• ALGAE
• /
• 제39권2호
• /
• pp.97-108
• /
• 2024

The Ralfsiaceae family, part of the Ralfsiales order and consisting of crustose brown algae, includes five genera: Analipus, Endoplura, Fissipedicella, Heteroralfsia, and Ralfsia. In this study, a novel crustose genus named Ramipedicella gen. nov. is introduced within the Ralfsiaceae based on molecular and morphological analyses. Phylogenetic analyses using both concatenated dataset (rbcL + COI-5P genes) and rbcL indicate that the crustose brown algae that we collected from Korea and Russia form a unique grouping within the Ralfsiaceae. This grouping is strongly supported by both bootstrap analysis and Bayesian posterior probabilities. The genetic differences in the rbcL and COI-5P sequences between Ramipedicella and other genera within Ralfsiaceae range from 6.7 to 9.3% for rbcL and from 15.5 to 20.8% for COI-5P. Ramipedicella is characterized by crustose thalli having new crusts growing on top of old ones with a hypothallial basal layer and erect perithallial filaments, long cells with width-to-length ratio of 1 : 1-16, single chloroplast per cell, plurangia with one to several sterile cells, one to several unangia produced from unicellular stalks or from the lateral-basal region to the paraphyses, and unangia arising sequencially in irregularly branched specialized filaments. Ramipedicella, the recently identified genus, comprises two distinct species. Ramipedicella miniloba, the type species, is distinguished by crusts with small lobes, numerous hair tufts, plurangia terminated by 1-4 sterile cells, and large oblong unangia. Ramipedicella longicellularis is identified by generally smooth crusts, absence of phaeophycean hairs, plurangia terminated by 1-2 apical sterile cells, and smaller mostly oblanceolate unangia.

• Genomics & Informatics
• /
• 제10권2호
• /
• pp.123-127
• /
• 2012

Gene set analysis (GSA) is useful in interpreting a genome-wide association study (GWAS) result in terms of biological mechanism. We compared the performance of two different GSA implementations that accept GWAS p-values of single nucleotide polymorphisms (SNPs) or gene-by-gene summaries thereof, GSA-SNP and i-GSEA4GWAS, under the same settings of inputs and parameters. GSA runs were made with two sets of p-values from a Korean type 2 diabetes mellitus GWAS study: 259,188 and 1,152,947 SNPs of the original and imputed genotype datasets, respectively. When Gene Ontology terms were used as gene sets, i-GSEA4GWAS produced 283 and 1,070 hits for the unimputed and imputed datasets, respectively. On the other hand, GSA-SNP reported 94 and 38 hits, respectively, for both datasets. Similar, but to a lesser degree, trends were observed with Kyoto Encyclopedia of Genes and Genomes (KEGG) gene sets as well. The huge number of hits by i-GSEA4GWAS for the imputed dataset was probably an artifact due to the scaling step in the algorithm. The decrease in hits by GSA-SNP for the imputed dataset may be due to the fact that it relies on Z-statistics, which is sensitive to variations in the background level of associations. Judicious evaluation of the GSA outcomes, perhaps based on multiple programs, is recommended.

• 정보처리학회논문지A
• /
• 제13A권1호
• /
• pp.79-86
• /
• 2006

최근 인간의 다양성과 SNP과의 연관연구에 드는 비용을 줄이기 위해서, 최소의 tagSNP을 선택하는 문제를 해결하기 위한 연구가 이루어지고 있다. 일반적으로 많은 수의 SNP들을 여러 블록으로 분할하여 각 블록 내에서 tagSNP을 선택하는 접근방법이 사용되고 있다. 본 논문에서 구현된 MarSel은 기존의 블록분할 접근 방법의 문제로 볼 수 있는 생물학적 의미의 부족을 해결하고자, 연관불균형(Linkage Disequilibrium, LD)의 개념을 도입한 시스템이다. 기존의 접근방법에서는 생물학적으로 재조합(recombination)이 일어나지 않는 연속된 구간에서도 여러 블록으로 나누어지는 문제가 생겼던 반면, MarSel에서는 연관불균형 계수 |D'|에 의해서 연속된 구간이 하나의 블록으로 유지된 상태에서 tagSNP을 선택하게 된다. 또한 MarSel에서는 각 블록 내에서 tagSNP을 선택 할 때에 엔트로피(entropy) 기반의 최적해 알고리즘을 이용함으로써 최소한의 tagSNP 선택을 보장하게 되며, 기존의 구현된 시스템들보다 더 많은 양의 데이터를 효율적으로 처리할 수 있도록 구현되었기 때문에 염색체 레벨의 연관 연구도 가능하게 해준다.

• Journal of Microbiology and Biotechnology
• /
• 제15권5호
• /
• pp.1028-1038
• /
• 2005

To elucidate phylogenetic relationships of Phellinus and its related genera, nuclear internal transcribed spacer and mitochondrial small subunit ribosomal DNA sequences from 65 strains were determined and compared. The combined dataset of two sequences increased informative characters and led to the production of trees with higher levels of resolution. Phylogenetic analysis of the combined dataset revealed thirteen evolutionary lineages and several unresolved species that were together subdivided into two large clusters consisting of oligonucleate species and binucleate species. These results coincided with previous cytological, morphological, and molecular studies. It is newly recognized that the Phellinus linteus complex forms a sister clade to Inonotus, and that Fulvifomes is somehow related to Inocutis. The Phellinus linteus complex of dimitic perennial taxa made an independent clade from Inonotus and suggested that hyphal miticity and fruitbody permanence had enough phylogenetic significance to keep the complex within the traditional genus Phellinus. Taxa lacking setae were clustered into Fulvifomes, Phylloporia, Inocutis, and Fomitiporia, and the first three were closely related sister groups, but Fomitiporia was a genus distantly related to them. Several taxa with branched setae were shown among distantly related genera. Molecular evidence indicated that the ancestral nuclear type could be a binucleate feature, and that there might be parallel gains of branched setae and parallel losses of setae in the Hymenochaetales.

• 대한의용생체공학회:의공학회지
• /
• 제38권5호
• /
• pp.237-241
• /
• 2017

Early detection of the pulmonary nodule is important for diagnosis and treatment of lung cancer. Recently, CT has been used as a screening tool for lung nodule detection. And, it has been reported that computer aided detection(CAD) systems can improve the accuracy of the radiologist in detection nodules on CT scan. The previous study has been proposed a method using Convolutional Neural Network(CNN) in Lung CAD system. But the proposed model has a limitation in accuracy due to its sparse layer structure. Therefore, we propose a Deep Convolutional Neural Network to overcome this limitation. The model proposed in this work is consist of 14 layers including 8 convolutional layers and 4 fully connected layers. The CNN model is trained and tested with 61,404 regions-of-interest (ROIs) patches of lung image including 39,760 nodules and 21,644 non-nodules extracted from the Lung Image Database Consortium(LIDC) dataset. We could obtain the classification accuracy of 91.79% with the CNN model presented in this work. To prevent overfitting, we trained the model with Augmented Dataset and regularization term in the cost function. With L1, L2 regularization at Training process, we obtained 92.39%, 92.52% of accuracy respectively. And we obtained 93.52% with data augmentation. In conclusion, we could obtain the accuracy of 93.75% with L2 Regularization and Data Augmentation.

• 한국정보과학회논문지:소프트웨어및응용
• /
• 제37권11호
• /
• pp.803-811
• /
• 2010

단백질의 세포내 위치와 단백질의 기능은 연관성이 크므로, 단백질의 세포내 위치 예측을 통해서 그 기능에 대한 정보를 얻을 수 있다. 예측 정확도를 높이기 위해서 아미노산 서열 정보이외의 외부 정보들을 효과적으로 이용하려는 연구가 활발하다. 본 논문에서는 아미노산 서열 유사성, 단백질 프로파일, 유전자 온톨로지, 모티프, 문헌 정보에 내재된 세포내 위치 예측 능력을 비교한다. 단백질간의 서열 유사성이 80% 이하인 PLOC 자료를 사용한 실험에서는 서열 유사성과 유전자 온톨로지를 이용하는 방법이 효과적이며, 94.8%의 예측정확도를 얻었다. 단백질 서열간의 유사성이 30% 이하로서 단백질간의 서열 유사성이 작은 BaCelLo IDS 자료는 유전자 온톨로지를 사용하는 것이 효과적이었고, 동물은 93.2%, 곰팡이는 86.6%의 예측정확도로 크게 향상된 성능을 얻었다.

• Journal of Ecology and Environment
• /
• 제34권4호
• /
• pp.411-423
• /
• 2011

This study was conducted to quantify soil $CO_2$ efflux using the continuous measurement method and to examine the applicability of an automatic continuous measurement system in a Korean deciduous broad-leaved forest. Soil respiration rate (Rs) was assessed through continuous measurements during the 2004-2005 full growing seasons using an automatic opening/closing chamber system in sections of a Gwangneung temperate deciduous forest, Korea. The study site was an old-growth natural mixed deciduous forest approximately 80 years old. For each full growth season, the annual Rs, which had a gap that was filled with data using an exponential function derived from soil temperature (Ts) at 5-cm depth, and Rs values collected in each season were 2,738.1 g $CO_2$ $m^{-2}y^{-1}$ in 2004 and 3,355.1 g $CO_2$ $m^{-2}y^{-1}$ in 2005. However, the diurnal variation in Rs showed stronger correlations with Ts (r = 0.91, P < 0.001 in 2004, r = 0.87, P < 0.001 in 2005) and air temperature (Ta) (r = 0.84, P < 0.001 in 2004, r = 0.79, P < 0.001 in 2005) than with deep Ts during the spring season. However, the temperature functions derived from the Ts at various depths of 0, -2, -5, -10, and -20 cm revealed that the correlation coefficient decreased with increasing soil depth in the spring season, whereas it increased in the summer. Rs showed a weak correlation with precipitation (r = 0.25, P < 0.01) and soil water content (r = 0.28, P < 0.05). Additionally, the diurnal change in Rs revealed a higher correlation with Ta than that of Ts. The $Q_{10}$ values from spring to winter were calculated from each season's dataset and were 3.2, 1.5, 7.4, and 2.7 in 2004 and 6.0, 3.1, 3.0, and 2.6 in 2005; thus, showing high fluctuation within each season. The applicability of an automatic continuous system was demonstrated for collecting a high resolution soil $CO_2$ efflux dataset under various environmental conditions.

• 대한의용생체공학회:의공학회지
• /
• 제41권5호
• /
• pp.195-202
• /
• 2020

Needle detection in ultrasound images is sometimes difficult due to obstruction of fat tissues. Accurate needle detection using continuous ultrasound (CUS) images is a vital stage of treatment planning for tissue biopsy and brachytherapy. The main goal of the study is classified into two categories. First, new detection model, i.e. D-Attention Unet, is developed by combining the context information of 3D medical data and CUS images. Second, the D-Attention Unet model was compared with other models to verify its usefulness for needle detection in continuous ultrasound images. The continuous needle images taken with ultrasonic waves were converted into still images for dataset to evaluate the performance of the D-Attention Unet. The dataset was used for training and testing. Based on the results, the proposed D-Attention Unet model showed the better performance than other 3 models (Unet, D-Unet and Attention Unet), with Dice Similarity Coefficient (DSC), Recall and Precision at 71.9%, 70.6% and 73.7%, respectively. In conclusion, the D-Attention Unet model provides accurate needle detection for US-guided biopsy or brachytherapy, facilitating the clinical workflow. Especially, this kind of research is enthusiastically being performed on how to add image processing techniques to learning techniques. Thus, the proposed method is applied in this manner, it will be more effective technique than before.

• 대한의용생체공학회:의공학회지
• /
• 제42권6호
• /
• pp.268-276
• /
• 2021

Functional near-infrared spectroscopy-based brain-computer interface (fNIRS-based BCI) has been receiving much attention. However, we are practically constrained to obtain a lot of fNIRS data by inherent hemodynamic delay. For this reason, when employing machine learning techniques, a problem due to the high-dimensional feature vector may be encountered, such as deteriorated classification accuracy. In this study, we employ an elastic net-based feature selection which is one of the embedded methods and demonstrate the utility of which by analyzing the results. Using the fNIRS dataset obtained from 18 participants for classifying brain activation induced by mental arithmetic and idle state, we calculated classification accuracies after performing feature selection while changing the parameter α (weight of lasso vs. ridge regularization). Grand averages of classification accuracy are 80.0 ± 9.4%, 79.3 ± 9.6%, 79.0 ± 9.2%, 79.7 ± 10.1%, 77.6 ± 10.3%, 79.2 ± 8.9%, and 80.0 ± 7.8% for the various values of α = 0.001, 0.005, 0.01, 0.05, 0.1, 0.2, and 0.5, respectively, and are not statistically different from the grand average of classification accuracy estimated with all features (80.1 ± 9.5%). As a result, no difference in classification accuracy is revealed for all considered parameter α values. Especially for α = 0.5, we are able to achieve the statistically same level of classification accuracy with even 16.4% features of the total features. Since elastic net-based feature selection can be easily applied to other cases without complicated initialization and parameter fine-tuning, we can be looking forward to seeing that the elastic-based feature selection can be actively applied to fNIRS data.

• 대한의용생체공학회:의공학회지
• /
• 제44권6호
• /
• pp.384-391
• /
• 2023

Among the elderly, fatal injuries and deaths are significantly attributed to falls. Therefore, a pre-impact fall detection system is necessary for injury prevention. In this study, a robust threshold-based algorithm was proposed for pre-impact fall detection, reducing false positives in highly dynamic daily-living movements. The algorithm was validated using public datasets (KFall and FARSEEING) that include the real-world elderly fall. A 6-axis IMU sensor (Movella Dot, Movella, Netherlands) was attached to S2 of 20 healthy adults (aged 22.0±1.9years, height 164.9±5.9cm, weight 61.4±17.1kg) to measure 14 activities of daily living and 11 fall movements at a sampling frequency of 60Hz. A 5Hz low-pass filter was applied to the IMU data to remove high-frequency noise. Sum vector magnitude of acceleration and angular velocity, roll, pitch, and vertical velocity were extracted as feature vector. The proposed algorithm showed an accuracy 98.3%, a sensitivity 100%, a specificity 97.0%, and an average lead-time 311±99ms with our experimental data. When evaluated using the KFall public dataset, an accuracy in adult data improved to 99.5% compared to recent studies, and for the elderly data, a specificity of 100% was achieved. When evaluated using FARSEEING real-world elderly fall data without separate segmentation, it showed a sensitivity of 71.4% (5/7).