BACKGROUND/OBJECTIVES: The aim of this study was to develop Korean food image detection and recognition model for use in mobile devices for accurate estimation of dietary intake. SUBJECTS/METHODS: We collected food images by taking pictures or by searching web images and built an image dataset for use in training a complex recognition model for Korean food. Augmentation techniques were performed in order to increase the dataset size. The dataset for training contained more than 92,000 images categorized into 23 groups of Korean food. All images were down-sampled to a fixed resolution of 150 × 150 and then randomly divided into training and testing groups at a ratio of 3:1, resulting in 69,000 training images and 23,000 test images. We used a Deep Convolutional Neural Network (DCNN) for the complex recognition model and compared the results with those of other networks: AlexNet, GoogLeNet, Very Deep Convolutional Neural Network, VGG and ResNet, for large-scale image recognition. RESULTS: Our complex food recognition model, K-foodNet, had higher test accuracy (91.3%) and faster recognition time (0.4 ms) than those of the other networks. CONCLUSION: The results showed that K-foodNet achieved better performance in detecting and recognizing Korean food compared to other state-of-the-art models.
Dataset

No Abstract available.
Dataset* ; Nursing*

Serially sectioned images (MR, CT, and anatomical images) of the whole body are helpful in anatomy education because three dimensional images can be reconstructed with the serially sectioned images, and then the three dimensional images can be sectioned and rotated. To make the most important anatomical images of the serially sectioned images, the cadaver's whole body should be embedded, frozen, and serially sectioned to make sectioned surfaces. In this study, to make the sectioned surfaces better than the Visible Human Project dataset, the equipments and techniques have been developed as follows. First, the equipments (embedding box, freezer) and techniques for embedding and freezing of the cadaver's whole body have been developed. Second, the equipments (cryomacrotome) and techniques for serial sectioning of the embedding box at 0.2 mm intervals have been developed. By using these equipments and techniques, the sectioned surfaces with good quality could be made at 0.2 mm intervals. The anatomical images made of the sectioned surfaces will be the basis for making better three dimensional images which are more helpful in anatomy education.
Dataset ; Education ; Freezing ; Humans

Three monophyletic branches are strongly supported in a phylogenetic analysis of the Teloschistaceae based on combined data sets of internal transcribed spacer and large subunit nrDNA and 12S small subunit mtDNA sequences. These are described as new monotypic genera: Jasonhuria S. Y. Kondr., L. Lokos et S. -O. Oh, Loekoesia S. Y. Kondr., S. -O. Oh et J. -S. Hur and Olegblumia S. Y. Kondr., L. Lokos et J. -S. Hur. Three new combinations for the type species of these genera are proposed.
Dataset ; DNA, Mitochondrial ; Lichens*

Mammogram images are majorly used for detecting the breast cancer. The level of positivity of breast cancer is detected after excluding the pectoral muscle from mammogram images. Hence, it is very significant to identify and segment the pectoral muscle from the mammographic images. In this work, a new multilevel thresholding, on the basis of electro-magnetism optimization (EMO) technique, is proposed. The EMO works on the principle of attractive and repulsive forces among the charges to develop the members of a population. Here, both Kapur's and Otsu based cost functions are employed with EMO separately. These standard functions are executed over the EMO operator till the best solution is achieved. Thus, optimal threshold levels can be identified for the considered mammographic image. The proposed methodology is applied on all the three twenty-two mammogram images available in mammographic image analysis society dataset, and successful segmentation of the pectoral muscle is achieved for majority of the mammogram images. Hence, the proposed algorithm is found to be robust for variations in the pectoral muscle.
Breast Neoplasms ; Dataset

PURPOSE: To evaluate the reproducibility of scan-based abutments using a blue light model scanner. MATERIALS AND METHODS: A wax cast abutment die was fabricated, and a silicone impression was prepared using a silicone material. Nine study dies were constructed using the prepared duplicable silicone, and the first was used as a reference. These dies were classified into three groups and scanned using a blue light model scanner. The first three-dimensional (3D) data set was obtained by scanning eight dies separately in the first group. The second 3D data set was acquired when four dies were placed together in the scanner and scanned twice in the second group. Finally, the third 3D data set was obtained when eight dies were placed together in the scanner and scanned once. These data were then used to define the data value using third-dimension software. All the data were then analyzed using the non-parametric Kruskal–Wallis H test (α=.05) and the post-hoc Mann-Whitney U-test with Bonferroni's correction (α=.017). RESULTS: The means and standard deviations of the eight dies together were larger than those of the four dies together and of the individual die. Moreover, significant differences were observed among the three groups (P < .05). CONCLUSION: With larger numbers of abutments scanned together, the scan becomes more inaccurate and loses reproducibility. Therefore, scans of smaller numbers of abutments are recommended to ensure better results.
Dataset ; Silicon ; Silicones

OBJECTIVES: The purpose was to explore sources of Koreans' life meaning and determine its structure that is reflective of Koreans' unique culture and values. METHODS: The study consisted of both qualitative and quantitative research methods. To this end, two sample data sets were collected. Study One was an exploratory study in which the qualitative component was conducted in order to gather all possible attributes of sources of life meaning among Koreans. All possible sources of life meaning were extracted through content analysis. Study Two was a quantitative study using a closed questionnaire and conducted in order to determine the structure of Koreans' life meaning by measuring Koreans' current level of life meaning. For the study, factor analysis was carried out. RESULTS: From Study One, 106 attributes of all possible sources of Koreans' life meaning were extracted. In Study Two, factor analysis with the responses from 638 subjects reduced 106 attributes to 53 attributes and ten factors were extracted as Koreans' sources of life meaning: Achievement, Security, Religion, Acceptance & Affirmation, Relationship, Self-Transcendence, Good Character, Self-Discipline, Physical Health and Intimate Friend. CONCLUSION AND IMPLICIATIONS: Among the ten factors extracted from this study, the factors of Security, Acceptance and Affirmation, Good Character, Self-Discipline, and Physical Health are Koreans' unique factors of Life Meaning, while Achievement, Religion, Relationship, Self-Transcendence and Intimate Friend are comparable to Wong's1) Personal Meaning Profile for Canadians. It implies that it is necessary to develop Koreans' own measurement tool in order to assess their life meaning properly. However, because this study was an exploratory in developing Koreans' life meaning mea-surement and had several limitations, in order to determine structure of Koreans' life meaning, further study must be necessary.
Dataset ; Friends ; Humans ; Surveys and Questionnaires

The era of "Big Data" presents opportunities to substantively address cancer prevention and control issues by improving health behaviors and refining theoretical models designed to understand and intervene in those behaviors. Yet, the terms “model” and “Big Data” have been used rather loosely, and clarification of these terms is required to advance the science in this area. The objectives of this paper are to discuss conceptual definitions of the terms "model" and "Big Data", as well as examine the promises and challenges of Big Data to advance cancer prevention and control research using behavioral theories. Specific recommendations for harnessing Big Data for cancer prevention and control are offered.
Dataset ; Health Behavior ; Models, Theoretical

BACKGROUND AND OBJECTIVES: Changes in distortion product otoacoustic emission (DPOAE) caused by contralateral suppression (CS) allow the function of the auditory efferent system to be evaluated. Parameters affording maximum CS are preferred in terms of clinical application. Our objective was to evaluate the effects of primary levels and frequencies on DPOAE-mediated CS. SUBJECTS AND METHODS: Sixteen subjects with normal hearing participated. DPOAEs were recorded with and without contralateral acoustic stimulation; we delivered broadband noise of 65 dB SPL at f2 frequencies between 1,000 Hz and 6,727 Hz, at 8 pt/octave. The L2 was varied between 40 dB SPL and 80 dB SPL in 10-dB steps. RESULTS: L2 did not significantly affect DPOAE-mediated CS. Higher L2 levels significantly reduced the fine structure depth of both the baseline and suppressed DPOAE datasets. The amount of CS was greatly affected by the f2 frequency; lower and higher frequency ranges afforded significantly stronger suppression than did mid-frequencies within the studied range. CONCLUSIONS: Our findings suggest that DPOAE CS should be measured over a wide range of frequencies as the amount of CS seems to be highly dependent on f2. The use of a higher L2 level may be optimal when it is sought to evoke strong DPOAE-mediated suppression while simultaneously minimizing DPOAE fine structure. Our findings may assist in optimization of clinical procedures evaluating the integrity of the auditory efferent system.
Acoustic Stimulation ; Dataset ; Hearing ; Noise

PURPOSE: Artificial intelligence (AI), represented by deep learning, can be used for real-life problems and is applied across all sectors of society including medical and dental field. The purpose of this study is to review articles about deep learning that were applied to the field of oral and maxillofacial radiology. MATERIALS AND METHODS: A systematic review was performed using Pubmed, Scopus, and IEEE explore databases to identify articles using deep learning in English literature. The variables from 25 articles included network architecture, number of training data, evaluation result, pros and cons, study object and imaging modality. RESULTS: Convolutional Neural network (CNN) was used as a main network component. The number of published paper and training datasets tended to increase, dealing with various field of dentistry. CONCLUSION: Dental public datasets need to be constructed and data standardization is necessary for clinical application of deep learning in dental field.
Artificial Intelligence ; Dataset ; Dentistry ; Learning