freely diffusing particles. This technique for transporting individual particles is utilized within a decoupled and prioritized approach to move multiple particles simultaneously. An iterative version of a bipartite graph matching algorithm is also used to assign goal locations to target objects optimally. As in the case of single particle transport, simulation and some physical experiments are performed to validate the multi-particle planning approach