Identification of diacetonamine from soybean curd residue as a sporulation-inducing factor toward Bacillus spp.

Under bioassay-guided investigation, a sporulation-inducing factor (SIF) toward Bacillus spp. was searched for in methanol (MeOH) extracts of soybean curd residues, and diacetonamine (1) was identified as the active compound. SIF was first isolated as a monoacetylated derivative (2, 4.1 mg from 655 g soybean curd residues), and its chemical structure was elucidated by field desorption mass spectrometry, electron ionization mass spectrometry, and nuclear magnetic resonance (NMR) analyses. After 48-h incubation, 40 mu M diacetonamine hydrochloride (1b) exhibited sporulation-inducing activity with 35% sporulation frequency toward a Bacillus amyloliquefaciens wild-type strain (AHU 2170), whereas 40 mu M diacetone acrylamide (3) showed 99% sporulation induction, which was much higher than that of 1b. Although Bacillus megaterium NBRC 15308 was sporulated by the treatment with 400 mu M 1b with 36 and 70% sporulation frequency after 72-and 96-h incubation respectively, 3 at the same concentration showed only 2% sporulation after 72-h incubation. Hence, diacetonamine (1) was characterized as a genuine SIF from soybean curd residues, but it was uncertain whether 1 is a natural product or an artifact. Spores of B. amyloliquefaciens induced by 1b survived after treatment with heating at 95 degrees C for 10 min, also suggesting that 1 is genuine SIF in soybean curd residue. As sporulation induction is likely linked to activation of antibiotic production in some spore-forming Firmicutes bacteria, compound 1 would be a possible chemical tool to develop an effective fermentation technology in Bacillus species

Exploring the Potential of Large Language models in Traditional Korean Medicine: A Foundation Model Approach to Culturally-Adapted Healthcare

Introduction: Traditional Korean medicine (TKM) emphasizes individualized diagnosis and treatment, making AI modeling difficult due to limited data and implicit processes. GPT-3.5 and GPT-4, large language models, have shown impressive medical knowledge despite lacking medicine-specific training. This study aimed to assess the capabilities of GPT-3.5 and GPT-4 for TKM using the Korean National Licensing Examination for Korean Medicine Doctors. Methods: GPT-3.5 (February 2023) and GPT-4 (March 2023) models answered 340 questions from the 2022 examination across 12 subjects. Each question was independently evaluated five times in an initialized session. Results: GPT-3.5 and GPT-4 achieved 42.06% and 57.29% accuracy, respectively, with GPT-4 nearing passing performance. There were significant differences in accuracy by subjects, with 83.75% accuracy for neuropsychiatry compared to 28.75% for internal medicine (2). Both models showed high accuracy in recall-based and diagnosis-based questions but struggled with intervention-based ones. The accuracy for questions that require TKM-specialized knowledge was relatively lower than the accuracy for questions that do not GPT-4 showed high accuracy for table-based questions, and both models demonstrated consistent responses. A positive correlation between consistency and accuracy was observed. Conclusion: Models in this study showed near-passing performance in decision-making for TKM without domain-specific training. However, limits were also observed that were believed to be caused by culturally-biased learning. Our study suggests that foundation models have potential in culturally-adapted medicine, specifically TKM, for clinical assistance, medical education, and medical research.Comment: 31 pages, 6 figure

"Is the Pope Catholic?" Applying Chain-of-Thought Reasoning to Understanding Conversational Implicatures

Conversational implicatures are pragmatic inferences that require listeners to deduce the intended meaning conveyed by a speaker from their explicit utterances. Although such inferential reasoning is fundamental to human communication, recent research indicates that large language models struggle to comprehend these implicatures as effectively as the average human. This paper demonstrates that by incorporating Grice's Four Maxims into the model through chain-of-thought prompting, we can significantly enhance its performance, surpassing even the average human performance on this task

Annotation Imputation to Individualize Predictions: Initial Studies on Distribution Dynamics and Model Predictions

Annotating data via crowdsourcing is time-consuming and expensive. Owing to these costs, dataset creators often have each annotator label only a small subset of the data. This leads to sparse datasets with examples that are marked by few annotators; if an annotator is not selected to label an example, their opinion regarding it is lost. This is especially concerning for subjective NLP datasets where there is no correct label: people may have different valid opinions. Thus, we propose using imputation methods to restore the opinions of all annotators for all examples, creating a dataset that does not leave out any annotator's view. We then train and prompt models with data from the imputed dataset (rather than the original sparse dataset) to make predictions about majority and individual annotations. Unfortunately, the imputed data provided by our baseline methods does not improve predictions. However, through our analysis of it, we develop a strong understanding of how different imputation methods impact the original data in order to inform future imputation techniques. We make all of our code and data publicly available.Comment: 12 pages, 5 figure

Read, Revise, Repeat: A System Demonstration for Human-in-the-loop Iterative Text Revision

Revision is an essential part of the human writing process. It tends to be strategic, adaptive, and, more importantly, iterative in nature. Despite the success of large language models on text revision tasks, they are limited to non-iterative, one-shot revisions. Examining and evaluating the capability of large language models for making continuous revisions and collaborating with human writers is a critical step towards building effective writing assistants. In this work, we present a human-in-the-loop iterative text revision system, Read, Revise, Repeat (R3), which aims at achieving high quality text revisions with minimal human efforts by reading model-generated revisions and user feedbacks, revising documents, and repeating human-machine interactions. In R3, a text revision model provides text editing suggestions for human writers, who can accept or reject the suggested edits. The accepted edits are then incorporated into the model for the next iteration of document revision. Writers can therefore revise documents iteratively by interacting with the system and simply accepting/rejecting its suggested edits until the text revision model stops making further revisions or reaches a predefined maximum number of revisions. Empirical experiments show that R3 can generate revisions with comparable acceptance rate to human writers at early revision depths, and the human-machine interaction can get higher quality revisions with fewer iterations and edits. The collected human-model interaction dataset and system code are available at \url{https://github.com/vipulraheja/IteraTeR}. Our system demonstration is available at \url{https://youtu.be/lK08tIpEoaE}.Comment: Accepted by The First Workshop on Intelligent and Interactive Writing Assistants at ACL202

Story Visualization by Online Text Augmentation with Context Memory

Story visualization (SV) is a challenging text-to-image generation task for the difficulty of not only rendering visual details from the text descriptions but also encoding a long-term context across multiple sentences. While prior efforts mostly focus on generating a semantically relevant image for each sentence, encoding a context spread across the given paragraph to generate contextually convincing images (e.g., with a correct character or with a proper background of the scene) remains a challenge. To this end, we propose a novel memory architecture for the Bi-directional Transformers with an online text augmentation that generates multiple pseudo-descriptions as supplementary supervision during training, for better generalization to the language variation at inference. In extensive experiments on the two popular SV benchmarks, i.e., the Pororo-SV and Flintstones-SV, the proposed method significantly outperforms the state of the arts in various evaluation metrics including FID, character F1, frame accuracy, BLEU-2/3, and R-precision with similar or less computational complexity.Comment: ICCV 202

OASIS 2: online application for survival analysis 2 with features for the analysis of maximal lifespan and healthspan in aging research

Online application for survival analysis (OASIS) has served as a popular and convenient platform for the statistical analysis of various survival data, particularly in the field of aging research. With the recent advances in the fields of aging research that deal with complex survival data, we noticed a need for updates to the current version of OASIS. Here, we report OASIS 2 (http://sbi.postech.ac.kr/oasis2), which provides extended statistical tools for survival data and an enhanced user interface. In particular, OASIS 2 enables the statistical comparison of maximal lifespans, which is potentially useful for determining key factors that limit the lifespan of a population. Furthermore, OASIS 2 provides statistical and graphical tools that compare values in different conditions and times. That feature is useful for comparing age-associated changes in physiological activities, which can be used as indicators of "healthspan." We believe that OASIS 2 will serve as a standard platform for survival analysis with advanced and user-friendly statistical tools for experimental biologists in the field of aging research.1127Ysciescopu

Development of Rechargeable Seawater Battery Module

Rechargeable seawater batteries (SWBs) use Na+ ions dissolved in water (seawater or salt-water) as the cathode material. They are attracting attention for marine applications such as light buoys, marine drones, auxiliary power for sailing boats and so on. So far, SWB design has been developed from the coin-type to prismatic-shape cell for research purposes to investigate cell components and electrochemical behaviors. However, for commercial applications, that generally require >12 V and >15 W, the development of an SWB module is required, including cell assembly and packing design. The purpose of this work was to conduct research on the SWB cell assembly method while considering the SWB's properties and minimizing current imbalance. Additionally, a 5 Series (S) 4 Parallel (P) SWB module is constructed and validated using commercially available light buoys (12 V, 15 W)

Impact of the repurposed drug thonzonium bromide on host oral-gut microbiomes

Drug repurposing is a feasible strategy for the development of novel therapeutic applications. However, its potential use for oral treatments and impact on host microbiota remain underexplored. Here, we assessed the influences of topical oral applications of a repurposed FDA-approved drug, thonzonium bromide, on gastrointestinal microbiomes and host tissues in a rat model of dental caries designed to reduce cross-contamination associated with coprophagy. Using this model, we recapitulated the body site microbiota that mirrored the human microbiome profile. Oral microbiota was perturbed by the treatments with specific disruption of Rothia and Veillonella without affecting the global composition of the fecal microbiome. However, disturbances in the oral-gut microbial interactions were identified using nestedness and machine learning, showing increased sharing of oral taxon Sutterella in the gut microbiota. Host-tissue analyses revealed caries reduction on teeth by thonzonium bromide without cytotoxic effects, indicating bioactivity and biocompatibility when used orally. Altogether, we demonstrate how an oral treatment using a repurposed drug causes localized microbial disturbances and therapeutic effects while promoting turnover of specific oral species in the lower gut in vivo

Food-derived sensory cues modulate longevity via distinct neuroendocrine insulin-like peptides

Environmental fluctuations influence organismal aging by affecting various regulatory systems. One such system involves sensory neurons, which affect life span in many species. However, how sensory neurons coordinate organismal aging in response to changes in environmental signals remains elusive. Here, we found that a subset of sensory neurons shortens Caenorhabditis elegans' life span by differentially regulating the expression of a specific insulin-like peptide (ILP), INS-6. Notably, treatment with food-derived cues or optogenetic activation of sensory neurons significantly increases ins-6 expression and decreases life span. INS-6 in turn relays the longevity signals to nonneuronal tissues by decreasing the activity of the transcription factor DAF-16/FOXO. Together, our study delineates a mechanism through which environmental sensory cues regulate aging rates by modulating the activities of specific sensory neurons and ILPs.1186Ysciescopu