Nguyen, Nga T. | Search NASA

DOE OSTI2022

Iron Availability within the Leaf Vasculature Determines the Magnitude of Iron Deficiency Responses in Source and Sink Tissues in Arabidopsis

Abstract Iron (Fe) uptake and translocation in plants are fine-tuned by complex mechanisms that are not yet fully understood. In Arabidopsis thaliana, local regulation of Fe homeostasis at the root level has been extensively studied and is better understood than the systemic shoot-to-root regulation. While the root system is solely a sink tissue that depends on photosynthates translocated from source tissues, the shoot system is a more complex tissue, where sink and source tissues occur synchronously. In this study, and to gain better insight into the Fe deficiency responses in leaves, we overexpressed Zinc/Iron-regulated transporter-like Protein (ZIP5), an Fe/Zn transporter, in phloem-loading cells (proSUC2::AtZIP5) and determined the timing of Fe deficiency responses in sink (young leaves and roots) and source tissues (leaves). Transgenic lines overexpressing ZIP5 in companion cells displayed increased sensitivity to Fe deficiency in root growth assays. Moreover, young leaves and roots (sink tissues) displayed either delayed or dampened transcriptional responses to Fe deficiency compared to wild-type (WT) plants. We also took advantage of the Arabidopsis mutant nas4x-1 to explore Fe transcriptional responses in the opposite scenario, where Fe is retained in the vasculature but in an unavailable and precipitated form. In contrast to proSUC2::AtZIP5 plants, nas4x-1 young leaves and roots displayed a robust and constitutive Fe deficiency response, while mature leaves showed a delayed and dampened Fe deficiency response compared to WT plants. Altogether, our data provide evidence suggesting that Fe sensing within leaves can also occur locally in a leaf-specific manner.

Cell Biology↗

DOE OSTI2020

A regression algorithm for accelerated lattice QCD that exploits sparse inference on the D-Wave quantum annealer

We propose a regression algorithm that utilizes a learned dictionary optimized for sparse inference on a D-Wave quantum annealer. In this regression algorithm, we concatenate the independent and dependent variables as a combined vector, and encode the high-order correlations between them into a dictionary optimized for sparse reconstruction. On a test dataset, the dependent variable is initialized to its average value and then a sparse reconstruction of the combined vector is obtained in which the dependent variable is typically shifted closer to its true value, as in a standard inpainting or denoising task. Here, a quantum annealer, which can presumably exploit a fully entangled initial state to better explore the complex energy landscape, is used to solve the highly non-convex sparse coding optimization problem. The regression algorithm is demonstrated for a lattice quantum chromodynamics simulation data using a D-Wave 2000Q quantum annealer and good prediction performance is achieved. The regression test is performed using six different values for the number of fully connected logical qubits, between 20 and 64. The scaling results indicate that a larger number of qubits gives better prediction accuracy.

97 MATHEMATICS AND COMPUTING↗