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Abstract This paper develops a New Keynesian model featuring financial intermediation, short- and long-term bonds, credit shocks, and scope for unconventional monetary policy. The log-linearized model reduces to four equations: Phillips and IS curves, as well as policy rules for the short-term interest rate and the central bank's long-bond portfolio (QE). Credit shocks and QE appear in both the IS and Phillips curves. In equilibrium, optimal monetary policy entails adjusting the short-term interest rate to offset natural rate shocks but using QE to offset credit market disruptions. Use of QE significantly mitigates the costs of a binding zero lower bound. 

Arabidopsis RESISTANCE TO POWDERY MILDEW 8.2 (RPW8.2) is specifically induced by the powdery mildew (PM) fungus (Golovinomyces cichoracearum) in the infected epidermal cells to activate immunity. However, the mechanism of RPW8.2-induction is not well understood. Here, we identify a G. cichoracearum effector that interacts with RPW8.2, named Gc-RPW8.2 interacting protein 1 (GcR8IP1), by a yeast two-hybrid screen of an Arabidopsis cDNA library. GcR8IP1 physically associated with RPW8.2 with its RING finger domain that is essential and sufficient for the association. GcR8IP1 was secreted and translocated into the nucleus of host cell infected with PM. Association of GcR8IP1 with RPW8.2 led to an increase of RPW8.2 in the nucleus. In turn, the nucleus-localised RPW8.2 promoted the activity of the RPW8.2 promoter, resulting in transcriptional self-amplification of RPW8.2 to boost immunity at infection sites. Additionally, ectopic expression or host-induced gene silencing of GcR8IP1 supported its role as a virulence factor in PM. Altogether, our results reveal a mechanism of RPW8.2-dependent defense strengthening via altered partitioning of RPW8.2 and transcriptional self-amplification triggered by a PM fungal effector, which exemplifies an atypical form of effector-triggered immunity. 

Unraveling molecular regulatory networks underlying disease progression is critically important for understanding disease mechanisms and identifying drug targets. The existing methods for inferring gene regulatory networks (GRNs) rely mainly on time-course gene expression data. However, most available omics data from cross-sectional studies of cancer patients often lack sufficient temporal information, leading to a key challenge for GRN inference. Through quantifying the latent progression using random walks-based manifold distance, we propose a latent-temporal progression-based Bayesian method, PROB, for inferring GRNs from the cross-sectional transcriptomic data of tumor samples. The robustness of PROB to the measurement variabilities in the data is mathematically proved and numerically verified. Performance evaluation on real data indicates that PROB outperforms other methods in both pseudotime inference and GRN inference. Applications to bladder cancer and breast cancer demonstrate that our method is effective to identify key regulators of cancer progression or drug targets. The identified ACSS1 is experimentally validated to promote epithelial-to-mesenchymal transition of bladder cancer cells, and the predicted FOXM1-targets interactions are verified and are predictive of relapse in breast cancer. Our study suggests new effective ways to clinical transcriptomic data modeling for characterizing cancer progression and facilitates the translation of regulatory network-based approaches into precision medicine. 

The practical application of lithium (Li) metal anode (LMA) is still hindered by non‐uniformity of solid electrolyte interphase (SEI), formation of “dead” Li, and continuous consumption of electrolyte although LMA has an ultrahigh theoretical specific capacity and a very low electrochemical redox potential. Herein, a facile protection strategy is reported for LMA using a double layer (DL) coating that consists of a polyethylene oxide (PEO)‐based bottom layer that is highly stable with LMA and promotes uniform ion flux, and a cross‐linked polymer‐based top layer that prevents solvation of PEO layer in electrolytes. Li deposited on DL‐coated Li (DL@Li) exhibits a smoother surface and much larger size than that deposited on bare Li. The LiF/Li₂O enriched SEI layer generated by the salt decomposition on top of DL@Li further suppresses the side reactions between Li and electrolyte. Driven by the abovementioned advantageous features, the DL@Li||LiNi_0.6Mn_0.2Co_0.2O₂cells demonstrate capacity retention of 92.4% after 220 cycles at a current density of 2.1 mA cm^–2(C/2 rate) and stability at a high charging current density of 6.9 mA cm^–2(1.5 C rate). These results indicate that the DL protection is promising to overcome the rate limitation of LMAs and high energy‐density Li metal batteries.