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Construction of a Cyclic Regular-Triangle Trimer of Cytochrome c555 with a Central Hole Using Sortase A
No full textProtein-based supramolecules require precise arrangement of building blocks. A regular-triangle trimer (cp-c555)3 has been constructed using an α-helix-inserted-circular permutant (cp-c555) of Aquifex aeolicus cytochrome (cyt) c555, where the trimers may dissociate to monomers. In this study, we stabilized the regular-triangle structure by constructing a cyclic regular-triangle of three α-helix-linked cyt c555 molecules using sortase-mediated ligation (SML). Comparing SML using sortase A for six cp-c555 variant trimers, the variant with GGG at the N-terminus and LPETG at the C-terminus reacted most efficiently. OP-(c555)3 was designed, in which two cyt c555 molecules were fused using an α-helix, generating a dimer. The cyt c555 C-terminal region was attached to the N-terminus of the dimer, and the cyt c555 N-terminal region was attached to the C-terminus of the dimer using the same α-helix. OP-(c555)3 was expressed in Escherichia coli cells, and the termini were connected by SML, forming a cyclic regular-triangle, CL-(c555)3. CL-(c555)3 showed higher thermostability than (cp-c555)3 and OP-(c555)3. CL-(c555)3 structural stability was confirmed using high-speed atomic force microscopy. The X-ray crystal structure of CL-(c555)3 showed a cyclic structure and a nanoporous supramolecular assembly. These results demonstrate that a nanoporous supramolecular assembly can be constructed by designing a cyclic molecule with a central hole using SML.journal articl
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Wasserstein Non-Negative Matrix Factorization for Multi-Layered Graphs and its Application to Mobility Data
No full textMulti-layered graphs are popular in mobility studies because transportation data include multiple modalities, such as railways, buses, and taxis. Another example of a multi-layered graph is the time series of mobility when periodicity is considered. The graphs are analyzed using standard signal processing methods such as singular value decomposition and tensor analysis, which can estimate missing values. However, their feature extraction abilities are insufficient for optimizing mobility networks. This study proposes a method that combines the Wasserstein non-negative matrix factorization (W-NMF) with line graphs to obtain low-dimensional representations of multi-layered graphs. A line graph is defined as the dual graph of a graph, where the vertices correspond to the edges of the original graph, and the edges correspond to the vertices. Thus, the shortest path length between two vertices in the line graph corresponds to the distance between the edges in the original graph. Through experiments using synthetic and benchmark datasets, we show that the performance and robustness of our method are superior to conventional methods. Additionally, we apply our method to real-world taxi origin—destination data as a mobility dataset and discuss the findings.journal articl
Cleavage cascade of the sigma regulator FecR orchestrates TonB-dependent signal transduction
No full textTonB-dependent signal transduction is a versatile mechanism observed in gram-negative bacteria, integrating energy-dependent substrate transport with signal relay. In Escherichia coli, the TonB-ExbBD motor complex energizes the TonB-dependent transporter FecA, facilitating ferric citrate import. FecA also functions as a sensor, transmitting signals to the cytoplasmic membrane protein FecR. We previously demonstrated that FecR undergoes a three-step cleavage process, culminating in the activation of the cytoplasmic sigma factor FecI, which drives fec gene transcription. Here, we describe the complete mechanism of FecR cleavage-mediated ferric citrate signaling involving FecA and TonB. The cleavage cascade begins with FecR autoproteolysis prior to membrane integration. The soluble C-terminal domain (CTD) fragment of FecR is co-translocated with the N-terminal domain (NTD) fragment through a Tat system-mediated process. In the periplasm, the interaction between the CTD and NTD fragments prevents further cleavage. This inhibition is lifted by TonB-mediated motor function, which releases the CTD, allowing the cleavage cascade to proceed. This process is essential for ferric citrate signal-induced activation of fec gene expression. Our findings reveal that the regulation of FecR cleavage, relying on the TonB-FecA axis, plays a central role in bacterial response to ferric citrate signals.journal articl
Microwave electro-optical imaging system using a frequency tracking optical local oscillator source with a polarization CMOS image sensor
No full textIn this study, we developed a high-frequency electric field imaging system based on the first-order electro-optic effect for the detection of time-varying frequency wave sources. The proposed system utilizes the optical heterodyne technique. The process begins with an antenna that detects the frequency of the electric field of interest, which is then fed into the optical local oscillator (LO) signal generation system. This optical LO signal tracks the frequency of the electric field being observed while maintaining a constant intermediate frequency. This configuration enables the visualization of dynamic electric fields, including asynchronous wave sources and frequency-modulated signals, without requiring a direct physical connection to the wave source.journal articl
Abbreviation Expansion using only a Single Word as Context
No full textAccurate interpretation of medical abbreviations is vital for clinical decision-making, given their frequent ambiguity in medical texts. We propose a simple Word Sense Disambiguation method using Odds Ratio (OR) of surrounding word tokens to build a knowledge dictionary from a large medical corpus. Abbreviations are expanded using the most informative word identified in this dictionary. Evaluated on ambiguous abbreviations from the NCBI Disease Corpus test set, our OR-based method achieved an accuracy of 0.82, closely approaching SVM (0.87) and BERT (0.89) models. Despite its simplicity, our method highlights its potential as an interpretable and precise alternative for clinical text.conference pape
Multilingual Symptom Detection on Social Media: Enhancing Health-related Fact-checking with LLMs
No full textSocial media has emerged as a valueable source for early pandemic detection, as repeated mentions of symptoms by users may signal the onset of an outbreak. However, to be a reliable system, validation through fact-checking and verification against official health records is essential. Without this step, systems risk spreading misinformation to the public. The effectiveness of these systems also depend on their ability to process data in multiple languages, given the multilingual nature of social media data.Yet, many NLP datasets and disease surveillance system remain heavily English-centric, leading to significant performance gaps for low-resource languages.This issue is especially critical in Southeast Asia, where symptom expression may vary culturally and linguistically.Therefore, this study evaluates the symptom detection capabilities of LLMs in social media posts across multiple languages, models, and symptoms to enhance health-related fact-checking. Our results reveal significant language-based discrepancies, with European languages outperforming under-resourced Southeast Asian languages. Furthermore, we identify symptom-specific challenges, particularly in detecting respiratory illnesses such as influenza, which LLMs tend to overpredict.The overestimation or misclassification of symptom mentions can lead to false alarms or public misinformation when deployed in real-world settings. This underscores the importance of symptom detection as a critical first step in medical fact-checking within early outbreak detection systems.conference pape
Do Interpersonal Skills Affect Human-AI Collaboration Performance? A Study with ChatGPT
No full textCollaboration between humans and artificial intelligence (AI) has demonstrated the potential to achieve performance surpassing that of AI alone. As AI becomes more integrated into society, human-AI collaboration is expected to emerge as a new form of teamwork. The recent advancements in large language models (LLMs) have accelerated research on human-LLM collaboration across various domains. While previous studies have focused on improving the performance of LLMs and methods for effective collaboration, little is known about how user-specific traits, such as interpersonal skills, influence collaboration outcomes with LLMs. This study addresses this gap by focusing on the role of interpersonal skills in human-AI interaction to deepen understanding of human-AI collaboration. The experimental results showed that participants with lower interpersonal skills were more likely to accept AI-generated responses, suggesting that they benefit more from AI. These findings suggest that interpersonal skills could influence how users critically assess with AI-generated content.conference pape
Capacitive Neuromorphic Computation Employing Nonlinearity Correction Techniq
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