"Teach AI How to Code": Using Large Language Models as Teachable Agents for Programming Education

This work investigates large language models (LLMs) as teachable agents for learning by teaching (LBT). LBT with teachable agents helps learners identify their knowledge gaps and discover new knowledge. However, teachable agents require expensive programming of subject-specific knowledge. While LLMs as teachable agents can reduce the cost, LLMs' over-competence as tutees discourages learners from teaching. We propose a prompting pipeline that restrains LLMs' competence and makes them initiate "why" and "how" questions for effective knowledge-building. We combined these techniques into TeachYou, an LBT environment for algorithm learning, and AlgoBo, an LLM-based tutee chatbot that can simulate misconceptions and unawareness prescribed in its knowledge state. Our technical evaluation confirmed that our prompting pipeline can effectively configure AlgoBo's problem-solving performance. Through a between-subject study with 40 algorithm novices, we also observed that AlgoBo's questions led to knowledge-dense conversations (effect size=0.73). Lastly, we discuss design implications, cost-efficiency, and personalization of LLM-based teachable agents

Mini Review: Potential Applications of Non-host Resistance for Crop Improvement

Plant breeding for disease resistance is crucial to sustain global crop production. For decades, plant breeders and researchers have extensively used host plant resistance genes (R-genes) to develop disease resistant cultivars. However, the general instability of R-genes in crop cultivars when challenged with diverse pathogen populations emphasizes the need for more stable means of resistance. Alternatively, nonhost resistance is recognized as the most durable, broad-spectrum form of resistance against the majority of potential pathogens in plants and has gained great attention as an alternative target for managing resistance. While transgenic approaches have been utilized to transfer nonhost resistance to host species, conventional breeding applications have been more elusive. Nevertheless, avenues for discovery and deployment of genetic loci for nonhost resistance via hybridization are increasingly abundant, particularly when transferring genes among closely related species. In this mini review, we discuss current and developing applications of nonhost resistance for crop improvement with a focus on the overlap between host and nonhost mechanisms and the potential impacts of new technology

Coronatine inhibits stomatal closure and delays hypersensitive response cell death induced by nonhost bacterial pathogens

Pseudomonas syringae is the most widespread bacterial pathogen in plants. Several strains of P. syringae produce a phytotoxin, coronatine (COR), which acts as a jasmonic acid mimic and inhibits plant defense responses and contributes to disease symptom development. In this study, we found that COR inhibits early defense responses during nonhost disease resistance. Stomatal closure induced by a nonhost pathogen, P. syringae pv. tabaci, was disrupted by COR in tomato epidermal peels. In addition, nonhost HR cell death triggered by P. syringae pv. tabaci on tomato was remarkably delayed when COR was supplemented along with P. syringae pv. tabaci inoculation. Using isochorismate synthase (ICS)-silenced tomato plants and transcript profiles of genes in SA- and JA-related defense pathways, we show that COR suppresses SA-mediated defense during nonhost resistance

Development of a prediction system for precipitation- and wind-causing typhoons affecting the Korean peninsula using observational data

Introduction: When the forecasted typhoon track differs from the numerical model’s prediction, the estimated precipitation and wind from the model may not be reliable. Typically, forecasters receive numerical model forecasts with a delay of 4 h or more in calculation time. However, a more timely reference of precipitation and wind forecasts is required in an emergency with an approaching typhoon. Analyses of the observational data of typhoon-related characteristics, such as heavy rainfall and strong winds, from 1997 to 2021 revealed that their distribution areas are considerably affected by typhoon tracks. In this study, we developed a precipitation and wind prediction system based on the observational data of the typhoons that affected the Korean Peninsula.Methods: Typhoon tracks were categorized into west-coast landfalls, southeast landfalls, and those passing the Korea Strait. Each category affects the Korean Peninsula differently in terms of rainfall and wind. We devised a system that predicts these patterns based on incoming typhoon tracks. We can make forecasts by comparing the approaching typhoons to previous instances and analyzing their center, movement direction, and size. Observations from these past typhoons were averaged to produce a forecast grid for each new typhoon.Results: Our system, validated from 2019 to 2022, showed a wind speed root-mean-square error of 3.37 m/s and a precipitation accuracy index of 0.72. For comparison, traditional numerical models yielded 5.04 m/s and 0.75, respectively. This indicates that our system is comparably efficient and computationally less demanding.Discussion: Our system’s strength is its ability to offer real-time typhoon forecasts, often faster than numerical models. However, its dependence on historical data limits its predictive power for atypical weather scenarios. It is essential to consider integrating ensemble models with these observations for enhanced accuracy. Since 2022, this system has been operational at the Korea Meteorological Administration, showing consistent reliability in forecasting

Beyond the Standard Model B-parameters with improved staggered fermions in Nf=2+1N_f=2+1 QCD

We calculate the kaon mixing B-parameters for operators arising generically in theories of physics beyond the standard model. We use HYP-smeared improved staggered fermions on the $N_f = 2+1$ MILC asqtad lattices. Operator matching is done perturbatively at one-loop order. Chiral extrapolations are done using "golden combinations" in which one-loop chiral logarithms are absent. For the combined sea-quark mass and continuum extrapolation, we use three lattice spacings: $a \approx 0.045, 0.06$ and $0.09 \text{fm}$. Our results have a total error of 5-6%, which is dominated by the systematic error from matching and continuum extrapolation. For two of the BSM $B$-parameters, we agree with results obtained using domain-wall and twisted-mass dynamical fermions, but we disagree by $(4-5)\sigma$ for the other two.Comment: 7 pages, 5 figures, Lattice 2013 Proceedin

GENERAL CONTROL NONREPRESSIBLE4 Degrades 14-3-3 and the RIN4 Complex to Regulate Stomatal Aperture with Implications on Nonhost Disease Resistance and Drought Tolerance

Plants have complex and adaptive innate immune responses against pathogen infections. Stomata are key entry points for many plant pathogens. Both pathogens and plants regulate stomatal aperture for pathogen entry and defense, respectively. Not all plant proteins involved in stomatal aperture regulation have been identified. Here, we report GENERAL CONTROL NONREPRESSIBLE4 (GCN4), an AAA+-ATPase family protein, as one of the key proteins regulating stomatal aperture during biotic and abiotic stress. Silencing of GCN4 in Nicotiana benthamiana and Arabidopsis thaliana compromises host and nonhost disease resistance due to open stomata during pathogen infection. AtGCN4 overexpression plants have reduced H+-ATPase activity, stomata that are less responsive to pathogen virulence factors such as coronatine (phytotoxin produced by the bacterium Pseudomonas syringae) or fusicoccin (a fungal toxin produced by the fungus Fusicoccum amygdali), reduced pathogen entry, and enhanced drought tolerance. This study also demonstrates that AtGCN4 interacts with RIN4 and 14-3-3 proteins and suggests that GCN4 degrades RIN4 and 14-3-3 proteins via a proteasome-mediated pathway and thereby reduces the activity of the plasma membrane H+-ATPase complex, thus reducing proton pump activity to close stomata

Formate Dehydrogenase (FDH1) Localizes to Both Mitochondria and Chloroplast to Play a Role in Host and Nonhost Disease Resistance

Nonhost disease resistance is the most common type of plant defense mechanism against potential pathogens. In this study, the metabolic enzyme formate dehydrogenase (FDH1) was identified to be involved in nonhost disease resistance in Nicotiana benthamiana and Arabidopsis thaliana. In Arabidopsis, AtFDH1 was highly upregulated in response to both host and nonhost bacterial pathogens. Arabidopsis Atfdh1 mutants were compromised in nonhost resistance, basal resistance, and gene-for-gene resistance. The expression patterns of salicylic acid (SA) and jasmonic acid (JA) marker genes after pathogen infections in Atfdh1 mutant indicated that SA is most likely involved in the FDH1-mediated plant defense response to both host and nonhost bacterial pathogens. Previous studies reported that FDH1 localizes to only mitochondria, or both mitochondria and chloroplasts. Our results showed that the AtFDH1 localized to mitochondria and the amount of FDH1 localized to mitochondria increased upon infection with host or nonhost pathogens. Interestingly, the subcellular localization of FDH1 was observed in both mitochondria and chloroplasts after infection with a nonhost pathogen in Arabidopsis. We speculate that FDH1 plays a role in cellular signaling networks between mitochondria and chloroplasts to produce coordinated defense responses such as SA-induced reactive oxygen species (ROS) generation and hypersensitive response (HR)-induced cell death against nonhost bacterial pathogens