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2. New Interpretable Patterns and Discriminative Features from Brain Functional Network Connectivity Using Dictionary Learning

Author

Ghayem, Fateme, Yang, Hanlu, Kantar, Furkan, Kim, Seung-Jun, Calhoun, Vince D., and Adali, Tulay

Subjects
Quantitative Biology - Neurons and Cognition, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Independent component analysis (ICA) of multi-subject functional magnetic resonance imaging (fMRI) data has proven useful in providing a fully multivariate summary that can be used for multiple purposes. ICA can identify patterns that can discriminate between healthy controls (HC) and patients with various mental disorders such as schizophrenia (Sz). Temporal functional network connectivity (tFNC) obtained from ICA can effectively explain the interactions between brain networks. On the other hand, dictionary learning (DL) enables the discovery of hidden information in data using learnable basis signals through the use of sparsity. In this paper, we present a new method that leverages ICA and DL for the identification of directly interpretable patterns to discriminate between the HC and Sz groups. We use multi-subject resting-state fMRI data from $358$ subjects and form subject-specific tFNC feature vectors from ICA results. Then, we learn sparse representations of the tFNCs and introduce a new set of sparse features as well as new interpretable patterns from the learned atoms. Our experimental results show that the new representation not only leads to effective classification between HC and Sz groups using sparse features, but can also identify new interpretable patterns from the learned atoms that can help understand the complexities of mental diseases such as schizophrenia.

Published
2022

3. Radio Map Estimation: A Data-Driven Approach to Spectrum Cartography

Author

Romero, Daniel and Kim, Seung-Jun

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

Radio maps characterize quantities of interest in radio communication environments, such as the received signal strength and channel attenuation, at every point of a geographical region. Radio map estimation typically entails interpolative inference based on spatially distributed measurements. In this tutorial article, after presenting some representative applications of radio maps, the most prominent radio map estimation methods are discussed. Starting from simple regression, the exposition gradually delves into more sophisticated algorithms, eventually touching upon state-of-the-art techniques. To gain insight into this versatile toolkit, illustrative toy examples will also be presented.

Published
2022
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7. Channel Gain Cartography via Mixture of Experts

Author

Lopez-Ramos, Luis M., Teganya, Yves, Beferull-Lozano, Baltasar, and Kim, Seung-Jun

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Machine Learning, J.2
Abstract

In order to estimate the channel gain (CG) between the locations of an arbitrary transceiver pair across a geographic area of interest, CG maps can be constructed from spatially distributed sensor measurements. Most approaches to build such spectrum maps are location-based, meaning that the input variable to the estimating function is a pair of spatial locations. The performance of such maps depends critically on the ability of the sensors to determine their positions, which may be drastically impaired if the positioning pilot signals are affected by multi-path channels. An alternative location-free approach was recently proposed for spectrum power maps, where the input variable to the maps consists of features extracted from the positioning signals, instead of location estimates. The location-based and the location-free approaches have complementary merits. In this work, apart from adapting the location-free features for the CG maps, a method that can combine both approaches is proposed in a mixture-of-experts framework., Comment: 5 pages, 2 figures, accepted in Globecom 2020

Published
2020

15. Unveiling Cas12j Trans‐Cleavage Activity for CRISPR Diagnostics: Application to miRNA Detection in Lung Cancer Diagnosis.

Author

Kang, Ju‐Eun, Kim, Hansol, Lee, Young‐Hoon, Lee, Ha‐Yeong, Park, Yeonkyung, Jang, Hyowon, Kim, Jae‐Rin, Lee, Min‐Young, Jeong, Byeong‐Ho, Byun, Ju‐Young, Kim, Seung Jun, Lim, Eun‐Kyung, Jung, Juyeon, Woo, Eui‐Jeon, Kang, Taejoon, and Park, Kwang‐Hyun

Subjects
LUNG cancer, NUCLEIC acids, CRISPRS, MOLECULAR diagnosis, MICRORNA
Abstract

Cas12j, a hypercompact and efficient Cas protein, has potential for use in CRISPR diagnostics, but has not yet been used because the trans‐cleavage activity of Cas12j is veiled. Here, the trans‐cleavage behavior of Cas12j1, 2, and 3 variants and evaluate their suitability for nucleic acid detection is unveiled. The target preferences and mismatch specificities of the Cas12j variants are precisely investigated and the optimal Cas12j reaction conditions are determined. As a result, the EXP‐J assay for miRNA detection by harnessing the robust trans‐cleavage activity of Cas12j on short ssDNA is developed. The EXP‐J method demonstrates exceptional detection capabilities for miRNAs, proving that Cas12j can be a pivotal component in molecular diagnostics. Furthermore, the translational potential of the EXP‐J assay is validated by detecting oncogenic miRNAs in plasma samples from lung cancer patients. This investigation not only elucidates the trans‐cleavage characteristics of Cas12j variants, but also advances the Cas12j‐based diagnostic toolkit. [ABSTRACT FROM AUTHOR]

Published
2024
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16. Development of a Prediction Model for Specific Fuel Consumption in Rotary Tillage Based on Actual Operation.

Author

Kim, Seung-Jun, Jang, Moon-Kyeong, Hwang, Seok-Joon, Lee, Won Suk, and Nam, Ju-Seok

Subjects
STANDARD deviations, ENERGY consumption, PEARSON correlation (Statistics), CONSUMPTION (Economics), PREDICTION models
Abstract

Tractor fuel consumption has typically been predicted using indoor test results under specific conditions. This study analyzes the factors affecting fuel consumption during rotary tillage in actual fields and develops a prediction model. The test field was divided into sections using a grid method, and rotary tillage operations were performed to measure various parameters, including soil strength, tractor's transmission and PTO gear stages, tillage pitch, travel speed, engine and PTO shaft torque and speed, and fuel consumption. Pearson correlation identified variables affecting specific fuel consumption, and regression analysis was used to develop a prediction model. The model's accuracy was analyzed using the coefficient of determination (R2) and root mean square error (RMSE), and it was compared with the ASABE's fuel consumption prediction model. The test results showed that higher transmission and PTO gear stages, and tillage pitch decreased specific fuel consumption, while soil strength had no significant effect. Thus, operating at higher gear and PTO stages within suitable conditions enhances energy efficiency in rotary tillage. Statistical analysis showed that specific fuel consumption significantly correlated with tractor travel speed, PTO shaft power, and PTO shaft speed. The prediction model, including these variables, had the highest accuracy with R2 of 0.91 and RMSE of 0.011 L/kW·h. The developed prediction model showed significantly improved accuracy compared to the ASABE model, indicating that it can predict specific fuel consumption based on key operational variables in actual rotary tillage operations. [ABSTRACT FROM AUTHOR]

Published
2024
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19. Identification of MYC as an antinecroptotic protein that stifles RIPK1–RIPK3 complex formation

Author

Seong, Daehyeon, Jeong, Manhyung, Seo, Jinho, Lee, Ji-Yoon, Hwang, Chi Hyun, Shin, Ho-Chul, Shin, Jeong Yoon, Nam, Young Woo, Jo, Jeong Yeon, Lee, Haeseung, Kim, Hye-Jung, Kim, Hwa-Ryeon, Oh, Ji Hoon, Ha, Sang-Jun, Kim, Seung Jun, Roe, Jae-Seok, Kim, Wankyu, Cheong, June-Won, Bae, Kwang-Hee, Lee, Sang Chul, Oberst, Andrew, Vandenabeele, Peter, Shin, Dong Hoon, Lee, Eun-Woo, and Song, Jaewhan

Published
2020

21. Learning Power Spectrum Maps from Quantized Power Measurements

Author

Romero, Daniel, Kim, Seung-Jun, Giannakis, Georgios B., and Lopez-Valcarce, Roberto

Subjects
Computer Science - Information Theory, Computer Science - Learning, Mathematics - Functional Analysis, Statistics - Machine Learning
Abstract

Power spectral density (PSD) maps providing the distribution of RF power across space and frequency are constructed using power measurements collected by a network of low-cost sensors. By introducing linear compression and quantization to a small number of bits, sensor measurements can be communicated to the fusion center with minimal bandwidth requirements. Strengths of data- and model-driven approaches are combined to develop estimators capable of incorporating multiple forms of spectral and propagation prior information while fitting the rapid variations of shadow fading across space. To this end, novel nonparametric and semiparametric formulations are investigated. It is shown that PSD maps can be obtained using support vector machine-type solvers. In addition to batch approaches, an online algorithm attuned to real-time operation is developed. Numerical tests assess the performance of the novel algorithms., Comment: Submitted Jun. 2016

Published
2016
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26. R-catcher, a potent molecular tool to unveil the arginylome

Author

Seo, Taewook, Kim, Jihyo, Shin, Ho-Chul, Kim, Jung Gi, Ju, Shinyeong, Nawale, Laxman, Han, Goeun, Lee, Hye Seon, Bang, Geul, Kim, Jin Young, Bang, Jeong Kyu, Lee, Kyung Ho, Soung, Nak-Kyun, Hwang, Joonsung, Lee, Cheolju, Kim, Seung Jun, Kim, Bo Yeon, and Cha-Molstad, Hyunjoo

Published
2021
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28. Symmetry Reduction of Molecular Shape of Cationic Chromophores for High‐Performance Terahertz Generators.

Author

Yoon, Ga‐Eun, Kim, Dong‐Joo, Park, Yu‐Jin, Kim, Chaeyoon, Kim, Seung‐Jun, Shin, Bong‐Rim, Lee, Yun‐Sang, Lee, Chae‐Won, Park, Jung‐Wook, Yu, In Cheol, Yoon, Woojin, Yun, Hoseop, Kim, Dongwook, Jazbinsek, Mojca, Rotermund, Fabian, and Kwon, O‐Pil

Subjects
MOLECULAR shapes, CHROMOPHORES, MOLECULAR vibration, CRYSTAL structure, ABSORPTION coefficients, TERAHERTZ spectroscopy
Abstract

The symmetry reduction of molecular shape of cationic chromophores as a design strategy to develop organic terahertz (THz) generators is used. Electron donating group (EDG) with an asymmetric shape is introduced into two types of cationic styryl‐quinolinium chromophores, leading to the development of several novel non‐centrosymmetric crystals with high macroscopic optical nonlinearity. In contrast, when an EDG with a symmetric shape is introduced, centrosymmetric crystal structure is obtained in all investigated crystals. For THz wave generation, crystals based on asymmetrically shaped EDG exhibit several optimal crystal characteristics, including a plate‐shape morphology, a large size, and an in‐plane polar axis. Furthermore, over the entire range of molecular phonon vibrations, 0.5–4 THz, the steric hindrance group in the asymmetrically shaped EDG has a strong influence on the suppression of THz phonon vibrations, leading to a low THz absorption coefficient. The crystals based on asymmetrically shaped EDG generate an ≈40 times stronger THz electric field than the inorganic ZnTe crystal and very broad THz spectra, extending up to 16 THz. Thus, the symmetry reduction of the molecular shape of cationic chromophores through the introduction of an asymmetrically shaped EDG is an effective design approach for the development of novel organic THz crystals. [ABSTRACT FROM AUTHOR]

Published
2024
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29. Structural analysis of the FERM domain of human protein tyrosine phosphatase non‐receptor type 21.

Author

Lee, Hye Seon, Ku, Bonsu, Shin, Ho-Cheol, and Kim, Seung Jun

Subjects
PHOSPHOPROTEIN phosphatases, PROTEIN-tyrosine phosphatase, PROTEIN domains, X-ray crystallography, HYDROPHOBIC interactions, PROTEIN-protein interactions
Abstract

Protein tyrosine phosphatase non‐receptor type 21 (PTPN21) is a cytosolic protein tyrosine phosphatase that regulates cell growth and invasion. Due to its oncogenic properties, PTPN21 has recently emerged as a potential therapeutic target for cancer. In this study, the three‐dimensional structure of the PTPN21 FERM domain was determined at 2.1 Å resolution by X‐ray crystallography. The crystal structure showed that this domain harbors canonical FERM folding and consists of three subdomains that are tightly packed via highly conserved intramolecular hydrophobic interactions. Consistent with this, the PTPN21 FERM domain shares high structural homology with several other FERM domains. Moreover, structural superimposition demonstrated two putative protein‐binding sites of the PTPN21 FERM domain, which are presumed to be associated with interaction with its binding partner, kinesin family member 1C. Thus, these data suggest that the FERM domain of PTPN21 serves as a module that mediates protein–protein interaction, like other FERM domains. [ABSTRACT FROM AUTHOR]

Published
2024
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37. Social Engineering Based Security Requirements Elicitation Model for Advanced Persistent Threats

Author

Kim, Seung-Jun, Lee, Seok-Won, Barbosa, Simone Diniz Junqueira, Series editor, Chen, Phoebe, Series editor, Filipe, Joaquim, Series editor, Kotenko, Igor, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Yuan, Junsong, Series editor, Zhou, Lizhu, Series editor, Kamalrudin, Massila, editor, Ahmad, Sabrina, editor, and Ikram, Naveed, editor

Published
2018
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