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  1. Abstract

    Heterotrimeric G-proteins modulate multiple signaling pathways in many eukaryotes. In plants, G-proteins have been characterized primarily from a few model angiosperms and a moss. Even within this small group, they seem to affect plant phenotypes differently: G-proteins are essential for survival in monocots, needed for adaptation but are nonessential in eudicots, and are required for life cycle completion and transition from the gametophytic to sporophytic phase in the moss Physcomitrium (Physcomitrella) patens. The classic G-protein heterotrimer consists of three subunits: one Gα, one Gβ and one Gγ. The Gα protein is a catalytically active GTPase and, in its active conformation, interacts with downstream effectors to transduce signals. Gα proteins across the plant evolutionary lineage show a high degree of sequence conservation. To explore the extent to which this sequence conservation translates to their function, we complemented the well-characterized Arabidopsis Gα protein mutant, gpa1, with Gα proteins from different plant lineages and with the yeast Gpa1 and evaluated the transgenic plants for different phenotypes controlled by AtGPA1. Our results show that the Gα protein from a eudicot or a monocot, represented by Arabidopsis and Brachypodium, respectively, can fully complement all gpa1 phenotypes. However, the basal plant Gα failed to complement the developmental phenotypes exhibited by gpa1 mutants, although the phenotypes that are exhibited in response to various exogenous signals were partially or fully complemented by all Gα proteins. Our results offer a unique perspective on the evolutionarily conserved functions of G-proteins in plants.

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  2. Abstract Background 3D imaging, such as X-ray CT and MRI, has been widely deployed to study plant root structures. Many computational tools exist to extract coarse-grained features from 3D root images, such as total volume, root number and total root length. However, methods that can accurately and efficiently compute fine-grained root traits, such as root number and geometry at each hierarchy level, are still lacking. These traits would allow biologists to gain deeper insights into the root system architecture. Results We present TopoRoot, a high-throughput computational method that computes fine-grained architectural traits from 3D images of maize root crowns or root systems. These traits include the number, length, thickness, angle, tortuosity, and number of children for the roots at each level of the hierarchy. TopoRoot combines state-of-the-art algorithms in computer graphics, such as topological simplification and geometric skeletonization, with customized heuristics for robustly obtaining the branching structure and hierarchical information. TopoRoot is validated on both CT scans of excavated field-grown root crowns and simulated images of root systems, and in both cases, it was shown to improve the accuracy of traits over existing methods. TopoRoot runs within a few minutes on a desktop workstation for images at the resolution range of 400^3, with minimal need for human intervention in the form of setting three intensity thresholds per image. Conclusions TopoRoot improves the state-of-the-art methods in obtaining more accurate and comprehensive fine-grained traits of maize roots from 3D imaging. The automation and efficiency make TopoRoot suitable for batch processing on large numbers of root images. Our method is thus useful for phenomic studies aimed at finding the genetic basis behind root system architecture and the subsequent development of more productive crops. 
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  3. Jez, Joseph M. ; Topp, Christopher N. (Ed.)
    A plants’ water and nutrients are primarily absorbed through roots, which in a natural setting is highly dependent on the 3-dimensional configuration of the root system, collectively known as root system architecture (RSA). RSA is difficult to study due to a variety of factors, accordingly, an arsenal of methods have been developed to address the challenges of both growing root systems for imaging, and the imaging methods themselves, although there is no ‘best’ method as each has its own spectrum of trade-offs. Here, we describe several methods for plant growth or imaging. Then, we introduce the adaptation and integration of three complementary methods, root mesocosms, photogrammetry, and electrical resistance tomography (ERT). Mesocosms can allow for unconstrained root growth, excavation and preservation of 3-dimensional RSA, and modularity that facilitates the use of a variety of sensors. The recovered root system can be digitally reconstructed through photogrammetry, which is an inexpensive method requiring only an appropriate studio space and a digital camera. Lastly, we demonstrate how 3-dimensional water availability can be measured using ERT inside of root mesocosms. 
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  4. null (Ed.)
    Numerous types of biological branching networks, with varying shapes and sizes, are used to acquire and distribute resources. Here, we show that plant root and shoot architectures share a fundamental design property. We studied the spatial density function of plant architectures, which specifies the probability of finding a branch at each location in the 3-dimensional volume occupied by the plant. We analyzed 1645 root architectures from four species and discovered that the spatial density functions of all architectures are population-similar. This means that despite their apparent visual diversity, all of the roots studied share the same basic shape, aside from stretching and compression along orthogonal directions. Moreover, the spatial density of all architectures can be described as variations on a single underlying function: a Gaussian density truncated at a boundary of roughly three standard deviations. Thus, the root density of any architecture requires only four parameters to specify: the total mass of the architecture and the standard deviations of the Gaussian in the three x , y , z growth directions. Plant shoot architectures also follow this design form, suggesting that two basic plant transport systems may use similar growth strategies. 
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  5. null (Ed.)
    Underpinning the success of deep learning is effective regularizations that allow a variety of priors in data to be modeled. For example, robustness to adversarial perturbations, and correlations between multiple modalities. However, most regularizers are specified in terms of hidden layer outputs, which are not themselves optimization variables. In contrast to prevalent methods that optimize them indirectly through model weights, we propose inserting proximal mapping as a new layer to the deep network, which directly and explicitly produces well regularized hidden layer outputs. The resulting technique is shown well connected to kernel warping and dropout, and novel algorithms were developed for robust temporal learning and multiview modeling, both outperforming state-of-the-art methods. 
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  6. null (Ed.)
  7. Grapevine 3D inflorescence architecture was comprehensively characterized among 10 wild Vitis species to reveal new phenotypic and evolutionary relationships. 
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