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Leaves are a key forage part for livestock, and the aging of leaves affects forage biomass and quality. Preventing or delaying premature leaf senescence leads to an increase in pasture biomass accumulation and an improvement in alfalfa quality. NAC transcription factors have been reported to affect plant growth and abiotic stress responses. In this study, 48 NAC genes potentially associated with leaf senescence were identified in alfalfa under dark or salt stress conditions. A phylogenetic analysis divided MsNACs into six subgroups based on similar gene structure and conserved motif. These MsNACs were unevenly distributed in 26 alfalfa chromosomes. The results of the collinearity analysis show that all of the MsNACs were involved in gene duplication. Some cis-acting elements related to hormones and stress were screened in the 2-kb promoter regions of MsNACs. Nine of the MsNAC genes were subjected to qRT-PCR to quantify their expression and Agrobacterium-mediated transient expression to verify their functions. The results indicate that Ms.gene031485, Ms.gene032313, Ms.gene08494, and Ms.gene77666 might be key NAC genes involved in alfalfa leaf senescence. Our findings extend the understanding of the regulatory function of MsNACs in leaf senescence. 

Accurate traffic speed prediction is critical to many applications, from routing and urban planning to infrastructure management. With sufficient training data where all spatio-temporal patterns are well- represented, machine learning models such as Spatial-Temporal Graph Convolutional Networks (STGCN), can make reasonably accurate predictions. However, existing methods fail when the training data distribution (e.g., traffic patterns on regular days) is different from test distribution (e.g., traffic patterns on special days). We address this challenge by proposing a traffic-law-informed network called Reaction-Diffusion Graph Ordinary Differential Equation (RDGODE) network, which incorporates a physical model of traffic speed evolution based on a reliable and interpretable reaction- diffusion equation that allows the RDGODE to adapt to unseen traffic patterns. We show that with mismatched training data, RDGODE is more robust than the state-of-the-art machine learning methods in the following cases. (1) When the test dataset exhibits spatio-temporal patterns not represented in the training dataset, the performance of RDGODE is more consistent and reliable. (2) When the test dataset has missing data, RDGODE can maintain its accuracy by intrinsically imputing the missing values. 

The propagation of spin waves in magnetically ordered systems has emerged as a potential means to shuttle quantum information over large distances. Conventionally, the arrival time of a spin wavepacket at a distance,d, is assumed to be determined by its group velocity,v_g. Here, we report time-resolved optical measurements of wavepacket propagation in the Kagome ferromagnet Fe₃Sn₂that demonstrate the arrival of spin information at times significantly less thand/v_g. We show that this spin wave “precursor” originates from the interaction of light with the unusual spectrum of magnetostatic modes in Fe₃Sn₂. Related effects may have far-reaching consequences toward realizing long-range, ultrafast spin wave transport in both ferromagnetic and antiferromagnetic systems. 

Abstract Endohedral metallofullerenes are chemically more inert compared to empty fullerenes, primarily due to their intramolecular electron transfer. In this work, we report an inverse electron demand Diels–Alder (IEDDA) reaction on M₃N@C₈₀(M=Lu, Sc), where they show significantly higher reactivity than empty fullerenes. The molecular structures of the [4+2] cycloadducts were unambiguously characterized. Moreover, the cycloadducts can fully revert to pristine M₃N@C₈₀via retro‐cycloaddition upon thermal treatment. With the unusual reactivity and reversibility, the IEDDA reaction enables an effective separation approach for metallofullerenes from their soot extracts, opening path to efficient and economical scale‐up synthesis of metallofullerenes in laboratory and industrial settings. 

An overarching goal in machine learning is to build a generalizable model with few samples. To this end, overparameterization has been the subject of immense interest to explain the generalization ability of deep nets even when the size of the dataset is smaller than that of the model. While the prior literature focuses on the classical supervised setting, this paper aims to demystify overparameterization for meta-learning. Here we have a sequence of linear-regression tasks and we ask: (1) Given earlier tasks, what is the optimal linear representation of features for a new downstream task? and (2) How many samples do we need to build this representation? This work shows that surprisingly, overparameterization arises as a natural answer to these fundamental meta-learning questions. Specifically, for (1), we first show that learning the optimal representation coincides with the problem of designing a task-aware regularization to promote inductive bias. We leverage this inductive bias to explain how the downstream task actually benefits from overparameterization, in contrast to prior works on few-shot learning. For (2), we develop a theory to explain how feature covariance can implicitly help reduce the sample complexity well below the degrees of freedom and lead to small estimation error. We then integrate these findings to obtain an overall performance guarantee for our meta-learning algorithm. Numerical experiments on real and synthetic data verify our insights on overparameterized meta-learning. 

A bioinspired, untethered, and small-scale origami crawler achieves effective locomotion and steering in severely confined spaces.