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SUMMARY The phytohormone cytokinin plays a significant role in nearly all aspects of plant growth and development. Cytokinin signaling has primarily been studied in the dicot model Arabidopsis, with relatively little work done in monocots, which include rice (Oryza sativa) and other cereals of agronomic importance. The cytokinin signaling pathway is a phosphorelay comprised of the histidine kinase receptors, the authentic histidine phosphotransfer proteins (AHPs) and type‐B response regulators (RRs). Two negative regulators of cytokinin signaling have been identified: the type‐A RRs, which are cytokinin primary response genes, and the pseudo histidine phosphotransfer proteins (PHPs), which lack the His residue required for phosphorelay. Here, we describe the role of the ricePHPgenes. Phylogenic analysis indicates that the PHPs are generally first found in the genomes of gymnosperms and that they arose independently in monocots and dicots. Consistent with this, the three ricePHPsfail to complement an Arabidopsisphpmutant (aphp1/ahp6). Disruption of the three ricePHPsresults in a molecular phenotype consistent with these elements acting as negative regulators of cytokinin signaling, including the induction of a number of type‐A RR and cytokinin oxidase genes. The triplephpmutant affects multiple aspects of rice growth and development, including shoot morphology, panicle architecture, and seed fill. In contrast to Arabidopsis, disruption of the ricePHPsdoes not affect root vascular patterning, suggesting that while many aspects of key signaling networks are conserved between monocots and dicots, the roles of at least some cytokinin signaling elements are distinct.
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PARAMETRIZED HIERARCHICAL PROCEDURES FOR NEURAL PROGRAMMING
Neural programs are highly accurate and structured policies that perform algorith- mic tasks by controlling the behavior of a computation mechanism. Despite the potential to increase the interpretability and the compositionality of the behavior of artificial agents, it remains difficult to learn from demonstrations neural networks that represent computer programs. The main challenges that set algorithmic do- mains apart from other imitation learning domains are the need for high accuracy, the involvement of specific structures of data, and the extremely limited observabil- ity. To address these challenges, we propose to model programs as Parametrized Hierarchical Procedures (PHPs). A PHP is a sequence of conditional operations, using a program counter along with the observation to select between taking an elementary action, invoking another PHP as a sub-procedure, and returning to the caller. We develop an algorithm for training PHPs from a set of supervisor demonstrations, only some of which are annotated with the internal call structure, and apply it to efficient level-wise training of multi-level PHPs. We show in two benchmarks, NanoCraft and long-hand addition, that PHPs can learn neural pro- grams more accurately from smaller amounts of both annotated and unannotated demonstrations.
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- Award ID(s):
- 1734633
- PAR ID:
- 10063833
- Date Published:
- Journal Name:
- ICLR 2018
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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