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  1. Free, publicly-accessible full text available August 23, 2025
  2. Summary

    Legume nodulation requires the detection of flavonoids in the rhizosphere by rhizobia to activate their production of Nod factor countersignals. Here we investigated the flavonoids involved in nodulation ofMedicago truncatula.

    We biochemically characterized five flavonoid‐O‐methyltransferases (OMTs) and a lux‐basednodgene reporter was used to investigate the response ofSinorhizobium medicaeNodD1 to various flavonoids.

    We found that chalcone‐OMT 1 (ChOMT1) and ChOMT3, but not OMT2, 4, and 5, were able to produce 4,4′‐dihydroxy‐2′‐methoxychalcone (DHMC). The bioreporter responded most strongly to DHMC, while isoflavones important for nodulation of soybean (Glycine max) showed no activity. Mutant analysis revealed that loss of ChOMT1 strongly reduced DHMC levels. Furthermore,chomt1andomt2showed strongly reduced bioreporter luminescence in their rhizospheres. In addition, loss of both ChOMT1 and ChOMT3 reduced nodulation, and this phenotype was strengthened by the further loss of OMT2.

    We conclude that: the loss of ChOMT1 greatly reduces root DHMC levels; ChOMT1 or OMT2 are important fornodgene activation in the rhizosphere; and ChOMT1/3 and OMT2 promote nodulation. Our findings suggest a degree of exclusivity in the flavonoids used for nodulation inM. truncatulacompared to soybean, supporting a role for flavonoids in rhizobial host range.

     
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    Free, publicly-accessible full text available June 1, 2025
  3. Fairness and robustness are two important goals in the design of modern distributed learning systems. Despite a few prior works attempting to achieve both fairness and robustness, some key aspects of this direction remain underexplored. In this paper, we try to answer three largely unnoticed and unaddressed questions that are of paramount significance to this topic: (i) What makes jointly satisfying fairness and robustness difficult? (ii) Is it possible to establish theoretical guarantee for the dual property of fairness and robustness? (iii) How much does fairness have to sacrifice at the expense of robustness being incorporated into the system? To address these questions, we first identify data heterogeneity as the key difficulty of combining fairness and robustness. Accordingly, we propose a fair and robust framework called H-nobs which can offer certified fairness and robustness through the adoption of two key components, a fairness-promoting objective function and a simple robust aggregation scheme called norm-based screening (NBS). We explain in detail why NBS is the suitable scheme in our algorithm in contrast to other robust aggregation measures. In addition, we derive three convergence theorems for H-nobs in cases of the learning model being nonconvex, convex, and strongly convex respectively, which provide theoretical guarantees for both fairness and robustness. Further, we empirically investigate the influence of the robust mechanism (NBS) on the fairness performance of H-nobs, the very first attempt of such exploration. 
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  4. null (Ed.)