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Studies on maize evolution and domestication are largely limited to the nuclear genomes, and the contribution of cytoplasmic genomes to selection and domestication of modern maize remains elusive. Maize cytoplasmic genomes have been classified into fertile (NA and NB) and cytoplasmic-nuclear male-sterility (CMS-S, CMS-C, and CMS-T) groups, but their contributions to modern maize breeding have not been systematically investigated.

Here we report co-selection and convergent evolution between nuclear and cytoplasmic genomes by analyzing whole genome sequencing data of 630 maize accessions modern maize and its relatives, including 24 fully assembled mitochondrial and chloroplast genomes. We show that the NB cytotype is associated with the expansion of modern maize to North America, gradually replaces the fertile NA cytotype probably through unequal division, and predominates in over 90% of modern elite inbred lines. The mode of cytoplasmic evolution is increased nucleotypic diversity among the genes involved in photosynthesis and energy metabolism, which are driven by selection and domestication. Furthermore, genome-wide association study reveals correlation of cytoplasmic nucleotypic variation with key agronomic and reproductive traits accompanied with the diversification of the nuclear genomes.

Our results indicate convergent evolution between cytoplasmic and nuclear genomes during maize domestication and breeding. These new insights into the important roles of mitochondrial and chloroplast genomes in maize domestication and improvement should help select elite inbred lines to improve yield stability and crop resilience of maize hybrids.

 

Aims.Recurring jets are observed in the solar atmosphere. They can erupt intermittently over a long period of time. By the observation of intermittent jets, we wish to understand what causes the characteristics of the periodic eruptions.

Methods.We report intermittent jets observed by the Goode Solar Telescope (GST) with the TiO Broadband Filter Imager (BFI), the Visible Imaging Spectrometer (VIS) in H_α, and the Near-InfraRed Imaging Spectropolarimeter (NIRIS). The analysis was aided and complemented by 1400 Å and 2796 Å data from the Interface Region Imaging Spectrograph (IRIS). These observational instruments allowed us to analyze the temporal characteristics of the jet events. By constructing the H_αdopplergrams, we found that the plasma first moves upward, but during the second phase of the jet, the plasma flows back. Working with time slice diagrams, we investigated the characteristics of the jet dynamics.

Results.The jet continued for up to 4 h. The time-distance diagram shows that the peak of the jet has clear periodic-eruption characteristics (5 min) during 18:00 UT–18:50 UT. We also found a periodic brightening phenomenon (5 min) during the jet bursts in the observed bands in the transition region (1400 Å and 2796 Å), which may be a response to intermittent jets in the upper solar atmosphere. The time lag is 3 min. Evolutionary images in the TiO band revealed a horizontal movement of the granulation at the location of the jet. By comparison to the quiet region of the Sun, we found that the footpoint of the jet is enhanced at the center of the H_αspectral line profile, without significant changes in the line wings. This suggests prolonged heating at the footpoint of the jet. In the mixed-polarity magnetic field region of the jet, we observed the emergence of magnetic flux, its cancellation, and shear, indicating possible intermittent magnetic reconnection. This is confirmed by the nonlinear force-free field model, which was reconstructed using the magneto-friction method.

Conclusions.The multiwavelength analysis indicates that the events we studied were triggered by magnetic reconnection that was caused by mixed-polarity magnetic fields. We suggest that the horizontal motion of the granulation in the photosphere drives the magnetic reconnection, which is modulated byp-mode oscillations.

 

In response to recent data regulation requirements, machine unlearning (MU) has emerged as a critical process to remove the influence of specific examples from a given model. Although exact unlearning can be achieved through complete model retraining using the remaining dataset, the associated computational costs have driven the development of efficient, approximate unlearning techniques. Moving beyond data-centric MU approaches, our study introduces a novel model-based perspective: model sparsification via weight pruning, which is capable of reducing the gap between exact unlearning and approximate unlearning. We show in both theory and practice that model sparsity can boost the multi-criteria unlearning performance of an approximate unlearner, closing the approximation gap, while continuing to be efficient. This leads to a new MU paradigm, termed prune first, then unlearn, which infuses a sparse model prior into the unlearning process. Building on this insight, we also develop a sparsity-aware unlearning method that utilizes sparsity regularization to enhance the training process of approximate unlearning. Extensive experiments show that our proposals consistently benefit MU in various unlearning scenarios. A notable highlight is the 77% unlearning efficacy gain of fine-tuning (one of the simplest unlearning methods) when using sparsity-aware unlearning. Furthermore, we demonstrate the practical impact of our proposed MU methods in addressing other machine learning challenges, such as defending against backdoor attacks and enhancing transfer learning. Codes are available at this https URL.