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Abstract Social controllability, or the ability to exert control during social interactions, is crucial for optimal decision-making. Inability to do so might contribute to maladaptive behaviors such as smoking, which often takes place in social settings. Here, we examined social controllability in nicotine-dependent humans as they performed an fMRI task where they could influence the offers made by simulated partners. Computational modeling revealed that smokers under-estimated the influence of their actions and self-reported a reduced sense of control, compared to non-smokers. These findings were replicated in a large independent sample of participants recruited online. Neurally, smokers showed reduced tracking of forward projected choice values in the ventromedial prefrontal cortex, and impaired computation of social prediction errors in the midbrain. These results demonstrate that smokers were less accurate in estimating their personal influence when the social environment calls for control, providing a neurocomputational account for the social cognitive deficits in this population. Pre-registrations: OSF Registries|How interoceptive state interacts with value-based decision-making in addiction (fMRI study). OSF Registries|COVID-19: social cognition, mental health, and social distancing (online study).

Abstract Polyamines, such as putrescine, spermidine, and spermine, are crucial for plant defense against both abiotic and biotic stresses. Putrescine is also known as a significant inducer of deoxynivalenol (DON) production in Fusarium graminearum, the primary causal agent of Fusarium head blight (FHB). However, the impact of other polyamines on DON production and whether modifying polyamine biosynthesis could improve wheat resistance to FHB are currently unknown. In this study, we demonstrate that key precursor components of putrescine synthesis, including arginine, ornithine, and agmatine, can induce DON production, albeit to a lesser extent than putrescine in trichothecene biosynthesis-inducing (TBI) culture under the same total nitrogen conditions. Intriguingly, spermidine and spermine, downstream products of putrescine in the polyamine biosynthesis pathway, do not induce DON production under the same conditions. Additionally, externally applying either spermidine or spermine to wheat heads significantly reduces the diseased spikelet number caused by F. graminearum. Furthermore, our results show that overexpression of the wheat spermidine synthase (SPDS) gene TaSPDS-7D1 significantly enhances the spermidine content and wheat resistance to FHB. In addition, the TaSPDS-7D1-overexpressing line OE3 exhibited a 1000-grain weight and plant height increase compared to the wild type. Our findings reveal that overexpression of the spermidine synthase gene can enhance wheat resistance to FHB without compromising wheat yield.

Abstract A-to-I mRNA editing in animals is mediated by ADARs, but the mechanism underlying sexual stage-specific A-to-I mRNA editing in fungi remains unknown. Here, we show that the eukaryotic tRNA-specific heterodimeric deaminase FgTad2-FgTad3 is responsible for A-to-I mRNA editing in Fusarium graminearum. This editing capacity relies on the interaction between FgTad3 and a sexual stage-specific protein called Ame1. Although Ame1 orthologs are widely distributed in fungi, the interaction originates in Sordariomycetes. We have identified key residues responsible for the FgTad3-Ame1 interaction. The expression and activity of FgTad2-FgTad3 are regulated through alternative promoters, alternative translation initiation, and post-translational modifications. Our study demonstrates that the FgTad2-FgTad3-Ame1 complex can efficiently edit mRNA in yeasts, bacteria, and human cells, with important implications for the development of base editors in therapy and agriculture. Overall, this study uncovers mechanisms, regulation, and evolution of RNA editing in fungi, highlighting the role of protein-protein interactions in modulating deaminase function.

Abstract Regulation of excessive inflammation and impaired cell proliferation is crucial for healing diabetic wounds. Although plant‐to‐mammalian regulation offers effective approaches for chronic wound management, the development of a potent plant‐based therapeutic presents challenges. This study aims to validate the efficacy of turmeric‐derived nanoparticles (TDNPs) loaded with natural bioactive compounds. TDNPs can alleviate oxidative stress, promote fibroblast proliferation and migration, and reprogram macrophage polarization. Restoration of the fibroblast–macrophage communication network by TDNPs stimulates cellular regeneration, in turn enhancing diabetic wound healing. To address diabetic wound management, TDNPs are loaded in an ultralight‐weight, high swelling ratio, breathable aerogel (AG) constructed with cellulose nanofibers and sodium alginate backbones to obtain TDNPs@AG (TAG). TAG features wound shape‐customized accessibility, water‐adaptable tissue adhesiveness, and capacity for sustained release of TDNPs, exhibiting outstanding performance in facilitating in vivo diabetic wound healing. This study highlights the potential of TDNPs in regenerative medicine and their applicability as a promising solution for wound healing in clinical settings.

With the rapid development of Internet social media,using recommendation algorithms to effectively model and filter massive amounts of information has become the key to predict user behavior preferences,hot spot tendency,network security si-tuation and other issues.At the same time,with the development of deep learning,graph neural network model has achieved good results in solving the dense graph structure data in recommendation system.Collaborative filtering algorithm,as the most widely used recommendation algorithm,uses user-item group interaction data to predict users' future preferences and item ratings.However,existing recommendation algorithms still face the problems of data sparseness and cold start,and lack of a good quantification of uncertainty.This paper proposes an inductive matrix completion prediction fusion algorithm based on probabilistic meta-learning(MetaIMC),which re-characterizes meta-learning from the perspective of Bayesian inference,builds a robust GNN-meta-learning model,and makes full use of data priors to build solutions for learning new tasks from sparse data.Firstly,MetaIMC can effectively use variational Bayesian inference to obtain the prior distribution,alleviate the uncertainty and ambiguity in the meta-model task training,and further improve the generalization ability of the model.Secondly,MetaIMC can implement new user reco-mmendations and solve the cold start problem without any user side information.Finally,in the two scenarios of traditional matrix completion and user cold start,the performance of the model is evaluated by using three public datasets of Flixster,Douban and Yahoo_music,which verifies the effectiveness of MetaIMC on traditional matrix completion task,and achieves the best performance on the cold start problem.

It is well known that the proper allocation of learning time is particularly important for promoting students’ academic performance. Based on the data from PISA 2018, this research used the method of threshold regression and quantile regression to explore the optimal length of learning time to promote the students’ academic performance. At the same time, this research also explored the heterogeneity of the effect of learning time on different academic levels of students. The results show that for four Chinese provinces and cities, including Beijing, Shanghai, Jiangsu province and Zhejiang province, students who study in rural areas and private schools usually have longer learning time than students in cities and public schools. Moreover, it is suggested that there is no significant association between school quality and students’ learning time. The average learning time of students from the four Chinese cities and provinces is obviously longer than that in OECD countries. Moreover, it is found that the impact of learning time on academic performance across subjects is inverted U-shaped, and the optimal study time can be found in the learning of mathematics, science, and reading related subjects. As for the effect of learning time, the results showed that learning time commitment is more effective for students who are academically disadvantaged. At the same time, this study found that there is a relationship between students’ excessive learning time and students’ subjective well-being and attitudes toward learning activities. The non-cognitive factors can influence students’ academic performance gradually. According to the results of this research, it is suggested that students need to balance their learning time allocation against the appropriate learning time standards. Moreover, schools should adopt different learning time allocation schemes for students at different academic achievement levels. The teachers also should uphold a more scientific design of students’ after-school homework, and teachers and parents should also focus on improving students’ learning efficiency.

According to the hot-rolled microstructure of medium manganese steel,a geometry model and initial conditions of intercritical austenitization were established,and the DICTRA dynamic analysis software was used to calculate the austenitization on the martensitic matrix. It is found that supersaturated carbon and manganese in martensite quickly transfer from ferrite to austenite and aggregate at the austenitic side of the phase interface in the initial austenite nucleation stage. In the subsequent transformation,carbon rapidly homogenizes in austenite,but the aggregation degree of manganese in the austenitic side of the phase interface increases. The migration velocity of the austenite phase interface in the initial stage is several orders of magnitude higher than that in the middle and late stages,but rapidly decreases. The phase interface movement in the early transformation stage is dominated by carbon diffusion,but is restricted by manganese diffusion rate in austenite in the middle and late stages. Increasing the temperature can significantly improve the phase interface velocity. Under the condition of achieving the same amount of austenite,the low-temperature and longtime annealing is benefit for manganese transferring from ferrite to austenite and enriching in austenite,which will improve the stability of austenite.

The Men Lei Zeng Guang Shi Zhu Du Gong Bu Shi (門類增廣十注杜工部詩) collected by National Library of China, is one of the rare surviving editions of Song dynasty. After examination, the taboo words in this book were taboo to Song emperor Xiao Zong (宋孝宗), but not to Guang Zong (光宗), Ning Zong (寧宗), and Li Zong (理宗). Therefore, this edition was assumed to have been block printed during the reign of Emperor Xiao Zong of Song (1162–1189). The relationship between this book and Jiu Jia Ji Zhu Du Shi (九家集注杜詩) is not relationship of adding and subtracting annotations as William Hung (洪業) believes, but separate compilations. It is found that the names of the Xin Tian (新添) and Ji Zhu (集注) notations in this book have been changed among Du Ji (杜集) of Song dynasty, showing that this book appeared earlier in the development chain of the Du Shi Ji Zhu (杜詩集注). The difference between this book and the Fen Men Ben (分門本) about Wei Su Zhu (偽蘇注) is important for study of the origin and evolution of it. From the aspects of categorization, volume arrangement, and actual poetry collection, there exists a close version relationship between this book and Fen Men Ben. All the above points indicate that this book preserves the original information about Du Shi Ji Zhu in the early period, which is of great significance to the development of Du Shi Ji Zhu in the Song dynasty.(Article content in Chinese with English extended abstract)

Brassinosteroids (BRs) are essential phytohormones for plant growth and development. BRs are perceived by the cell surface receptor kinase BRI1, and downstream signal transduction through multiple components leads to activation of the transcription factors BZR1 and BZR2/BES1. BZR1 activity is highly controlled by BR through reversible phosphorylation, protein degradation, and nucleocytoplasmic shuttling. To further understand the molecular function of BZR1, we performed tandem affinity purification of the BZR1 complex and identified BZR1-associated proteins using mass spectrometry. These BZR1-associated proteins included several known BR signaling components, such as BIN2, BSK1, 14-3-3λ, and PP2A, as well as a large number of proteins with previously unknown functions in BR signal transduction, including the kinases MKK5 and MAPK4, histone deacetylase 19, cysteine proteinase inhibitor 6, a DEAD-box RNA helicase, cysteine endopeptidases RD21A and RD21B, calmodulin-binding transcription activator 5, ubiquitin protease 12, cyclophilin 59, and phospholipid-binding protein synaptotagmin A. Their interactions with BZR1 were confirmed by in vivo and in vitro assays. Furthermore, MKK5 was found to phosphorylate BZR1 in vitro. This study demonstrates an effective method for purifying proteins associated with low-abundance transcription factors, and identifies new BZR1-interacting proteins with potentially important roles in BR response.