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Abstract In Arabidopsis (Arabidopsis thaliana), overproduction of salicylic acid (SA) increases disease resistance and abiotic stress tolerance but penalizes growth. This growth–defense trade-off has hindered the adoption of SA-based disease management strategies in agriculture. However, investigation of how SA inhibits plant growth has been challenging because many SA-hyperaccumulating Arabidopsis mutants have developmental defects due to the pleiotropic effects of the underlying genes. Here, we heterologously expressed a bacterial SA synthase gene in Arabidopsis and observed that elevated SA levels decreased plant growth and reduced the expression of cold-regulated (COR) genes in a dose-dependent manner. Growth suppression was exacerbated at below-ambient temperatures. Severing the SA-responsiveness of individual COR genes was sufficient to overcome the growth inhibition caused by elevated SA at ambient and below-ambient temperatures while preserving disease- and abiotic-stress-related benefits. Our results show the potential of decoupling SA-mediated growth and defense trade-offs for improving crop productivity. 

Redirected walking allows users to naturally locomote within virtual environments that are larger than or different in layout from the physically tracked space. In this paper, we proposed novel optimization-driven alignment-based and Artificial Potential Field (APF) redirected walking controllers, as well as an integrated version of the two. The first two controllers employ objective functions of one variable, which is the included angle between the user's heading vector and the target vector originating from the user's physical position. The optimized angle represents the physical cell that is best aligned with the virtual cell or the target vector on which the designated point has the minimum APF value. The derived optimized angle is used to finely set RDW gains. The two objective functions can be optimized simultaneously, leading to an integrated controller that is potentially able to take advantage of the alignment-based controller and APF-based controller. Through extensive simulation-based studies, we found that the proposed alignment-based and integrated controllers significantly outperform the state-of-the-art controllers and the proposed APF based controller in terms of the number of resets. Furthermore, the proposed alignment controller and integrated controller provide a more uniform likelihood distribution across distance between resets, as compared to the other controllers. 

Cryoelectron tomography directly visualizes heterogeneous macromolecular structures in their native and complex cellular environments. However, existing computer-assisted structure sorting approaches are low throughput or inherently limited due to their dependency on available templates and manual labels. Here, we introduce a high-throughput template-and-label-free deep learning approach, Deep Iterative Subtomogram Clustering Approach (DISCA), that automatically detects subsets of homogeneous structures by learning and modeling 3D structural features and their distributions. Evaluation on five experimental cryo-ET datasets shows that an unsupervised deep learning based method can detect diverse structures with a wide range of molecular sizes. This unsupervised detection paves the way for systematic unbiased recognition of macromolecular complexes in situ. 

Effective data management plays a key role in oceanographic research as cruise-based data, collected from different laboratories and expeditions, are commonly compiled to investigate regional to global oceanographic processes. Here we describe new and updated best practice data standards for discrete chemical oceanographic observations, specifically those dealing with column header abbreviations, quality control flags, missing value indicators, and standardized calculation of certain properties. These data standards have been developed with the goals of improving the current practices of the scientific community and promoting their international usage. These guidelines are intended to standardize data files for data sharing and submission into permanent archives. They will facilitate future quality control and synthesis efforts and lead to better data interpretation. In turn, this will promote research in ocean biogeochemistry, such as studies of carbon cycling and ocean acidification, on regional to global scales. These best practice standards are not mandatory. Agencies, institutes, universities, or research vessels can continue using different data standards if it is important for them to maintain historical consistency. However, it is hoped that they will be adopted as widely as possible to facilitate consistency and to achieve the goals stated above. 

Abstract In this paper, we report the synthesis of a phenanthroline and neomycin conjugate (7). Compound7binds to a human telomeric G‐quadruplex (G1) with a higher affinity compared with its parent compounds (phenanthroline and neomycin), which is determined by several biophysical studies. Compound7shows good selectivity for G‐quadruplex (G4) DNA over duplex DNA. The binding of7withG1is predominantly enthalpy‐driven, and the binding stoichiometry of7withG1is one for the tight‐binding event as determined by ESI mass spectrometry. A plausible binding mode is a synergistic effect of end‐stacking and groove interactions, as indicated by docking studies. Compound7can inhibit human telomerase activity at low micromolar concentrations, which is more potent than previously reported 5‐substituted phenanthroline derivatives.