In the present study we first report in Korea the identification and characterization of Fusarium oxysporum isolated from rotten stems and roots of paprika (Capsicum annuum var. grossum) at Masan, Kyungsangnamdo in 2006. The fungal species produced white aerial mycelia accompanying with dark violet pigment on PDA. The optimal temperature and pH for the growth of the species was 25℃ and pH 7, respectively. Microscopic observation of one of isolates of the species shows that its conidiophores are unbranched and monophialides, its microconidia have oval-ellipsoidal shape with no septate and are of 3.0~11 × 1.5~3.5 µm sizes, its macroconidia are of 15~20 × 2.0~3.5 µm sizes and have slightly curved or slender shape with 2~3 septate. The results of molecular analysis show that the ITS rDNA of F. oxysporum from paprika shares 100% sequence identity with that of known F. oxysporum isolates. The identified species proved it's pathogenicity by causing rotting symptom when it was inoculated on paprika fruits. The growth of F. oxysporum from paprika was suppressed on PDA by agrochemicals such as benomyl, tebuconazole and azoxystrobin. The identified species has the ability of producing extracelluar enzymes that degrade cellobiose and pectin.

Instruction Tuning on Large Language Models is an essential process for model to function well and achieve high performance in specific tasks. Accordingly, in mainstream languages such as English, instruction-based datasets are being constructed and made publicly available. In the case of Korean, publicly available models and datasets all rely on using the output of ChatGPT or translating datasets built in English. In this paper, We introduce KIT-19 as an instruction dataset for the development of LLM in Korean. KIT-19 is a dataset created in an instruction format, comprising 19 existing open-source datasets for Korean NLP tasks. In this paper, we train a Korean Pretrained LLM using KIT-19 to demonstrate its effectiveness. The experimental results show that the model trained on KIT-19 significantly outperforms existing Korean LLMs. Based on the its quality and empirical results, this paper proposes that KIT-19 has the potential to make a substantial contribution to the future improvement of Korean LLMs' performance.

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