Biology subjects

Seno, S.

Publications and source records attributed to Seno, S..

6 recordsLinked to original sources

bioRxiv2026

Two closely related RNA helicases, UAP56 and URH49, couple mRNA splicing and export pathways with their distinct complexes

Transcription and mRNA processing are integrated into appropriate mRNA export pathway(s) enabling fine-tuned gene expression. The closely related RNA helicases, UAP56 and URH49, form distinct complexes known as the apo-TREX and the apo-AREX complexes, respectively, which remodel into the ATP-TREX complexes, thus forming multiple mRNA export pathways. However, the mechanisms by which helicases and their complexes shape these pathways remain unclear. Here, we showed that UAP56 and URH49 play general roles in mRNA splicing ahead of export. Further analyses revealed three patterns of splicing regulation and their association with mRNA export: Type I, in which both helicases redundantly or synergistically regulate splicing within the same transcripts, associated with cooperative mRNA export; Type II, in which either helicase preferentially regulates splicing in distinct transcripts; and Type III, in which the helicases exert opposing splicing effects within the same transcripts. Type II and III regulation was associated with selective mRNA export. Moreover, we demonstrated that the distinct complex formation influenced the splicing patterns regulated by the two helicases. Our findings reveal an additional layer of transcriptome regulation mediated by UAP56, URH49, and their complexes, extending beyond their canonical roles in mRNA export.

molecular biology↗

bioRxiv2026

Collective AI use is associated with researcher engagement: Real-time evidence from a scientific conference

Recent large-scale bibliometric analyses suggest that individual AI use can increase productivity while reducing downstream engagement and topic diversity. Here we ask whether collective AI deployment is associated with shared engagement. Using an Audience Response Engagement (ARE) system at NGS Expo 2025 (N=110 biomedical researchers), we captured real-time consensus and generated updated visualizations within minutes. Our data reveal a substantial gap between adoption and transparency: 93.6% of researchers use AI at least weekly, yet only 5.5% consistently disclose this usage--a 17-fold disparity. This pattern is consistent with systemic policy uncertainty (39.1% report unclear guidelines). Behavioral clustering identifies a "High-Concern" group (31%) as a candidate for targeted interventions: highest productivity yet lowest disclosure. These findings suggest that collective AI deployment in physical settings is associated with shared engagement.

scientific communication and education↗

bioRxiv2026

Modeling Time to Visual Insight in Mooney Image Recognition with a Chaotic Recurrent Neural Network

Insight is often described as a sudden shift in or formation of a conceptual representation, enabling humans to restructure existing knowledge and solve problems beyond conventional analytical approaches. Although prior computational studies have modeled aspects of insight using deep neural networks (DNNs) or reinforcement learning, few have captured the dynamic emergence of insight through autonomous neural computation. Here, we present a neural network model that simulates the time required to reach visual insight in the Mooney image recognition task, a widely used paradigm for studying visual insight and perceptual reorganization. The model couples a DNN module for perceptual feature extraction with a recurrent neural network (RNN) that implements a chaotic search process for recognition. The RNN is formulated as a continuous-time dynamical system, autonomously explores internal states, and stabilizes when the missing visual features required for recognition are internally reconstructed. Using the same image set as in human psychophysical experiments, the model reproduces key statistical properties of human search times (STs), including (i) lognormal-like ST distributions across participants for each image, (ii) a proportional relationship between the log-scale mean and standard deviation estimated from lognormal fits across images, and (iii) discrete levels of the fitted log-scale mean across images (a proxy for image difficulty). Importantly, these properties emerge without assuming any lognormal distribution for participant-to-participant variability, whereas previous models reproduced similar signatures by positing lognormal-distributed individual differences. We further show that lognormal-like signatures can arise from exponential search dynamics when combined with both standard experimental preprocessing and finite observation windows, highlighting the need to distinguish generative mechanisms from measurement and analysis effects. Together, these results motivate a mechanistic link between chaotic neural dynamics and insight-related search and provide a computational framework for implementing insight in artificial systems.

neuroscience↗

bioRxiv2025

Superior colliculus coordinates whole-brain dynamics for sudden unconscious visual insight

The human capacity for sudden insight, often marked by abrupt and illuminating "eureka" moments, has long intrigued neuroscientists seeking to understand its elusive neural basis1-6. Mooney image recognition offers a compelling behavioral paradigm of this phenomenon, in which ambiguous black-and-white patterns are abruptly perceived as coherent objects7-13. Whole-brain cortical involvement, which is mainly revealed by the contrast between recognition and nonrecognition, is well established8,14-27. However, the neural dynamics underlying the transition from nonrecognition to recognition, particularly the contributions of noncortical structures, have remained largely unknown. Here, we show that the transition in whole-brain dynamics can be effectively characterized by three large-scale activity patterns and that the superior colliculus (SC) emerges as a structure with a distinct temporal profile that is potentially critical for insight. Using functional clustering of functional magnetic resonance imaging data comprising 41,446 time points from 14 human participants performing this insight task, we identified three distinct functional clusters exhibiting stimulus-driven activation, suppression, and recognition-associated patterns. Notably, the SC in the third cluster displayed a distinct activation peak immediately before the recognition response. Furthermore, empirical dynamic modeling revealed that the SC was the only region in the whole brain that exhibited mutual positive directed interactions with all three clusters and was functionally embedded within higher-order cortical interactions mediating the transformation from stimulus input to motor output. Our findings provide compelling experimental evidence that the SC plays a critical role in orchestrating whole-brain dynamics for sudden insight, calling for a reappraisal of this evolutionarily conserved structure as a key player supporting unconscious but high-level cognitive processing.

neuroscience↗

bioRxiv2020

A model ecosystem of twelve cryopreservable microbial species allowing for a non-invasive approach

Simultaneous understanding of both individual and ecosystem dynamics is crucial in an era marked by the degradation of ecosystem services. Herein, we present a high-throughput synthetic microcosm system comprising 12 functionally and phylogenetically diverse microbial species. These species are axenically culturable, cryopreservable, and can be measured noninvasively via microscopy, aided by machine learning. This system includes prokaryotic and eukaryotic producers and decomposers, and eukaryotic consumers to ensure functional redundancy. Our model system displayed both positive and negative interspecific interactions and higher-order interactions that surpassed the scope of any two-species interaction. Although complete species coexistence was not our primary objective, we identified several conditions under which at least one species from the producers, consumers, and decomposers groups, and one functionally redundant species, persisted for over six months. These conditions set the stage for detailed investigations in the future. Given its designability and experimental replicability, our model ecosystem offers a promising platform for deeper insights into both individual and ecosystem dynamics, including evolution and species interactions.

microbiology↗

bioRxiv2020

SC-JNMF: Single-cell clustering integrating multiple quantification methods based on joint non-negative matrix factorization

AO_SCPLOWBSTRACTC_SCPLOWUnsupervised cell clustering is important in discovering cell diversity and subpopulations. Single-cell clustering using gene expression profiles is known to show different results depending on the method of expression quantification; nevertheless, most single-cell clustering methods do not consider the method. In this article, we propose a robust and highly accurate clustering method using joint non-negative matrix factorization (joint NMF) based on multiple gene expression profiles quantified using different methods. Matrix factorization is an excellent method for dimension reduction and feature extraction of data. In particular, NMF approximates the data matrix as the product of two matrices in which all factors are non-negative. Our joint NMF can extract common factors among multiple gene expression profiles by applying each NMF to them under the constraint that one of the factorized matrices is shared among the multiple NMFs. The joint NMF determines more robust and accurate cell clustering results by leveraging multiple quantification methods compared to the conventional clustering methods, which uses only a single quantification method. In conclusion, our study showed that our clustering method using multiple gene expression profiles is more accurate than other popular methods.

bioinformatics↗