• The Korea Journal of Herbology
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• v.33 no.2
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• pp.29-43
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• 2018

Objectives : Araliae Continentalis Radix(AC) is a medicinal herb belonging to the drug efficacy group treating musculoskeletal disorders(MSD) with anti-inflammatory, analgesic and antipyretic action. However, due to morphologic and onomastic similarity, adulterants(Angelicae Pubescentis Radix: AP, Gypsophilae Oldhamianae Radix: GO, Levistici Officinalis Radix: LO) have been included or replaced the standard. Methods : Multilateral methods were carried out on the identification of AC and its adulterants. Macroscopic and microscopic characteristics were observed by using stereoscope and microscope. For the comparison of chromatogram pattern, standard compounds were analyzed simultaneously using high performance liquid chromatography. Results : 1. The macroscopic identification of original plants was determined by the phyllotaxis type, the inflorescence type, the leaf margin and the color of flowers. The macroscopic identification of herbal materials was examined by oil spots, the cambium, heteromorphic vascular bundles, and the pholem. 2. For the microscopic identification, the fact whether its xylem ray is proliferated or not was first determined. Then medicinal herbs were secondly divided by cellular inclusions, fiber bundles, the distribution of secretary canals and the shape of cambium. 3. AC and its adulterants showed different chromatographic fingerprints. AC was containing continentalic acid and kaurenoic acid. AP was containing osthole and columbianadin. LO was containing osthole and falcarindiol. None of the compounds were found in GO. Conclusions : This recent identification keys of might be helpful to discriminate the pharmacopoeia standard and its adulterants for the right usage in clinics.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.270-272
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• 2022

Safety is one of the factors that prevent clinical drugs from being distributed on the market. In the case of neurotoxicity, which is the main cause of safety problems caused by drug side effects, risk assessment of drugs and compounds is required in advance. Currently, experiments for testing drug safety are based on animal experimetns, which have the disadvantage of being time-consuming and expensive. Therefore in order to solve the above problem, a neurotoxic prediction model through an in silico experiment was suggested. In this study, the category of neurotoxicity was expanded using a unified medical language system and various related compound data were obtained based on an integrated database. The SMILES (Simplified Molecular Input Line Entry System) of the obtained compounds were converted into fingerprints and it is used as input of machine learning. The model finally predicts the presence or absence of neurotoxicity. The experiment proposed in this study can reduce the time and cost required for the in vivo experiment. Furthermore, it is expected to shorten the research period for new drug development and reduce the burden of suspension of development.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.2
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• pp.91-100
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• 2023

The existing indoor localization method using Wi-Fi fingerprinting has a high collection cost and relatively low accuracy, thus requiring integrated correction of convergence with other technologies. This paper proposes a new method that significantly reduces collection costs compared to existing methods using Wi-Fi fingerprinting. Furthermore, it does not require labeling of data at collection and can estimate pedestrian travel paths even in large indoor spaces. The proposed pedestrian movement path estimation process is as follows. Data collection is accomplished by setting up a feature area near an indoor space intersection, moving through the set feature areas, and then collecting data without labels. The collected data are processed using Kernel Linear Discriminant Analysis (KLDA) and the valley point of the Euclidean distance value between two data is obtained within the feature space of the data. We build learning data by labeling data corresponding to valley points and some nearby data by feature area numbers, and labeling data between valley points and other valley points as path data between each corresponding feature area. Finally, for testing, data are collected randomly through indoor space, KLDA is applied as previous data to build test data, the K-Nearest Neighbor (K-NN) algorithm is applied, and the path of movement of test data is estimated by applying a correction algorithm to estimate only routes that can be reached from the most recently estimated location. The estimation results verified the accuracy by comparing the true paths in indoor space with those estimated by the proposed method and achieved approximately 90.8% and 81.4% accuracy in two experimental spaces, respectively.

• Analytical Science and Technology
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• v.20 no.4
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• pp.339-346
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• 2007

To search the ninhydrin derivatives that have high chromogenic and fluorogenic properties, molecular holographic quantitative structure property relationship (HQSPR) models on the reactivity between glycine and ninhydrin analogues as latent fingerprint detector were derived and investigated quantitatively. The ${\varepsilon}LUMO$ (e.v.) energy of ninhydrin molecule was an important factor to reactivity of ninhydrin. And, it is suggested that the nucleophilic reaction by orbital-controlled reaction from the frontier molecular orbital (FMO) interaction between glycine and ninhydrin derivatives was more superior than that of electrophilic reaction by charged controlled reaction. The analytical results in atomic contribution maps also shows that the reactivity of ninhydrin was increased by meta-substituents as strong electron withdrawing groups on the benzo ring. Therefore, it is sugested by HQSPR and QSPR model that the 5,6-dinitroninhydrin molecule would increase the reactivity as much as three times as compared to none substituted ninhydrin molecule.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.214-214
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• 2022

Soluble starch synthases (SSs) elongate α-glucans from ADP-Glc to the glucan nonreducing ends and play a critical role in synthesizing resistant starch in the rice. A total of 10 SSs isoforms were reported in rice, including granules-bound starch synthase I (GBSSI), GBSSII, starch synthase I (SSI), SSIIa (SSII-3), SSIIb (SSII-2), SSIIc (SSII-1), SSIIIa (SSIII-2), SSIIIb (SSIII-1), SSIVa (SSIV-1), and SSIVb (SSIV-2). SSIII proteins are involved in forming the B chain and elongating cluster filling chains in amylopectin metabolism. The functions of SSIIIb (SSIII-1) are less clear as compared to SSs. Here, we sought to shed light on the genetic diversity profiling of the SSIII-1 gene in 374 rice accessions composed of 54 wild-type accessions and 320 bred cultivars (temperate japonica, indica, tropical japonica, aus, aromatic, and admixture). In total, 17 haplotypes were identified in the SSIII-1 coding region of 320 bred cultivars, while 44 haplotypes were detected from 54 wild-type accessions. The genetic diversity indices revealed the most negative Tajima's D value in the temperate-japonica, followed by the wild type, while Tajima's D values in other ecotypes were positive, indicating balancing selection. Nucleotide diversity in the SSIII-1 region was highest in the wild group (0.0047) while lowest in temperate-japonica. Lower nucleotide diversity in the temperate-japonica is evidenced by the negative Tajima's D and suggested purifying selection. The fixation index (F_ST) revealed a very high level of gene flow (low F_ST) between the tropical-japonica and admixture groups (F_ST=-0.21) followed by admixture and wild groups (-0.04), indica and admixture groups (0.02), while low gene flow with higher F_ST estimates between the temperate-japonica and aus groups (0.72), tropical-japonica and aromatic groups (0.71), and temperate-japonica and admixture groups (0.52). Taken together, our study offers insights into haplotype diversity and evolutionary fingerprints of SSIII-1. It provides genomic information to increase the resistant starch content of cooked rice.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.219-219
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• 2022

Granule-bound starch synthase I (GBSSI), encoded by the waxy gene, is responsible for the accumulation of amylose during the development of starch granules in rice endosperm. Despite many findings on waxy alleles, the genetic diversity and evolutionary studies are still not fully explored regarding their functional effects. Comprehensive evolutionary analyses were performed to investigate the genetic variations and relatedness of the GBSSI gene in 374 rice accessions composed of 54 wild accessions and 320 bred cultivars (temperate japonica, tropical japonica, indica, aus, aromatic, and admixture). GBSS1 coding regions were analyzed from a VCF file retrieved from whole-genome resequencing data, and eight haplotypes were identified in the GBSSI coding region of 320 bred cultivars. The genetic diversity indices revealed the most negative Tajima's D value in the tropical-japonica, followed by the aus and temperate-japonica, while Tajima's D values in indica were positive, indicating balancing selection. Diversity reduction was noticed in temperate japonica (0.0003) compared to the highest one (wild, 0.0044), illustrating their higher genetic differentiation by F_ST-value (0.604). The most positive Tajima's D value was observed in indica (0.5224), indicating the GBSSI gene domestication signature under balancing selection. In contrast, the lowest and negative Tajima's D value was found in tropical japonica (-0.5291), which might have experienced a positive selection and purified due to the excess of rare alleles. Overall, our study offers insights into haplotype diversity and evolutionary fingerprints of GBSSI. It ako provides genomic information to increase the starch content of cooked rice.

• Food Science of Animal Resources
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• v.43 no.6
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• pp.1055-1066
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• 2023

The cultured meat industry is continuously evolving due to the collective efforts of cultured meat companies and academics worldwide. Though still technologically limited, recent reports of regulatory approvals for cultured meat companies have initiated the standards-based approach towards cultured meat production. Incidents of deception in the meat industry call for fool-proof authentication methods to ensure consumer safety, product quality, and traceability. The cultured meat industry is not exempt from the threats of food fraud. Meat authentication techniques based on DNA, protein, and metabolite fingerprints of animal meat species needs to be evaluated for their applicability to cultured meat. Technique-based categorization of cultured meat products could ease the identification of appropriate authentication methods. The combination of methods with high sensitivity and specificity is key to increasing the accuracy and precision of meat authentication. The identification of markers (both physical and biochemical) to differentiate conventional meat from cultured meat needs to be established to ensure overall product traceability. The current review briefly discusses some areas in the cultured meat industry that are vulnerable to food fraud. Specifically, it targets the current meat and meat product authentication tests to emphasize the need for ensuring the traceability of cultured meat.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.434-442
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• 2023

The relationship between acute kidney injury (AKI) prediction and nephrotoxic drugs, or drugs that adversely affect kidney function, is one that has yet to be explored in the critical care setting. One contributing factor to this gap in research is the limited investigation of drug modalities in the intensive care unit (ICU) context, due to the challenges of processing prescription data into the corresponding drug representations and a lack in the comprehensive understanding of these drug representations. This study addresses this gap by proposing a novel approach that leverages patient prescription data as a modality to improve existing models for AKI prediction. We base our research on Electronic Health Record (EHR) data, extracting the relevant patient prescription information and converting it into the selected drug representation for our research, the extended-connectivity fingerprint (ECFP). Furthermore, we adopt a unique multimodal approach, developing machine learning models and 1D Convolutional Neural Networks (CNN) applied to clinical drug representations, establishing a procedure which has not been used by any previous studies predicting AKI. The findings showcase a notable improvement in AKI prediction through the integration of drug embeddings and other patient cohort features. By using drug features represented as ECFP molecular fingerprints along with common cohort features such as demographics and lab test values, we achieved a considerable improvement in model performance for the AKI prediction task over the baseline model which does not include the drug representations as features, indicating that our distinct approach enhances existing baseline techniques and highlights the relevance of drug data in predicting AKI in the ICU setting.

• The Journal of the Acoustical Society of Korea
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• v.25 no.4
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• pp.151-158
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• 2006

In this paper, we proposed a new audio fingerprint extraction method, based on Philips' music retrieval algorithm, which uses the energy difference of neighboring filter-bank and probabilistic characteristics of music. Since Philips method uses too many filter-banks in limited frequency band, it may cause audio fingerprints to be highly sensitive to additive noises and to have too high correlation between neighboring bands. The proposed method improves robustness to noises by reducing the number of filter-banks while it maintains the discriminative power by representing the energy difference of bands with 2 bits where the quantization levels are determined by probabilistic characteristics. The correlation which exists among 4 different levels in 2 bits is not only utilized in similarity measurement. but also in efficient reduction of searching area. Experiments show that the proposed method is not only more robust to various environmental noises (street, department, car, office, and restaurant), but also takes less time for database search than Philips in the case where music is highly degraded.

• Journal of the Korean Society for Marine Environment & Energy
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• v.15 no.3
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• pp.247-256
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• 2012

Stable carbon isotope ratio of oil components are known to be unaffected by weathering processes and thus has been widely used to determine the origin of spilled oil. In this study, molecular index and composition of stable carbon isotope in 15 crude oils and petroleum product were analyzed and used as oil fingerprints to determine the discriminating power of each fingerprinting method among target crude oils. Through the fingerprints of alkane distribution only Bintulu and B-C(1%) were distinguishable from other crude oils. The pristane/phytane ratio can classify the crude oils into three groups but differentiation of crude oils within a group was impossible using the ratio. The crude oils of A.L., A.S.L., Foroozan and B-C(1%) were differentiated from the other oils using PAH source recognition indexes of C2D/C2P and C3D/C3P. The usage of 4-mD/1-mD and 2/3-mD/1-mD ratio was able to distinguish A.S.L., Bintulu and Oman from the other crude oils. However the PAH source recognition ratios in the other crude oils were similar and thus they were impractical to be used for source identification among the target crude oils. Stable carbon isotope ratios of alkanes were able to uniquely specify each crude oil in the plot of ${\delta}^{13}C_{C21}$ and ${\delta}^{13}C_{C25}$ except A.L., A.M., Qatar-Marine, B-C(1%). The oil fingerprinting method using stable carbon isotope ratios of individual alkane compounds showed more discriminating power among the target crude oils than the conventional source recognition indexes of PAHs or alkanes.