• Proceedings of the Korean Society of Crop Science Conference
• /
• 2022.10a
• /
• pp.169-169
• /
• 2022

Over the past 100 years, the global average temperature has risen by 0.75 ℃. The Korean Peninsula has risen by 1.8 ℃, more than twice the global average. According to the RCP 8.5 scenario, the CO₂ concentration in 2100 will be 940 ppm, about twice as high as current. The National Institute of Crop Science(NICS) is using the SPAR (Soil-Plant Atmosphere Research) facility that can precisely control the environment, such as temperature, humidity, and CO₂. A Python-based colony photosynthesis algorithm has been developed, and the carbon and nitrogen absorption rate of rice is evaluated by setting climate change conditions. In this experiment, Oryza Sativa cv. Shindongjin were planted at the SPAR facility on June 10 and cultivated according to the standard cultivation method. The temperature and CO₂ settings are high temperature and high CO₂ (current temperature+4.7℃ temperature+4.7℃·CO₂ 800ppm), high temperature single condition (current temperature+4.7℃·CO₂ 400ppm) according to the RCP8.5 scenario, Current climate is set as (current temperature·CO₂400ppm). For colony photosynthesis measurement, a LI-820 CO₂ sensor was installed in each chamber for setting the CO₂ concentration and for measuring photosynthesis, respectively. The colony photosynthetic rate in the booting stage was greatest in a high temperature and CO₂ environment, and the higher the nitrogen fertilization level, the higher the colony photosynthetic rate tends to be. The amount of photosynthesis tended to decrease under high temperature. In the high temperature and high CO₂ environment, seed yields, the number of an ear, and 1000 seed weights tended to decrease compared to the current climate. The number of an ear also decreased under the high temperature. But yield tended to increase a little bit under the high temperature and high CO₂ condition than under the high temperature. In addition, In addition to this study, it seems necessary to comprehensively consider the relationship between colony photosynthetic ability, metabolite reaction, and rice yield according to climate change.

• Journal of Life Science
• /
• v.21 no.7
• /
• pp.1032-1038
• /
• 2011

Recently identified Ciona intestinalis voltage sensor-containing phosphatase (Ci-VSP) consists of an ion channel-like transmembrane domain (VSD) and a phosphatase-like domain. Ci-VSP senses the change of membrane potential by its VSD and works as a phosphoinositide phosphatase by its phosphatase domain. In this study, we present the construction of His-tagged phosphatase-like domain of Ci-VSP, its recombinant expression and purification, and its enzymatic activity behavior in order to examine the biochemical behavior of phosphatase domain of Ci-VSP without interference. We found that Ci-VSP(248-576)-His can be eluted with an elution buffer containing 25 mM NaCl and 100 mM imidazole during His-tag purification. In addition, we found the proper measurement condition for kinetics study of Ci-VSP(248-576)-His against p-nitrophenyl phosphate (pNPP). We measured the kinetic constant of Ci-VSP(248-576)-His at $37^{\circ}C$, pH 5.0 or 5.5, under 30 min of reaction time, and less than $2.0\;{\mu}g$ of protein amount. With these conditions, we acquired that Ci-VSP(248-576)-His has $K_m$ of $354{\pm}0.143\;{\mu}M$, $V_{max}$ of $0.0607{\pm}0.0137\;{\mu}mol$/min/mg and $k_{cat}$ of $0.359{\pm}0.009751\;min^{-1}$ for pNPP dephosphorylation. Therefore, we produced a pure form of Ci-VSP(248-576)-His, and this showed a higher activity against pNPP. This purified protein will provide the road to a structural investigation on an interesting protein, Ci-VSP.

• Journal of Internet Computing and Services
• /
• v.23 no.5
• /
• pp.145-154
• /
• 2022

Automatic Target Recognition (ATR) technology is emerging as a core technology of Future Combat Systems (FCS). Conventional ATR is performed based on IMINT (image information) collected from the SAR sensor, and various image-based deep learning models are used. However, with the development of IT and sensing technology, even though data/information related to ATR is expanding to HUMINT (human information) and SIGINT (signal information), ATR still contains image oriented IMINT data only is being used. In complex and diversified battlefield situations, it is difficult to guarantee high-level ATR accuracy and generalization performance with image data alone. Therefore, we propose a knowledge graph-based ATR method that can utilize image and text data simultaneously in this paper. The main idea of the knowledge graph and deep model-based ATR method is to convert the ATR image and text into graphs according to the characteristics of each data, align it to the knowledge graph, and connect the heterogeneous ATR data through the knowledge graph. In order to convert the ATR image into a graph, an object-tag graph consisting of object tags as nodes is generated from the image by using the pre-trained image object recognition model and the vocabulary of the knowledge graph. On the other hand, the ATR text uses the pre-trained language model, TF-IDF, co-occurrence word graph, and the vocabulary of knowledge graph to generate a word graph composed of nodes with key vocabulary for the ATR. The generated two types of graphs are connected to the knowledge graph using the entity alignment model for improvement of the ATR performance from images and texts. To prove the superiority of the proposed method, 227 documents from web documents and 61,714 RDF triples from dbpedia were collected, and comparison experiments were performed on precision, recall, and f1-score in a perspective of the entity alignment..