Tokyo University of Information Sciences

As AI writing support becomes ubiquitous, the question of how disclosing its use affects reader perception remains critical and underexplored. We conducted a controlled study with 261 participants to examine how disclosing varying levels of AI involvement shifts perceptions of the author across six distinct communicative acts. Our analysis of 990 evaluations reveals that disclosure generally erodes perceived trustworthiness, caring, competence, and likability, with the most precipitous declines observed in social and interpersonal writing. A thematic analysis of participant feedback attributes these negative shifts to a perceived loss of human sincerity, diminished authorial effort, and the contextual inappropriateness of AI. Notably, however, we find that higher AI literacy mitigates these negative perceptions, leading to greater tolerance or even appreciation for AI assistance. Our results highlight the nuanced social dynamics of AI-mediated authorship and inform design implications for transparent, context-sensitive writing systems that better preserve trust and authenticity.

Population aging and chronic diseases among older adults pose significant challenges for healthcare systems worldwide. Asia, Japan, and India represent contrasting cases in their approach to these challenges. This study examined factors associated with quality of life (QOL) among older adults with chronic diseases and compared these factors between Japan and India. This cross-sectional study included outpatients aged ≥65 years with chronic diseases attending university hospitals in Japan (n = 230) and India (n = 265). The World Health Organization's Quality of Life assessment measured QOL across physical, psychological, social, and environmental domains. Demographic characteristics, social activities, and socioeconomic status were assessed. Multiple regression analysis identified factors associated with QOL in each country. Living arrangements differed between the countries, with 90.2% of Indian participants living with grandchildren, compared with 4.8% in Japan. Japanese participants had significantly higher psychological and environmental QOL scores. In Japanese participants, social activities and financial management were strongly associated with QOL across all domains (adjusted R2 = 0.145-0.378). Indian participants showed minimal associations (adjusted R2 = 0.008-0.048). This study reveals distinct patterns in QOL determinants between Japan and India, suggesting that cultural context significantly influences factors affecting QOL among older adults with chronic diseases. These findings emphasize the need for culturally sensitive approaches to older adults' care.

Abstract Correlation tests are very important tools for the pathway analysis or graphical modeling of high-dimensional data. In this study, we consider a correlation test under the strongly spiked eigenvalue (SSE) model in high-dimension, low-sample-size scenarios, where the sample size is much smaller than the dimension. High-dimensional data often fit the SSE model. Previously, a high-dimensional test for a correlation matrix under the non-SSE model was constructed using the extended cross-data-matrix (ECDM) methodology. Here, we show that an asymptotic distribution of the test statistic using ECDM methodology under the SSE model can be written using the distribution of the sum of some weighted chi-squared variables when both dimension and sample size reach infinity. We propose a new test procedure using the asymptotic distribution. We also show that the proposed test procedure has preferable properties for power and size. We discuss the performance of the test procedure through simulations and present a demonstration with actual data analyses using a microarray data set.

The enzyme adenosine deaminase acting on RNA (ADAR) plays a key role in editing RNA by converting adenosine to inosine, a process known as A-to-I RNA editing. Initially, ADAR was identified as an enzyme responsible for converting adenosine to inosines, specifically in dsRNA. However, recent studies indicate that ADAR1 can also edit DNA on hybrid strands composed of DNA:RNA strands. One of the representative structures in cells is the R-loop, which is formed at open DNA regions with single-stranded DNA and DNA:RNA hybrid strands, throughhybridization of the cis-transcript or an invading RNA strand with the template DNA. In the previous study, authors have shown that ADAR1 regulates the R-loop formation and genome stability at telomeres in cancer cells carrying noncanonical variants of telomeric repeats.In this chapter, the latest and basic protocols to measure the accumulation and formation of the R-loop are described.

In response to Japan's demographic decline and evolving labor market needs, the country's government established professional universities in 2019 as hybrid institutions that integrate academic legitimacy with vocational orientation. This study analyzes the professional university system through a three-level analytical framework that encompasses the macro national policy, meso institutional practice, and micro student experience dimensions, utilizing the Tokyo Information Design Professional University (TID) as a representative case. At the macro level, professional universities were institutionalized under The School Education Act and The Professional University Establishment Standards, characterized by distinctive requirements such as degree conferral, practice-oriented curricula, and faculty with dual qualifications. At the meso level, TID demonstrates a strong implementation of these mandates through extensive internships, project-based learning, and a high proportion of practice-oriented faculty, thus indicating that national policy goals have been effectively achieved and, in certain aspects, exceeded. At the micro level, students acknowledge the university's distinctive, career-focused education; however, their overall satisfaction remains moderate, thereby revealing a gap between institutional distinctiveness and student experience. At the cross-level, there is a strong alignment between national policy and institutional practice macro-meso, whereas the connection between institutional practice and student perception meso-micro remains partial. This study conceptualizes alignment as a dynamic analytical dimension that clarifies how institutional innovation mediates between policy design and educational outcomes. The findings provide theoretical insights into institutional effectiveness and practical implications for advancing vocational higher education reform in Japan and beyond.

Dopamine (DA) is an important neuromodulator that has been suggested to play key roles in a range of neuropsychiatric disorders. However, its computational impact at the neural circuit level has not been elucidated. Here, we extended a spike-timing-dependent plasticity (STDP)-based single-neuron spatio-temporal pattern detection model by incorporating a DA input and DA-type STDP modulation. We analyzed the direct effect of the DA-type STDP curve and the effect of DA release. We found that the DA-type STDP curve accelerates learning but may promote coarse potentiation of the synaptic efficacy, which leads to false detections. It was also shown that DA can improve the pattern-detection performance up to a 44 percent success rate (2.44 times control) with no false detection, but a DA release with an excessive amount or with too short a delay induces false detection. These results suggest that DA can maintain the pattern detection ability only when released within a limited concentration and a sufficient delay.

Residential photovoltaic and battery energy storage systems (PV/BESS systems) are gaining attention as a measure against natural disasters and rising electricity prices. This paper aims to propose an operational strategy that balances electricity cost reductions, battery lifespans, and outage mitigation for the residential PV/BESS system. The optimization model considering battery degradation determines normal operations with balancing cost reductions and degradation. Additionally, a rule-based approach simulates system performance during various outages and evaluates supply continuity using a resilience metric: the percent continuous supply hour. Outage mitigation benefits are quantified by considering the distribution of residential values of lost load (VoLLs). Results show that the operation considering degradation maintains a high state of charge (SoC) at all times. For 25.7% of households with large demand, electricity cost reductions exceed equipment costs. Outage simulations demonstrate that the mean energy supplied during a 48-h outage ranges from 14 kWh to 26.7 kWh. Furthermore, the proposed operation increases the resilience metric from 20% to 30% under severe and unpredictable outages. Finally, incorporating outage mitigation benefits increases the proportion of households adopting PV/BESS systems by 21.5% points.

We present Ab-VS-Bench, a new benchmark for evaluating large language models (LLMs) on antibody virtual screening (VS) tasks through natural language instructions. Unlike prior efforts that focus on structural annotation, binding prediction, or developability using frozen antibody models, Ab-VS-Bench targets the end-to-end VS workflow and leverages LLMs' instruction-following capabilities. The benchmark comprises three core tasks inspired by small-molecule VS: (1) Scoring-predicting antibody-antigen binding affinity, (2) Ranking-ordering antibodies by affinity or thermostability, and (3) Screening-identifying high-affinity binders from large antibody libraries. We convert experimental datasets into instructiontuning format and evaluate multiple model variants, including zero-shot LLMs, instruction-finetuned LLMs, and multimodal models enhanced with antibody-specific embeddings. Ab-VSBench provides a unified framework to benchmark LLMs for antibody discovery, aiming to catalyze progress in languageguided biotherapeutic design.

This artwork reimagines the unrealized inventions of Hollywood actress and inventor Hedy Lamarr, using generative AI and 3D printing to speculate on women’s contributions to science and technology that have been historically overlooked due to gender bias.