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Every animal secretes mucus, placing them among the most diverse biological materials. Mucus hydrogels are complex mixtures of water, ions, carbohydrates, and proteins. Uncertainty surrounding their composition and how interactions between components contribute to mucus function complicates efforts to exploit their properties. There is substantial interest in commercializing mucus from the garden snail,Cornu aspersum, for skincare, drug delivery, tissue engineering, and composite materials.C. aspersumsecretes three mucus—one shielding the animal from environmental threats, one adhesive mucus from the pedal surface of the foot, and another pedal mucus that is lubricating. It remains a mystery how compositional differences account for their substantially different properties. Here, we characterize mucus proteins, glycosylation, ion content, and mechanical properties that could be used to provide insight into structure-function relationships through an integrative “mucomics” approach. We identify macromolecular components of these hydrogels, including a previously unreported protein class termed Conserved Anterior Mollusk Proteins (CAMPs). Revealing differences betweenC. aspersummucus shows how considering structure at all levels can inform the design of mucus-inspired materials.

 

Large language models (LLMs) encode parametric knowledge about world facts and have shown remarkable performance in knowledge-driven NLP tasks. However, their reliance on parametric knowledge may cause them to overlook contextual cues, leading to incorrect predictions in context-sensitive NLP tasks (e.g., knowledge acquisition tasks). In this paper, we seek to assess and enhance LLMs’ contextual faithfulness in two aspects: knowledge conflict and prediction with abstention. We demonstrate that LLMs’ faithfulness can be significantly improved using carefully designed prompting strategies. In particular, we identify opinion-based prompts and counterfactual demonstrations as the most effective methods. Opinion-based prompts reframe the context as a narrator’s statement and inquire about the narrator’s opinions, while counterfactual demonstrations use instances containing false facts to improve faithfulness in knowledge conflict situations. Neither technique requires additional training. We conduct experiments on three datasets of two standard NLP tasks, machine reading comprehension and relation extraction, and the results demonstrate significant improvement in faithfulness to contexts. Code and data are released at https://github.com/wzhouad/context-faithful-llm. 

We introduce a proper notion of two-dimensional signature for images. This object is inspired by the so-called rough paths theory, and it captures many essential features of a two-dimensional object such as an image. It thus serves as a low-dimensional feature for pattern classification. Here, we implement a simple procedure for texture classification. In this context, we show that a low-dimensional set of features based on signatures produces an excellent accuracy. 

Reinforcement learning is a general technique that allows an agent to learn an optimal policy and interact with an environment in sequential decision-making problems. The goodness of a policy is measured by its value function starting from some initial state. The focus of this paper was to construct confidence intervals (CIs) for a policy’s value in infinite horizon settings where the number of decision points diverges to infinity. We propose to model the action-value state function (Q-function) associated with a policy based on series/sieve method to derive its confidence interval. When the target policy depends on the observed data as well, we propose a SequentiAl Value Evaluation (SAVE) method to recursively update the estimated policy and its value estimator. As long as either the number of trajectories or the number of decision points diverges to infinity, we show that the proposed CI achieves nominal coverage even in cases where the optimal policy is not unique. Simulation studies are conducted to back up our theoretical findings. We apply the proposed method to a dataset from mobile health studies and find that reinforcement learning algorithms could help improve patient’s health status. A Python implementation of the proposed procedure is available at https://github.com/shengzhang37/SAVE.

 

Chemical defense systems involving tryptophan-derived secondary metabolites (TDSMs) and salicylic acid (SA) are induced by general nonself signals and pathogen signals, respectively, in Arabidopsis thaliana. Whether and how these chemical defense systems are connected and balanced is largely unknown. In this study, we identified the AVRRPT2-INDUCED GENE2A (AIG2A) and AIG2B genes as gatekeepers that prevent activation of SA defense systems by TDSMs. These genes also were identified as important contributors to natural variation in disease resistance among A. thaliana natural accessions. The loss of AIG2A and AIG2B function leads to upregulation of both SA and TDSM defense systems. Suppressor screens and genetic analysis revealed that a functional TDSM system is required for the upregulation of the SA pathway in the absence of AIG2A and AIG2B, but not vice versa. Furthermore, the AIG2A and AIG2B genes are co-induced with TDSM biosynthesis genes by general pathogen elicitors and nonself signals, thereby functioning as a feedback control of the TDSM defense system, as well as limiting activation of the SA defense system by TDSMs. Thus, this study uncovers an AIG2A- and AIG2B-mediated mechanism that fine-tunes and balances SA and TDSM chemical defense systems in response to nonpathogenic and pathogenic microbes.