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Dioecy (separate sexes) has independently evolved numerous times across the angiosperm phylogeny and is recently derived in many lineages. However, our understanding is limited regarding the evolutionary mechanisms that drive the origins of dioecy in plants. The recent and repeated evolution of dioecy across angiosperms offers an opportunity to make strong inferences about the ecological, developmental, and molecular factors influencing the evolution of dioecy, and thus sex chromosomes. The genusAsparagus(Asparagaceae) is an emerging model taxon for studying dioecy and sex chromosome evolution, yet estimates for the age and origin of dioecy in the genus are lacking.

We use plastome sequences and fossil time calibrations in phylogenetic analyses to investigate the age and origin of dioecy in the genusAsparagus. We also review the diversity of sexual systems present across the genus to address contradicting reports in the literature.

We estimate that dioecy evolved once or twice approximately 2.78−3.78 million years ago inAsparagus, of which roughly 27% of the species are dioecious and the remaining are hermaphroditic with monoclinous flowers.

Our findings support previous work implicating a young age and the possibility of two origins of dioecy inAsparagus, which appear to be associated with rapid radiations and range expansion out of Africa. Lastly, we speculate that paleoclimatic oscillations throughout northern Africa may have helped set the stage for the origin(s) of dioecy inAsparagusapproximately 2.78−3.78 million years ago.

 

The construction of a better exchange-correlation potential in time-dependent density functional theory (TDDFT) can improve the accuracy of TDDFT calculations and provide more accurate predictions of the properties of many-electron systems. Here, we propose a machine learning method to develop the energy functional and the Kohn–Sham potential of a time-dependent Kohn–Sham (TDKS) system is proposed. The method is based on the dynamics of the Kohn–Sham system and does not require any data on the exact Kohn–Sham potential for training the model. We demonstrate the results of our method with a 1D harmonic oscillator example and a 1D two-electron example. We show that the machine-learned Kohn–Sham potential matches the exact Kohn–Sham potential in the absence of memory effect. Our method can still capture the dynamics of the Kohn–Sham system in the presence of memory effects. The machine learning method developed in this article provides insight into making better approximations of the energy functional and the Kohn–Sham potential in the TDKS system.

 

Abstract 167 Er 3+ doped solids are a promising platform for quantum technology due to erbium’s telecom C-band optical transition and its long hyperfine coherence times. We experimentally study the spin Hamiltonian and dynamics of 167 Er 3+ spins in Y 2 O 3 using electron paramagnetic resonance (EPR) spectroscopy. The anisotropic electron Zeeman, hyperfine and nuclear quadrupole matrices are fitted using data obtained by X-band (9.5 GHz) EPR spectroscopy. We perform pulsed EPR spectroscopy to measure spin relaxation time T 1 and coherence time T 2 for the 3 principal axes of an anisotropic g tensor. Long electronic spin coherence time up to 24.4 μ s is measured for lowest g transition at 4 K, exceeding previously reported values at much lower temperatures. Measurements of decoherence mechanism indicates T 2 limited by spectral diffusion and instantaneous diffusion. Long spin coherence times, along with a strong anisotropic hyperfine interaction makes 167 Er 3+ :Y 2 O 3 a rich system and an excellent candidate for spin-based quantum technologies.