Barnes Hospital Record

https://digitalcommons.wustl.edu/bjc_barnes_record/1210/thumbnail.jp

1940-04-11 Morehead Independent

Morehead Independent published on April 11, 1940

The genetic control of reproductive development under high ambient temperature

Ambient temperature has a large impact on reproductive development and grain yield in temperate cereals. However, little is known about the genetic control of development under different ambient temperatures. Here, we demonstrate that in barley (Hordeum vulgare), high ambient temperatures accelerate or delay reproductive development depending on the photoperiod response gene PHOTOPERIOD1 (Ppd-H1) and its upstream regulator EARLY FLOWERING3 (HvELF3). A natural mutation in Ppd-H1 prevalent in spring barley delayed floral development and reduced the number of florets and seeds per spike, while the wild-type Ppd-H1 or a mutant Hvelf3 allele accelerated floral development and maintained the seed number under high ambient temperatures. High ambient temperature delayed the expression phase and reduced the amplitude of clock genes and repressed the floral integrator gene FLOWERING LOCUS T1 independently of the genotype. Ppd-H1-dependent variation in flowering time under different ambient temperatures correlated with relative expression levels of the BARLEY MADS-box genes VERNALIZATION1 (HvVRN1), HvBM3, and HvBM8 in the leaf. Finally, we show that Ppd-H1 interacts with regulatory variation at HvVRN1. Ppd-H1 only accelerated floral development in the background of a spring HvVRN1 allele with a deletion in the regulatory intron. The full-length winter Hvvrn1 allele was strongly down-regulated, and flowering was delayed by high temperatures irrespective of Ppd-H1. Our findings demonstrate that the photoperiodic and vernalization pathways interact to control flowering time and floret fertility in response to ambient temperature in barley

Kernelized Reinforcement Learning with Order Optimal Regret Bounds

Reinforcement learning (RL) has shown empirical success in various real world settings with complex models and large state-action spaces. The existing analytical results, however, typically focus on settings with a small number of state-actions or simple models such as linearly modeled state-action value functions. To derive RL policies that efficiently handle large state-action spaces with more general value functions, some recent works have considered nonlinear function approximation using kernel ridge regression. We propose $\pi$-KRVI, an optimistic modification of least-squares value iteration, when the state-action value function is represented by an RKHS. We prove the first order-optimal regret guarantees under a general setting. Our results show a significant polynomial in the number of episodes improvement over the state of the art. In particular, with highly non-smooth kernels (such as Neural Tangent kernel or some Mat\'ern kernels) the existing results lead to trivial (superlinear in the number of episodes) regret bounds. We show a sublinear regret bound that is order optimal in the case of Mat\'ern kernels where a lower bound on regret is known