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Objectives: Coronavirus disease 2019 (COVID-19) has been declared a global pandemic owing to the rapid spread of the causative agent, severe acute respiratory syndrome coronavirus 2. Its Delta and Omicron variants are more transmissible and pathogenic than other variants. Some debates have emerged on the mechanism of variants of concern. In the COVID-19 wave that began in December 2021, the Omicron variant, first reported in South Africa, became identifiable in most cases globally. The aim of this study was to provide data to inform effective responses to the transmission of the Omicron variant. Methods: The Delta variant and the spike protein D614G mutant were compared with the Omicron variant. Viral loads from 5 days after symptom onset were compared using epidemiological data collected at the time of diagnosis. Results: The Omicron variant exhibited a higher viral load than other variants, resulting in greater transmissibility within 5 days of symptom onset. Conclusion: Future research should focus on vaccine efficacy against the Omicron variant and compare trends in disease severity associated with its high viral load. [ABSTRACT FROM AUTHOR]

Adversarial attack is a technique that causes a malfunction of classification models by adding noise that cannot be distinguished by humans, which poses a threat to a deep learning model. In this paper, we propose an efficient method to detect adversarial images using Gaussian process regression. Existing deep learning-based adversarial detection methods require numerous adversarial images for their training. The proposed method overcomes this problem by performing classification based on the statistical features of adversarial images and clean images that are extracted by Gaussian process regression with a small number of images. This technique can determine whether the input image is an adversarial image by applying Gaussian process regression based on the intermediate output value of the classification model. Experimental results show that the proposed method achieves higher detection performance than the other deep learning-based adversarial detection methods for powerful attacks. In particular, the Gaussian process regression-based detector shows better detection performance than the baseline models for most attacks in the case with fewer adversarial examples. [ABSTRACT FROM AUTHOR]

This paper suggests an automatic approach to extracting Java method implementation patterns associated with method identifiers using Cascade K-Medoids. Java method implementation patterns indicate recurring implementations for achieving the purpose described in the method identifier with the given parameters and return type. If the implementation is different from the purpose, readers of the code tend to take more time to comprehend the method, which eventually affects to the increment of software maintenance cost. In order to automatically identify implementation patterns and its representative sample code, we first propose three groups of feature vectors for characterizing the Java method signature, method body and their relation. Then, we apply Cascade K-Medoids by enhancing the K-Medoids algorithm with the Calinski and Harrabasez algorithm. As the evaluation of our approach, we identified 16,768 implementation patterns of 7,169 method identifiers from 50 open source projects. The implementation patterns have been validated by the 30 industrial practitioners with from 1 to 6 years industrial experience, resulting in 86% of the precision. [ABSTRACT FROM AUTHOR]

A styled document is a document that contains diverse decorating functions such as different font, colors, tables and images generally authored in a word processor (e.g., MS-WORD, Open Office). Compared to a plain-text document, a styled document enables a human to easily recognize a logical structure such as section, subsection and contents of a document. However, it is difficult for a computer to recognize the structure if a writer does not explicitly specify a type of an element by using the styling functions of a word processor. It is one of the obstacles to enhance document version management systems because they currently manage the document with a file as a unit, not the document elements as a management unit. This paper proposes a machine learning based approach to analyzing the logical structure of a styled document composing of sections, subsections and contents. We first suggest a feature vector for characterizing document elements from a styled document, composing of eight features such as font size, indentation and period, each of which is a frequently discovered item in a styled document. Then, we trained machine learning classifiers such as Random Forest and Support Vector Machine using the suggested feature vector. The trained classifiers are used to automatically identify logical structure of a styled document. Our experiment obtained 92.78% of precision and 94.02% of recall for analyzing the logical structure of 50 styled documents. [ABSTRACT FROM AUTHOR]

Purpose: To analyze which sonographic features of thyroid nodules with macrocalcifications were predictable of thyroid malignancy. Materials and Methods: We reviewed sonographic findings of 854 macrocalcified thyroid nodules in patients who underwent fine needle aspiration biopsy between December 2009 and January 2011. There were 171 non-diagnostic aspirations, 34 nodules with category 3, 4, 5 based on Bethesda system, which were not confirmed by surgery, and these nodules were excluded from the analysis. Sonographic characteristics of the macrocalcifications including its thickness, interruption, and existence of soft tissue rim outside the macrocalcification were analyzed. Other sonographic characteristics of nodules such as shape, margin, composition, echo pattern, vascularity, and underlying parenchymal echogenicity were also evaluated. The correlation of sonographic features with cytopathologic results and the diagnostic performance of sonographic features for the prediction of malignancy were analyzed. Results: Among 649 nodules, 179 (27.6%) nodules were malignant and 470 (72.4%) nodules were benign. Among the features of the macrocalcification, interruption, irregular thickness, or the presence of soft tissue outside calcification rim were associated with malignancy (p<0.001). A high sensitivity and negative predictive values for the prediction of malignancy was found in sonographic characteristics of irregular thickness (92.2% and 91.0%, respectively) and the presence of soft tissue (88.5% and 88.8%, respectively). Conclusion: Sonographic characteristics of macrocalcification such as interruption, irregular thickness and the presence of soft tissue rim were associated with malignancy in thyroid nodules with macrocalcifications. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the value of breast MRI in analysis of papillomas of the breast. Materials and Methods: From 1996 to 2004, 94 patients underwent surgery due to papillomas of the breast. Among them, 21 patients underwent 3D fast low angle shot (FLASH) dynamic breast MRI. Eight masses were palpable and 11 of 21 patients had nipple discharge. Two radiologists indifferently analyzed the location, size of the lesions and shape, margin of the masses, multiplicity and ductal relation. The MRI findings were categorized according to breast imaging reporting and data system (BI-RADS) lexicon. The amount and pattern of enhancement and associated findings were also evaluated according to BI-RADS. We then compared the MRI findings with galactography, mammography and breast ultrasonography (US) and examined histopathologic correlation. Results: On breast MRI, the lesion size was 0.4-1.59 cm, and 18 patients showed subareolar location. On 4.25 cm (mean 1.54) dynamic enhanced images, imaging findings showed mass (n = 10), intracystic mass (n = 3), focus (n = 5), ductal enhancement (n = 2), and segmental enhancement (n = 1). In cases of the masses, the shapes of the masses were round (n = 4), lobulated (n = 3), and irregular (n = 6), and margins were circumscribed (n = 6), microlobulated (n = 5), and indistinct (n = 2). The enhancement patterns were homogeneous enhancement (n = 7), heterogeneous (n = 3) or rim enhancement (n = 3). Conclusion: The contrast enhanced dynamic breast MRI was highly sensitive for diagnosis of breast papillomas. MRI could play a key role in the pre-operative work-up for multiple papillomas and papillomatosis. [ABSTRACT FROM AUTHOR]

An abstract of the article "Transcriptional Activator TonE-Binding Protein in Cellular Protection and Differentiation," by Jeong Ah Kim, Un Sil Jeon, Min Seong Kwon, Sun Woo Lim, and H. Moo Kwon is presented.