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We developed a new method for simultaneously visualizing RNAs and proteins in plant cells. This method works well for endogenous as well as infectious RNAs and their cognate binding proteins. 

Abstract Active region NOAA 13842 produced two successive solar flares: an X7.1-class flare on 2024 October 1, and an X9.0-class flare on 2024 October 3. This study continues our previous simulation work that successfully reproduced the X7.1-class solar flare. In this study, we performed a data-constrained magnetohydrodynamic simulation using the nonlinear force-free field (NLFFF) as the initial condition to investigate the X9.0-class solar flare. The NLFFF showed the sheared field lines, resulting in the tether-cutting reconnection, the magnetic flux ropes, and eventually led to eruption. The magnetic reconnection during the pre-eruption phase plays a critical role in accelerating the subsequent eruption, which is driven by torus instability and magnetic reconnection. Furthermore, our simulation results are consistent with several observational features associated with the X9.0 flare. This simulation could reproduce diverse phenomena associated with the X9.0 flare, including the tether-cutting reconnection, the flare ribbons and the postflare loops, the transverse field enhancement, and the remote brightening away from the flare ribbons. However, the initial trigger, magnetic flux emergence, was inferred from observations rather than explicitly modeled, and future comprehensive simulations should incorporate this mechanism directly. 

Long-horizon tasks in unstructured environments are notoriously challenging for robots because they require the prediction of extensive action plans with thousands of steps while adapting to ever-changing conditions by reasoning among multimodal sensing spaces. Humans can efficiently tackle such compound problems by breaking them down into easily reachable abstract sub-goals, significantly reducing complexity. Inspired by this ability, we explore how we can enable robots to acquire sub-goal formulation skills for long-horizon tasks and generalize them to novel situations and environments. To address these challenges, we propose the Zero-shot Abstract Sub-goal Framework (ZAS-F), which empowers robots to decompose overarching action plans into transferable abstract sub-goals, thereby providing zero-shot capability in new task conditions. ZAS-F is an imitation-learning-based method that efficiently learns a task policy from a few demonstrations. The learned policy extracts abstract features from multimodal and extensive temporal observations and subsequently uses these features to predict task-agnostic sub-goals by reasoning about their latent relations. We evaluated ZAS-F in radio frequency identification (RFID) inventory tasks across various dynamic environments, a typical long-horizon task requiring robots to handle unpredictable conditions, including unseen objects and structural layouts. Ourexperiments demonstrated that ZAS-F achieves a learning efficiency 30 times higher than previous methods, requiring only 8k demonstrations. Compared to prior approaches, ZAS-F achieves a 98.3% scanning accuracy while significantly reducing the training data requirement. Further, ZAS-F demonstrated strong generalization, maintaining a scan success rate of 99.4% in real-world deployment without additional finetuning. In long-term operations spanning 100 rooms, ZAS-F maintained consistent performance compared to short-term tasks, highlighting its robustness against compounding errors. These results establish ZAS-F as an efficient and adaptable solution for long-horizon robotic tasks in unstructured environments. 

Abstract We conducted data-constrained magnetohydrodynamic (MHD) simulations for solar active region (AR) NOAA AR 11429, which produced two X-class flares within a span of 63 minutes. The simulations were performed using the zero-βMHD approximation, with the initial condition derived from the nonlinear force-free field extrapolated from the photospheric magnetograms taken 2 hr before the first X5.4 flare. During the simulation, we enhanced magnetic reconnection locally by applying anomalous resistivity in the induction equation within the regions of interest. As a result, the simulations successfully reproduced the expansion of two magnetic flux ropes (MFRs) corresponding to the two observed eruptions. The result shows that the difference in stability between the two MFRs is related to the location of the magnetic reconnection that triggers the solar eruptions. Furthermore, comparison with the analysis of failed MFR eruptions indicates that both the initiation reconnection and the subsequent driving mechanism, torus instability, are equally important for a successful eruption. This simulation reveals a new mechanism in which long loops, formed via tether-cutting reconnection, push up the overlying twisted field lines, leading to their destabilization by torus instability. 

The cancellation problem asks whether A[X1,X2,…,Xn] ≅ B[Y1, Y2, . . . , Yn] implies A ≅ B. Hamann introduced the class of steadfast rings as the rings for which a version of the cancellation problem considered by Abhyankar, Eakin, and Heinzer holds. By work of Asanuma, Hamann, and Swan, steadfastness can be characterized in terms of p-seminormality, which is a variant of normality introduced by Swan. We prove that p-seminormality and steadfastness deform for reduced Noetherian local rings. We also prove that p-seminormality and steadfastness are stable under adjoining formal power series variables for reduced (not necessarily Noetherian) rings. Our methods also give new proofs of the facts that normality and weak normality deform, which are of independent interest. 

Abstract Processing bodies (PBs) and stress granules (SGs) are membrane-less cellular compartments consisting of ribonucleoprotein complexes. Whereas PBs are more ubiquitous, SGs are assembled mainly in response to stress. PBs and SGs are known to physically interact and molecules exchange between the two have been documented in mammals. However, the molecular mechanisms underpinning these processes are virtually unknown in plants. We have reported recently that tandem CCCH zinc finger 1 (TZF1) protein can recruit MAPK signaling components to SGs. Here we have found that TZF1-MPK3/6-MKK4/5 form a protein-protein interacting network in SGs. The mRNA decapping factor 1 (DCP1) is a core component of PBs. MAPK signaling mediated phosphorylation triggers a rapid reduction of DCP1 partition into PBs, concomitantly associated with an increase of DCP1 assembly into SGs. Furthermore, we have found that plant SG marker protein UBP1b (oligouridylate binding protein 1b) plays a role in maintaining DCP1 in PBs by suppressing the accumulation of MAPK signaling components. Together, we propose that MAPK signaling and UBP1b mediate the dynamics of PBs and SGs in plant cells. 

Abstract RNA comprises a versatile group of biomolecules that play diverse roles in a wide range of biological processes. From synthesis to degradation, RNAs interact with cognate proteins that assist in processes such as transcription, splicing, modification, trafficking, and the execution of their functions. While numerous valuable techniques exist to study RNA-protein interactions, observing RNAs and their associated proteins simultaneously within cells remains a challenge, despite its potential to provide deeper insights into RNA-protein interactions. In this study, we adapted a modified immunofluorescence (IF) assay combined with RNA fluorescence in situ hybridization (FISH) to successfully visualize the colocalization of potato spindle tuber viroid with RNA polymerase II in the nucleus. This new method that combines IF and FISH will facilitate future studies on RNA and protein colocalization in various plant systems.