Field:
Chemistry/Materials Science

Session Topic:
Nature's Chemistry: Exploring Synthetic Biology and Biogenic Materials

Introductory Speaker:
YUZAWA Satoshi, Keio University
Title:

Harnessing Synthetic Biology for sustainable production of chemicals andbeyondSynthetic Biology represents a
dynamic interdisciplinary field at
the intersection of Chemistry,
Biology, and Engineering. This
innovative research area seeks
to fabricate artificial biologicalsystems, characterized by two
primary approaches: the topdown approach, involving themanipulation of existing natural
systems, and the bottom-up
approach, centered around theassemblyofchemicalandbiological components (Fig. 1).
In this lecture, I will provide a
concise overview of the historicaldevelopmentofSynthetic
Biology,includingthefirstgenome sequence of bacteria in
1995 [1], the first genome synthesis of bacteria in 2008 [2] and the first creationof artificial bacteria in 2010 [3]. Shifting focus, I will talk about instances of the
top-down approach, highlighting the burgeoning interest in genome-engineered
microbes for sustainable chemical production [4]. This includes the production oftherapeutics, fuels, flavors, colorants, health supplements, and cosmetic
ingredients from renewable nonfood biomass or carbon dioxide. The escalating
demand for bio-based products is underscored by a total investment in Synthetic
Biology startups surpassing $50 billion (~$20 billion in 2021). I will close my talkby introducing the bottom-up approach. Our session features two distinguishedspeakers: Satoshi Abe from the Tokyo Institute of Technology, Japan, discussing
research related to the bottom-up approach, and a speaker from TBD, France,
addressing topics aligned with TBD (either the top-down or bottom-up) approach.
I invite you to engage in our session and explore the multifaceted future ofSynthetic Biology. Your participation and insights are welcomed.
References:[1] Fleischmann et al. Whole-Genome Random Sequencing and Assembly ofHaemophilus influenzae Rd. Science 1995, 269, 496-512.
[2] Gibson et al. Complete Chemical Synthesis, Assembly, and Cloning of a Mycoplasma
genitalium Genome. Science 2008, 319, 1215-1220.
[3] Gibson et al. Creation of a bacterial cell controlled by a chemically synthesized genome.
Science 2010, 329, 52-56.
[4] Li et al. Mining natural products for advanced biofuels and sustainable bioproducts.
Curr Opin Biotechnol 2023, 84, 103003.
Background Review Article:[1] Jones et al. Synthetic microbiology in sustainability applications. Nat Rev Microbiol
2024, doi: 10.1038/s41579-023-01007-9.

Field:Chemistry/Materials Science

Session Topic:
Nature's Chemistry: Exploring Synthetic Biology and Biogenic Materials

Speaker:
Nicolas MARTIN, Centre de Recherche Paul Pascal

Title:

Minimal models of synthetic cells

Living cells are extremely sophisticated compartmentalized chemical systems.
They perform intricate tasks by dynamically coordinating matter and energy fluxes
across space and time. Reproducing such complex behaviours in model systems
could pave the way to the development of smart autonomous micro-machines.
Driven by the promise of these advancements, recent years have witnessed a
growing interest in constructing “synthetic cells”, namely, artificial microcompartments that mimic the cellular organization. A major challenge in thisvibrant field is imbuing synthetic cells with dynamic capabilities, including theability to sense, respond and adapt to their environment, self-sustain andcommunicate with each other. In this presentation, I will show that micrometresized droplets formed by liquid-liquid phase separation of polymers in water,
known as “coacervates”, hold promise as platforms for creating minimal models ofsynthetic cells (Figure 1). Specifically, I will discuss how these droplets can be usedas microreactors to promote simple reactions.[1] I will highlight how the design ofstimuli-responsive droplets enables precise spatiotemporal control overbiomolecule localization. I will further showcase how microfluidics offers a robust
approach to build structurally defined compartments and unravel physicalmechanisms underlying droplet growth and dissolution. Last, recent directions
toward constructing more advanced synthetic cells that integrate multiple
functions in a drop will be discussed.[2] Ultimately, these droplets provide new
avenues for understanding cellular compartmentalization while shedding light on
the transition from non-living to living matter.
References:[1] T.P. Fraccia, N. Martin, Non-enzymatic oligonucleotide ligation in coacervate
protocells sustains compartment-content coupling. Nat. Commun. 2023, 14, 2606
[2] C. Xu, N. Martin, M. Li, S. Mann, E Living material assembly of bacteriogenic
protocells, Nature, 2022, 609, 1029-1037
Glossary:

- Synthetic cells: micrometre-sized compartments that reproduce structural and/or
functional features of living cells using a small set of biological or synthetic parts.
- Liquid-liquid phase separation: spontaneous demixing phenomenon between two
immiscible liquid phases.
- Coacervates: micrometre-sized droplets formed in water by liquid-liquid phaseseparation between two immiscible aqueous solutions (that typically containwater-soluble polymers, surfactants or salts)
Background Review Article:
N. Martin, Dynamic synthetic cells based on liquid-liquid phase separation,
ChemBioChem, 2019, 20, 2553-2568

Figure 1. Design of coacervate droplets for synthetic cells assembly.

Field:Chemistry/Materials Science

Session Topic:
Nature's Chemistry: Exploring Synthetic Biology and Biogenic Materials

Speaker:
Satoshi Abe, Tokyo Institute of Technology

Title:

In-cell Protein Assemblies as Biomaterials

Proteins are the most critical functional
molecules for biological activities withvarious structures and play essential roles
in metabolism, such as signal transduction
and catalysis. They form precise molecularassemblies that synthetic chemistry cannot
form, making them exciting biomaterials
both functionality and structurally. Amongthem, I focused on the phenomenon ofprotein crystallization in vivo (Figure 1).[1]Protein crystals are often used to purify
proteins and to elucidate their threedimensional structures. However, since the Figure 1. In-cell protein
first protein crystals were reported in crystallization.
human tissues in the 1850s,
various proteins have beenfound to crystallize in naturalenvironments
andnonnatural cultured cells. In
nature,proteincrystalsfunction as solid materials
for immune activation, virus
storage, and solid catalysts.
Recently,functional
development using these incell protein crystals has been Figure 2. Crystal engineering of in-cell protein crystals.
underway,and
rapid
crystallization methods are expected to be applied to structure biology andbiomaterials science. In this presentation, I will report on our recent research on
applying in-cell protein crystals to biomaterials (Figure 2).[2] Protein crystals
encapsulating the target protein can be synthesized when in-cell crystals aresynthesized with an expression of the target protein. Solid catalysts with multiple
immobilized enzymes in the single crystals and solid materials with immobilized
protein caged assembly have been constructed. These studies are a method forsynthesizing materials using the reaction system of living organisms and can beutilized to synthesize biofunctional materials encapsulating various functional
molecules.
References:[1] M. Kojima et al. Biomater. Sci. 2021, 10, 354-367., R. Schonherr et al. Biol.
Chem. 2018, 399, 751-772 (2018)., C. N. Mudogo et al. Traffic. 2020, 21, 220230.
[2] S. Abe et al. Adv. Mater. 2015, 27, 7951-7956., T. K. Ngyuen et al. ACS Appl.
Nano Mater. 2021, 4, 1672-1681., T. T. Pham et al. Nano Lett. 2023, 23, 1011810125.
Glossary:
Background Review Article:
M. Kojima, Satoshi Abe and Takafumi Ueno. Engineering of protein crystals for use
assolid
biomaterials.
Biomater.
Sci.
2021,
10,
354-367.
DOI:
10.1039/D1BM01752G

Field:Social Sciences/Humanities

Session Topic:
New Standards of Visualizations in Social Sciences

Introductory Speaker:
Vivien PHILIZOT, Universté de Strasbourg

Title:

Visualization in the Humanities and Social Sciences:
Epistemic Perspectives

As the American historian of science Matthew Norton Wise puts it, « much of the historyof science could be written in terms of making new things visible – or familiar things
visible in a new way. » (Wise 2006, 75). If much of science is about making the invisible
visible, it is because there is a close connection between knowledge and the forms in which
it is expressed. Such connections have been the subject of much research over the past
forty years in the fields of laboratory studies and anthropological approaches to laboratory
work (Latour and Woolgar 1979; Woolgar 1982; Lynch 1982), science studies (Lynch 1985,
Hacking 1985), media studies (Bredekamp, Dünkel, and Schneider 2015) andvisualization and information studies (Tilling 1975; Tufte 1983, Friendly et al. 2006).
These contributions attest to a growing interest in visualization in science and in themateriality of the visual representations that mediate knowledge. This lecture will
introduce the epistemological issues raised by visualization as they arise at theintersection of knowledge and materiality. It will also highlight the contribution of thehumanities to practices of visualization, using an approach drawn from visual culture
studies, science studies, and information studies.
References:
Bredekamp, Horst, Vera Dünkel, and Birgit Schneider, eds. 2015. The Technical Image:
A History of Styles in Scientific Imagery. Chicago: The University of Chicago Press.
Daston, Lorraine, and Peter Galison. 2007. Objectivity. Mass: Zone Books.
Drucker, Johanna. 2014. Graphesis - Visual Forms of Knowledge Production. Harvard
University Press.
Drucker, Johanna. 2020. Visualisation: l’interprétation modélisante. Translated byMarie-Mathilde Bortolotti-Burdeau. Collection Esthétique des données 03. Paris:
Éditions B42.
Friendly, Michael, C Chen, W Härdle, and A Unwin. 2006. “A Brief History of Data
Visualization.” In Handbook of Computational Statistics: Data Visualization, 1–34.
Springer-Verlag. https://www.datavis.ca/papers/vita/Friendly06hbook.html.
Friendly, Michael, and Howard Wainer. 2021. A History of Data Visualization andGraphic Communication. Illustrated edition. Cambridge, Massachusetts: Harvard

University Press.
Funkhouser, H. Gray. 1937. “Historical Development of the Graphical Representation of
Statistical Data.” Osiris 3:269–404.
Galison, Peter. 1997. Image and Logic: A Material Culture of Microphysics. Chicago:
University of Chicago Press.
Hacking, Ian. 1985. “Do We See Through a Microscope?” In Images of Science: Essays
on Realism and Empiricism, edited by P. M. Churchland and C.A. Hooker, 132–52.
Chicago: University of Chicago Press.
Hall, Peter. (2011) 2018. “Bulles, Lignes et Fils. Comment La Visualisation del’information Façonne-t-Elle La Société ?” Back Office, no. 2, 58–73.
Kittler, Friedrich. (1990) 2017. “Real Time Analysis, Time Axis Manipulation.”Translated by Geoffrey Winthrop-Young. Cultural Politics 13 (1): 1–18.
https://doi.org/10.1215/17432197-3755144.
Larkin, Jill H., and Herbert A. Simon. 1987. “Why a Diagram Is (Sometimes) WorthTen Thousand Words.” Cognitive Science 11 (1): 65–100.
https://doi.org/10.1111/j.1551-6708.1987.tb00863.x.
Latour, Bruno. 1985. “Les ‘Vues’ de l’esprit. Une Introduction à l’anthropologie Des
Sciences et Des Techniques.” Culture et Technique, no. 14, 5–29.
Latour, Bruno. (1987) 1989. La Science En Action. Introduction à La Sociologie Des
Sciences. Paris: La Découverte.
Latour, Bruno. 1991. Nous n’avons Jamais Été Modernes. Essai d’anthropologie
Symétrique. Paris: La Découverte.
Latour, Bruno. 1993. “Le Topofil de Boa Vista Ou La Référence Scientifique - Montage
Photo-Philosophique.” Raison Pratique, no. 4, 187–216.
Latour, Bruno. (1999) 2007. “Sol amazonien et circulation de la référence.” In L’espoir
de Pandore: pour une version réaliste de l’activité scientifique, 33–82. Paris:
Éditions La Découverte.
Latour, Bruno. 2014. “The More Manipulations, the Better.” In Representation in
Scientific Practice Revisited, edited by Catelijne Coopmans, Janet Vertesi, Michael
E. Lynch, and Steve Woolgar, 347–50. MIT Press.
Latour, Bruno, and Steve Woolgar. (1979) 1988. La Vie de Laboratoire. La Production
Des Faits Scientifiques. Paris: La Découverte.
Lynch, Michael. 1985. “La Rétine Extériorisée : Sélection et Mathématisation Des
Documents Visuels.” Culture Technique, no. 14, 37–66.
Lynch, Michael. 2006. “The Production of Scientific Images. Vision and Re-Vision in the
History, Philosophy, and Sociology of Science.” In Visual Cultures of Science:

Rethinking Representational Practices in Knowledge Building And Science
Communication, edited by Luc Pauwels, 26–40. Hanover: Dartmouth College Press.

Lynch, Michael E. 1982. “Technical Work and Critical Inquiry: Investigations in a
Scientific Laboratory.” Social Studies of Science 12 (4): 499–533.
https://www.jstor.org/stable/284826.
Morton, Timothy. 2013. Hyperobjects: Philosophy and Ecology after the End of theWorld. Posthumanities 27. Minneapolis (Minn.): University of Minnesota press.
Schneider, Birgit. 2012. “Climate Model Simulation Visualization from a Visual Studies
Perspective.” WIREs Climate Change 3 (2): 185–93. https://doi.org/10.1002/wcc.162.
Schneider, Birgit, and Thomas Nocke, eds. 2014. Image Politics of Climate Change:
Visualizations, Imaginations, Documentations. Image, volume 55. Bielefeld:
Transcript.
Tilling, Laura. 1975. “Early Experimental Graphs.” The British Journal for the Historyof Science 8 (3): 193–213.
Tufte, Edward R. 1983. The Visual Display of Quantitative Information. Cheshire:
Graphic Press.

Wise, M. Norton. 2006. “Making Visible.” Isis 97 (1): 75–82.
https://doi.org/10.1086/501101.
Wittgenstein, Ludwig. (1953) 2009. Philosophical investigations. Translated by G. E. M.
Anscombe, P. M. S. Hacker, and Joachim Schulte. Rev. 4th ed. Chichester, West
Sussex, U.K. ; Malden, MA: Wiley-Blackwell.
Woolgar, Steve. 1982. “Laboratory Studies: A Comment on the State of the Art.” Social
Studies of Science 12 (4): 481–98. https://www.jstor.org/stable/284825.

Glossary:
Background Review Article:

Field:Social Sciences/Humanities

Session Topic:
New Standards of Visualizations in Social Sciences

Speaker:OTSUKA, Jun, Kyoto University

Title:

Changing Ideals of Science in the Age of AI

Scientific justification is an essential component of research activities. Researchers are tasked
with the responsibility of justifying their hypotheses to various stakeholders, such as peer
reviewers, policymakers, and the general public, to gain acceptance and implementation of
their findings or proposals. This process of justification involves demonstrating that researchoutcomes are derived from objective and rational methodologies. Key elements in thisendeavor include data encoding, visualization, and statistical inference, which collectively
ensure that research conclusions are grounded in objectively verifiable evidence and logicalreasoning. Historical studies [1-3] have shown that the evolution of these justificatory
methods—for instance, the introduction of photography, the quantification of evidence, andthe advancement of mathematical statistics—has been crucial in fostering objectivity andrationality within modern science.
However, the emergence of machine learning and AI-driven technologies presents
the potential to significantly alter the nature and role of scientific justification. The
widespread presence of big data makes manual data encoding obsolete and unnecessary.
Furthermore, the complexity and high dimensionality of contemporary data and models
challenge traditional methods of visualization and interpretation. Additionally, the shift fromconventional theory-based statistical procedures to large-scale neural networks, whose
reliability is primarily evaluated through empirical benchmark tests, marks a significant
departure in scientific inferences [4].
These transformations might enhance the objectivity and rationality ofscientific processes but at the cost of making them less accessible andcomprehensible to human understanding. The presentation will examine thepotential impacts of these changes on the ideals that have historically directedscientific inquiry from a philosophical perspective.
References:[1] Daston, L. & Galison, P. Objectivity. (Princeton University Press, 2007).
[2] Porter, T. M. Trust in Numbers. 324 (Princeton University Press, 1996).
[3] Porter, T. M. The Rise of Statistical Thinking, 1820–1900. (Princeton
University Press, 1986).
[4] Otsuka, J. Thinking About Statistics: The Philosophical Foundations.
(Routledge, 2022).

Field:Social Sciences/Humanities

Session Topic:
New Standards of Visualizations in Social Sciences

Speaker:
Pedro RAMACIOTTI MORALES, Institut des Systèmes Complexes de Paris Île-de-France
Title:

Mapping politics in social media

Political opinions underlie a vast number of studies in several disciplines. Public opinion,
political psychology and political sociology, political competition and voting behavior,polarization, agenda setting, and political communication studies often rely on data on
individual political opinions. Newer and important fields of study also rely on individual
political variables: the study of social media, misinformation, algorithmic biases, online
polarization, online social networks, and opinion dynamics also rely on individual politicalopinions as explanations or control.
Political opinion has traditionally been studied using surveys: asking individuals where
they stand on issues of relevance in politics. Some important limitations of survey research
include the cost of administration (limiting frequency and reach, impacting cross-sectional
and longitudinal studies), reliance on the truthfulness in responses (related for instanceto social desirability), and framing.
In this presentation, I will present a method for inferring multidimensional political
position estimates for large online populations using social media data (Ramaciotti et al.,2022). This method mixes Bayesian inference on interactional data and political expert
survey data. Because it builds on expert surveys, it enables inference along the cleavage
and strategic ideology and issue dimensions that are relevant for different nationalsettings. Due to independence from text data, this method enables language-independent
analysis suited for comparative studies. Relying on interactional data traces and not
elicited answers, this method overcomes some limitations of survey research related toframing and respondent truthfulness. Finally, because estimation is done at the level ofusers, these political variables can be used jointly with other data traces enabling adiversity of studies. I will illustrate this method using Twitter data collected from debates
in several countries, showing its application to the study of the spread of misinformation(Ramaciotti et al., 2023).

References:
Ramaciotti, P., Cointet, J. P., Muñoz Zolotoochin, G., Fernández Peralta, A., Iñiguez, G.,& Pournaki, A. (2022). Inferring attitudinal spaces in social networks. Social Network
Analysis and Mining, 13(1), 14.
Ramaciotti, P., Berriche, M., & Cointet, J. P. (2023, June). The geometry of
misinformation: embedding Twitter networks of users who spread fake news ingeometrical opinion spaces. In Proceedings of the International AAAI Conference on Web
and Social Media (Vol. 17, pp. 730-741).

Field:Mathematics/Informatics/Engineering

Session Topic:
Reliability of Modern Technologies in Society

Introductory Speaker:
OSEKI Yohei, University of Tokyo
Title:

Reliability of Artificial Intelligence in Society

Modern technologies have widely spread in society, thanks to the rapid development of
Artificial Intelligence (AI) and related technologies like machine learning and big data.
Importantly, modern AIs have outperformed humans in various downstream tasks suchas natural language processing (e.g. machine translation) and computer vision (e.g. image
recognition). However, those AIs, despite their super-human performance, have been
regarded as not reliable due to several issues with (1) interpretability, (2) generalizability,and (3) efficiency [1], thus generating a barrier towards a human-centric AI society where
humans and AIs can cooperate with each other [2].
1.
2.
3.

Interpretability: AIs cannot be interpreted due to their “black box” nature.
Generalizability: AIs cannot be generalized to unforeseen environments.
Efficiency: AIs cannot be trained efficiently from the small amount of data.

In this session, we will address various aspects of the reliability of AIs in society fromthe interdisciplinary perspective. Specifically, the introductory speaker first reviews thereliability of AIs in general and illustrates them in the field of natural language processing(NLP). Then, two speakers present state-of-the-art results on the reliability of AIs andfuture directions to make AIs more reliable in the fields of human-computer interaction(HCI) and cognitive robotics, respectively. In addition, the reliability of AIs in academia
like natural sciences and social sciences/humanities will also be discussed.
References:[1] Lake, Ullman, Tenenbaum, & Gershman. (2017). Building machines that learn andthink like people. Behavioral and Brain Sciences 40: e253.
[2] Cabinet Office, Government of Japan. (2019). Social Principles of Human-Centric AI.
https://www.cas.go.jp/jp/seisaku/jinkouchinou/pdf/humancentricai.pdf.
Glossary:- Natural language processing: AI in the domain of “language” (e.g. machine translation)- Computer vision: AI in the domain of “vision” (e.g. image recognition)- Human-computer interaction: a field to investigate how humans and AIs can interact- Cognitive robotics: a field to simulate human cognition in real world with AIs/robots

Field:Mathematics/Informatics/Engineering

Session Topic:
Reliability of Modern Technologies in Society

Speaker:
Baptiste CARAMIAUX, Institut des Systèmes Intelligents et de RobotiqueTitle:

Interactive AI: Bringing Technology closer to People

Résumé
Machine learning and artificial intelligence systems have shown remarkable results overthe last ten years. However, their successes have gone hand in hand with the developmentof a less open technology, more oriented towards a black-box model, i.e. one with little
transparency. This evolution has had important repercussions on society, where black-box
models were deployed in applications even though they showed significant biases. In thefield of Human-Computer Interaction (HCI), the discipline that studies the contextualized
uses of technology and how to design technology to facilitate its use, more and more work
is seeking to introduce more interactivity into AI in order to provide a means of preventing
these problems (Amershi et al., 2014; Gillies et al., 2016). More specifically, the so-calledinteractive AI approach aims to involve users more closely in the development of AI
systems, giving them more influence over the data and the model in order to audit themodel or customize it according to their needs. However, this approach brings with it anumber of challenges. On the one hand, these challenges are technical, such as designinga more efficient model that can be refined and customized on the basis of small datasets(Mishra et al. 2021). On the other hand, these challenges are linked to the definition ofevaluation measures adapted to this system. In this presentation, I will focus on the latter.
Using two examples (Sungeelee et al, 2024; Scurto et al. 2021), I will illustrate how theevaluation of an AI system needs to be considered differently from the usual methods of
AI development (e.g., based on accuracy, confusion matrix or f-score metrics) when
considering in interaction with users. In addition, I will use this AI evaluation problem asa means of triggering more fundamental questions about AI design and its impact onsociety and science. This presentation, I hope, will therefore highlight problems at thefrontiers of different sciences and with significant societal impacts.

References
Amershi, S., Cakmak, M., Knox, W. B., & Kulesza, T. (2014). Power to thepeople: The role of humans in interactive machine learning. AI magazine, 35(4), 105-120.
Gillies, M., Fiebrink, R., Tanaka, A., Garcia, J., Bevilacqua, F., Heloir, A., ... &Caramiaux, B. (2016). Human-centred machine learning. In Proceedings ofthe 2016 CHI conference extended abstracts on human factors in computing
systems (pp. 3558-3565).
Mishra, S., & Rzeszotarski, J. M. (2021). Designing interactive transfer learning
tools for ML non-experts. In Proceedings of the 2021 CHI Conference on
Human Factors in Computing Systems (pp. 1-15).
Scurto, H., Kerrebroeck, B. V., Caramiaux, B., & Bevilacqua, F. (2021).
Designing deep reinforcement learning for human parameter exploration.
ACM Transactions on Computer-Human Interaction (TOCHI), 28(1), 1-35.
Sungeelee, V., Jarrassé, N., Sanchez, T., & Caramiaux, B. (2024, March).
Comparing Teaching Strategies of a Machine Learning-based Prosthetic Arm.
In Proceedings of the 29th International Conference on Intelligent User
Interfaces (pp. 715-730).

Field:Mathematics/Informatics/Engineering

Session Topic:
Reliability of Modern Technologies in Society

Speaker:
Hiroki Mori, Future Robot Organization, Waseda University

Title:

Meaning of Meanings: Embodied structure of language

Large Language Models (LLMs) like ChatGPT are socially impactful technology, resulting in decreased
freelance jobs and income [1]. Raj et al. suggests that high-income professions like teachers andmagistrate judges are notably affected by LLMs [2]. Another notable instance is a Japanese novel
incorporating ChatGPT winning the Akutagawa Prize, a prestigious Japanese literary award. The author
revealed that about 5% of its sentences were generated by the model. This signifies the growing
acceptance of artificial intelligence even in creative activities.
The question arises: Is it safe to allow generative AI, such as LLMs, to distribute society? To address
this, we must consider whether generative AI understands "meaning." The widely used word2vec
technology, based on the "distributional hypothesis" in which a meaning of a word is determined by thesurrounding words. This means that different words that have similar words distributed around them areconsidered to have similar meanings and are placed in closed locations in a multidimensional continuous
space (embedded space). However, based on this idea, words such as "right" and "left," "fast" and"quick," etc. would be placed close to each other even though they have opposite meanings.
Our study investigated “embodied semantics” based on word allocation in the embedded space after
integrating a robot's behavior and linguistic descriptions [3]. We found that words with similar meanings
as actions moved closer together, while those with different meanings moved further apart. Thishighlights the importance of physicality in meaning comprehension.
Ultimately, the result suggests that for reliable artificial intelligence, models need to exhibit human-likesemantic understanding based on body and behavior. Concepts like cognitive developmental robotics[4] and human-in-the-loop [5] are deemed crucial for achieving reliable AI.

References:[1] Xiang Hui, Oren Reshef, Luofeng Zhou, The Short-Term Effects of Generative
Artificial Intelligence on Employment: Evidence from an Online Labor Market, SSRN:
4527336[2] Ed Felten, Manav Raj, Robert Seamans, “How will Language Modelers like ChatGPT
Affect Occupations and Industries?,” arXiv:2303.01157[3] Minori Toyoda, Kanata Suzuki, Hiroki Mori, Yoshihiko Hayashi, and Tetsuya Ogata,
Embodying Pre-Trained Word Embeddings Through Robot Actions, IEEE Robotics andAutomation Letters, Vol. 6, No. 2, 2021
[4] Minoru Asada, Koh Hosoda, Yasuo Kuniyoshi, Hiroshi Ishiguro, Member, Toshio Inui,“Cognitive Developmental Robotics: A Survey,” IEEE Transactions on Autonomous
Mental Development, Vol. 1, Issue 1, pp.12 – 34, 2009[5] Eduardo Mosqueira, Elena Hernández, David Alonso, José Bobes, Ángel Fernández,“Human‐in‐the‐loop machine learning: a state of the art,” Artificial Intelligence Review,Vol. 56, pp.3005–3054, 2023

Field:Physics/Astrophysics

Session Topic:
Time Domain Astronomy -from long time to short time-

Introductory Speaker:
Pierre MAGGI, Observatoire astronomique de Strasbourg
Title:

Time Domain Astronomy -from long time to short time- and from ancient times tomodern times

Abstract :In this introductory talk, I will offer a panorama of time-domain astronomy. Thisis the study of time-variable or transient celestial phenomena, which, I will argue, alsofits the definition of astronomy in general. I will start with an historical perspective,
showing that analysing such phenomena always lead to a change of world view in our
understanding of the Universe. Fast-forwarding to recent times, I will present the adventof the recent era of time-domain astronomy and how it affected and drove the developmentof modern radio, optical, and high-energy astronomy. I will discuss the observational
challenges of the field, and (some of) the many scientific prospects of the field.

References:
Grindlay, Tang, Los, and Servillat, IAUS 285 29, 2012
Klebesadel, Strong, and Olson, ApJL 182 L85, 1973
Pietka, Fender, and Keane, MNRAS 446 3687, 2015
Polzin, Margutti, Coppejans, et al., ApJ 959 75, 2023
Stratta,
Ciolfi,
Amati,
et
al.,
AdSpR
62
Clark and Stephenson, “The Historical Supernovae” 1977 (Oxford)

662,

2018

Glossary:
GRB : Gamma-ray bursts : A brief flash of high-energy gamma rays lasting less than afew seconds, followed by an afterglow extending from gamma-ray to radio on timescales ofminutes to weeks. Occurs either when a massive star collapse with the ejection of acollimated jet (“long” GRBs), or upon the merger of two neutron stars in a binary (“short”
GRBs)
NS: Neutron Star; the collapsed remnant of a massive stellar core, consisting mostly ofneutron packed together at ~nuclear density
Pulsar: Contraction of pulsating-star; a neutron star that emits a narrow radio beam
periodically visible from Earth, with periods ranging from a few milliseconds to a few
hours. Sometimes visible as X-ray pulsars

SN: Supernova; a bright event marking the gravitational or thermonuclear disruption ofsome stars
TDE: Tidal disruption event : When stars in the central region of galaxies get too close to
the central supermassive black holes, they may be destroyed by tidal forces. Matter fromthe disrupted star is then accreted onto the black hole, leading to enhanced emission thatis visible as a slow “flash” on timescales from weeks to years.
QPO: Quasi-periodic oscillations : A variable signal found in the light curve of many
accreting sources from NS to stellar mass or supermassive black holes, characterised by abroad line in frequency space.

Field:Physics/Astrophysics

Session Topic:
Time Domain Astronomy -from long time to short time-

Speaker:Motogi Kazuhito, Yamaguchi University
Title:

Time-domain studies on a massive protostar beyond angular resolution

Time-domain astronomy stands as one of the final frontiers in the field. Typically,
astronomical events, like star/galaxy births and cosmic element evolution, unfold
over timescales far exceeding human lifetimes. Conversely, astronomers delve
into rapidly variable phenomena under the stellar scale, measured in nanoseconds
for neutron star rotations and milliseconds to several tens of minutes for stellar
explosions. These events remain spatially unresolved even with modern
telescopes. Until now, such short and long timescale studies have progressedindependently within distinct communities. This talk introduces a new mid timescale phenomenon that bridges these short and long timescale studies.
Our monitoring observations of a very young massive protostar [1] over ~10
years have unveiled, for the first time, a periodic variation in protostellar
luminosity (Fig 1). The observed periodicity of ~1 year suggests potential stellar
pulsations [2], likely resulting from an extremely swollen atmosphere. The
expected stellar radius is approximately 400 solar radii, similar to that of a redsupergiant star. Theoretically, such pronounced swelling critically influences
protostellar evolution [3]. While suggestions from time-domain data alone remaininconclusive, the next-generation radio inteterferometers, such as ngVLA and SKA,
could resolve the protostellar surface, giving more direct and critical verification.
Unraveling the evolutionary process of massive stars is crucial for comprehending
the cosmic synthesis of heavy elements, the origins of the solar system, and theformation of neutron stars/black holes which cause various physical phenomena
such as pulsars and gravitational waves. We anticipate that time -resolved
astronomy will shed light on the evolutionary journey of massive stars from an
u n p r e c e d e n t e d
p e r s p e c t i v e .

Figure 1: (Left) The highest-resolution image of the circumstellar disk around massive
protostar G353 observed by Atakama Large Millimetr/submillimeter Array (ALMA). The
color points indicate locations of gas clumps emitting methanol maser emission and
Doppler velocities along the line-of-sight. The size of the solar system is shown in theupper-left corner. (Right) Integrated light curve for methanol maser clumps near the
protostar from MJD 59000 to 60000.
References:[1] Motogi et al. 2019, ApJL, 877, L25[2] Inayoshi et al. 2013, ApJL, 769, L20[3] Haemmerle et al. 2016, A&A, 585, A65
Glossary:
Neutron star: An extremely high-density star made of neutrons.
Protostar: A young, contracting star before the ignition of nuclear burning in thestellar core.
Massive star:
A star with a stellar mass exceeding 8 times of the sun, which eventually die inan explosion called supernova. The stellar core turn into the neutron star or
black hole, because of strong compression by backlash of the supernova.
Almost all heavy element in the universe had synthesized by massive star via
nuclear burning, supernova, and merger of binary neutron stars.
Solar radius: 7×105 km.
Red supergiant star:
An extremely swollen massive star such as Betelgeuse. They are just before
their death (~last 10 per cent of their lifetime).

Radio interferometer:
The synthesized telescope consists of multiple radio telescopes. Its angularresolution is obtained by observing radio wavelengths over each telescope's
maximum separation (baseline length), while its sensitivity is determined by
the total surface area of the telescopes.
Methanol maser:
An astronomical maser is a strong line emission analogous to a laser in optical
wavelength. Several interstellar molecules, such as methanol and water, show
maser activity in a limited volume of the circumstellar gas. Methanol molecules
cause maser activity excited by infrared emission from a massive protostar.
Therefore, a methanol maser is used as an indirect sensor of stellar luminosity.
Integrated flux:
Radio energy flux integrated over the observing frequency band. It has thesame dimension with luminosity per unit area.
MJD: Modified Julian Date

Field:Physics/Astrophysics

Session Topic:
Time Domain Astronomy -from long time to short time-

Speaker:
Cherry NG-GUIHENEUF,
Laboratoire de physique et chimie de l'environnement et de l'Espace
Title:

Digitizing the radio transient sky

The recent technological advancement has enabled astronomers to digitize large
areas of the radio sky with remarkable resolutions. This unprecedented capability
provides sensitivity to transient phenomena to which we would have otherwise
been blind. We will discuss the case of Fast Radio Bursts (FRBs), which are bright,
millisecond-duration radio bursts from extra-galactic distances. First discovered in2007 [1], our knowledge of FRBs was initially limited since only a few tens ofcataclysmic FRBs were observed. The large field-of-view and the commensal
beamforming capability of the CHIME radio telescope [2] have been a gamechanger for the study of FRBs. Thanks to the development of a real-time GPU
beamforming algorithm [3], CHIME has made thousands of FRB discoveries in thelast few years which has revolutionized the landscape of Fast Radio Burst research.
We are beginning to populate the short-timescale variability region of the radiotransient parameter space (see Fig. 1) which has until now been under-explored.
It is now apparent that FRBs are prolific, with high event rate of >5000
bursts/sky/day. FRBs have by now been detected by 16+ radio telescopes(between 110 MHz – 8 GHz). However, there is so-far no conclusive detections of

Figure 1) Radio pseudo-luminosity vs the product of observing frequency (ν) and variability timescale (W)(plot from [4]). The frequency-time spectrum of the first FRB discovered by [1] is shown as an inset.

multi-wavelength counterparts, except an FRB-like burst from the Galacticmagnetar SGR1935+2154 [5].
Despite the yet unknown origin(s) of FRBs, the fact that FRBs are the mostcompact known extragalactic sources of electromagnetic radiation means that
there are high hopes that we might be able to employ FRBs as cosmological probes[6], for example, to locate the missing baryons and to map the intergalactic
medium, which are extremely hard to probe otherwise. To do that, we will needtens of thousands of well-localized FRBs. Large-scale telescope projects such as
the $2-billion, multi-national Square Kilometre Array1(SKA) is expected to comeonline before 2030. The SKA will be the most sensitive radio telescope everconstructed and can discover many FRBs at the intermediate redshift range,
providing an orthogonal cosmological tool that will complement the existing pictureof the history of the Universe.
References:[1] Lorimer et al., 2007, Science, 318, 5851, 777[2] The CHIME/FRB Collaboration et al., 2018, ApJ, 863, 48[3] C. Ng, 2017, Proccedings of the IAU 337[4] N. Hurley-Walker et al., 2022, Nature, 601, 526–530[5] The CHIME/FRB Collaboration et al., 2020, Nature, 587, 7832, 54-58[6] V. Ravi et al., 2020, Astro2020 Decadal Survey white paper
Glossary: Transient – a temporary, non-permanent, time-varying phenomenon Extra-galactic – Outside of and beyond our Milky Way Galaxy
 Cataclysmic – one-off, non-repeating
 Field-of-view – the angular extent of the observable sky seen at a given moment
 Commensal beamforming – beamforming is a signal processing technique used inantenna arrays for directional signal reception. Commensal is the ability to
beamform in parallel, simultaneously as other signal processing processes GPU – Graphics Processing Unit
 Radio pseudo-luminosity – the apparent brightness which is the product of flux
density (S) and the distance of the object squared (d2). This provides a way tocompare the apparent brightness of different objects, assuming they are all at thesame distance.
 Observing frequency – the window of the electromagnetic spectrum being studied Variability timescale – the time duration of the transient phenomenon
 Busts/sky/day – the average event rate across the entire sky in a single day Magnetar – a type of neutron star with an extremely powerful magnetic field Cosmology – the science of the origin and development of the universe Missing baryons – the observed discrepancy of baryonic matter (composite
subatomic particles, including protons and neutrons) detected shortly after the Big
Bang with respect to more recent times.

1

SKAO website: https://www.skao.intIntergalactic medium (IGM) – the material found between galaxies which consists
mostly of hot, tenuous hydrogen gas.
Redshift – describes how much light is shifted to longer wavelengths due to the
expansion of the Universe. Combined with the Hubble relation, a distance can becalculated from a redshift value.

Field:Earth Science/Geosciences/Environment

Session Topic:
Uncovering Paleo Disasters and Human Impacts on Modern Earth Science

Introductory Speaker:
Ishimura Daisuke, Tokyo Metropolitan University
Title:How do we know paleo-hazards and link them to future disaster risk
assessment?

The past is the key to the present as well as “the present is the key to the past”.
In particular, when considering present-day low-frequency disasters (e.g., earthquake,tsunami, volcanic eruption), the present instrumental record alone is not a sufficientperiod of observation to assess the frequency, intensity, and magnitude of the hazards inthe geologic time scale. Therefore, we are trying to assess the disaster risk by uncovering
past disasters (i.e. natural hazards). In an active margin like Japan, a variety of natural
hazards have occurred from the past, and they have been regarded as disasters that havestruck people. It is said that the history of Japan is also the history of disasters. In Japan,
the history of how ancient peoples resist natural hazards has been preserved in documents
and artifacts, and this knowledge has been handed down from generation to generation.
At the same time, these natural hazards have greatly influenced the landscape and cultureof Japan, and people have also had their benefits.
This session topic is to know past disasters and natural hazards for the benefit ofearth science and future human activities. In this presentation, we will use Japan as an
example to show how we can uncover past natural disasters and relate them to humanactivities. It will also introduce low-frequency disasters and increasingly severe
meteorological disasters that have struck Japan in recent years. Through these stories,
we hope to provide an opportunity to think about the global environment in the Holocene
epoch, when the climate environment became the way it is today, and in the Anthropocene
epoch, when modern industry began to influence the global environment.
References:[1][2]Glossary:- Active margin: A zone on the Earth's surface where crustal deformation and seismic
activity are active. It locates along plate boundaries.
- Holocene: The most recent geological epoch from 11,700 years ago to the present. It
follows the last glacial period and is the current warm period in the glacial-interglacial
cycle.
- Anthropocene: It is a hypothetical geological epoch following the Holocene, proposed as
the starting point for humans to have a significant impact on the Earth's geology andecosystems.

Background Review Article:

Field:Earth Science/Geosciences/Environment

Session Topic:
Uncovering Paleo Disasters and Human Impacts on Modern Earth Science

Speaker:
Anaelle SIMONNEAU, ISTO, UMR 7327, Univ Orleans, CNRS, BRGM, OSUC, F-45071
Orléans, France

Title:

Flash flood events as Paleo disasters & their effects on human societies

In the last 20 years, flooding has been the most common natural disaster by far,accounting for 43% of all recorded events. The IPCC (Intergovernmental Panel On Climate
Change (Ipcc), 2023) report states that a flood event can be defined as “a complex interplayof hydrology, climate, and human management, and that the relative importance of thesefactors varies for different flood types and regions.”. At the continental and regional scales,
the projected changes in floods are uneven in different parts of the world, but there is alarger fraction of regions with an increase than with a decrease over the 21st century(Wilhelm et al., 2022). Researchers are however more confident at global than at regional
scale which need today more investigations. A key feature of paleoflood records isvariability in flood recurrence at centennial timescales, although constraining climateflood relationships remains challenging.
Paleofloods can be reconstruct using documentary evidence (grape harvest data,
religious
documents,
newspapers,
logbooks);model-based
analyses;or
paleoreconstructions (i.e. natural archives such as speleothems (Denniston and Luetscher,2017), spatial patterns of natural environment, lake sediments Simonneau et al., 2013).
Reconstructing palaeo-floods in pre-instrumental information, reconstruct from lake
sediment, can particularly help to put events occurring in the instrumental record(referred to as ‘observed’) in a longer-term context (Simonneau et al., 2013), especially overthe last 2000 years (Wirth et al., 2013a), and examine climate and meteorological trends
based on reference levels discussing natural (Wirth et al., 2013b) and human forcing(Brisset et al., 2017). Objectives are 1/ to determine quantitative information for specific
events, such as the timing, peak discharge, and maximum stage of an individual flood orfloods, and 2/ investigate more general spatial and temporal patterns of flooding,commonly to assess relations among climate, land use, flood frequency and magnitude,
and geomorphic response. Methods consist in stratigraphic correlation and mapping,visual description of sediment cores, high-resolution imaging, but also based on
destructive analysis to quantify particle size distribution, sediment density, magnetic
properties, mineral and isotopic geochemistry of the sediment and organic matter content
and composition.

References:
Brisset, E., Guiter, F., Miramont, C., Troussier, T., Sabatier, P., Poher, Y., Cartier, R.,
Arnaud, F., Malet, E., Anthony, E.J., 2017. The overlooked human influence in historicand prehistoric floods in the European Alps. Geology 45, 347–350.
https://doi.org/10.1130/G38498.1
Büntgen, U., Tegel, W., Nicolussi, K., McCormick, M., Frank, D., Trouet, V., Kaplan, J.O.,
Herzig, F., Heussner, K.-U., Wanner, H., Luterbacher, J., Esper, J., 2011. 2500 Years ofEuropean Climate Variability and Human Susceptibility. Science 331, 578–582.
https://doi.org/10.1126/science.1197175
Denniston, R.F., Luetscher, M., 2017. Speleothems as high-resolution paleoflood archives.
Quaternary Science Reviews 170, 1–13. https://doi.org/10.1016/j.quascirev.2017.05.006
Intergovernmental Panel On Climate Change (Ipcc), 2023. Climate Change 2022 –Impacts, Adaptation and Vulnerability: Working Group II Contribution to the Sixth
Assessment Report of the Intergovernmental Panel on Climate Change, 1st ed. Cambridge
University Press. https://doi.org/10.1017/9781009325844
Simonneau, A., Chapron, E., Vannière, B., Wirth, S.B., Gilli, A., Di Giovanni, C.,
Anselmetti, F.S., Desmet, M., Magny, M., 2013. Mass-movement and flood-induced
deposits in Lake Ledro, southern Alps, Italy: implications for Holocene palaeohydrology
and natural hazards. Clim. Past 9, 825–840. https://doi.org/10.5194/cp-9-825-2013
Soon, W., Velasco Herrera, V.M., Selvaraj, K., Traversi, R., Usoskin, I., Chen, C.-T.A., Lou,
J.-Y., Kao, S.-J., Carter, R.M., Pipin, V., Severi, M., Becagli, S., 2014. A review of Holocene
solar-linked climatic variation on centennial to millennial timescales: Physical processes,
interpretative frameworks and a new multiple cross-wavelet transform algorithm. EarthScience Reviews 134, 1–15. https://doi.org/10.1016/j.earscirev.2014.03.003
Wilhelm, B., Amann, B., Corella, J.P., Rapuc, W., Giguet-Covex, C., Merz, B., Støren, E.,
2022. Reconstructing Paleoflood Occurrence and Magnitude from Lake Sediments.
Quaternary 5, 9. https://doi.org/10.3390/quat5010009
Wirth, S.B., Gilli, A., Simonneau, A., Ariztegui, D., Vannière, B., Glur, L., Chapron, E.,
Magny, M., Anselmetti, F.S., 2013a. A 2000 year long seasonal record of floods in thesouthern European Alps: FLOOD SEASONALITY IN THE SOUTHERN ALPS. Geophys.
Res. Lett. 40, 4025–4029. https://doi.org/10.1002/grl.50741
Wirth, S.B., Glur, L., Gilli, A., Anselmetti, F.S., 2013b. Holocene flood frequency acrossthe Central Alps – solar forcing and evidence for variations in North Atlantic atmosphericcirculation.
Quaternary
Science
Reviews
80,
112–128.
https://doi.org/10.1016/j.quascirev.2013.09.002
Glossary: N.D.
Background Review Article: (Soon et al., 2014) (Büntgen et al., 2011)
Büntgen, U., Tegel, W., Nicolussi, K., McCormick, M., Frank, D., Trouet, V., Kaplan, J.O.,
Herzig, F., Heussner, K.-U., Wanner, H., Luterbacher, J., Esper, J., 2011. 2500 Years ofEuropean Climate Variability and Human Susceptibility. Science 331, 578–582.
https://doi.org/10.1126/science.1197175
Soon, W., Velasco Herrera, V.M., Selvaraj, K., Traversi, R., Usoskin, I., Chen, C.-T.A., Lou,
J.-Y., Kao, S.-J., Carter, R.M., Pipin, V., Severi, M., Becagli, S., 2014. A review of Holocene
solar-linked climatic variation on centennial to millennial timescales: Physical processes,
interpretative frameworks and a new multiple cross-wavelet transform algorithm. EarthScience Reviews 134, 1–15. https://doi.org/10.1016/j.earscirev.2014.03.003

Field:Earth Science/Geosciences/Environment

Session Topic:
Uncovering Paleo Disasters and Human Impacts on Modern Earth Science

Speaker:
NAKANISHI Ryo, Kyoto University (Graduate School of Science)

Title:

Reconstruction of paleo earthquakes from geological archives and modeling

Because M~9 earthquakes, which are called “giant earthquakes”, are due toinfrequent recurrence intervals with several hundred to several thousand years [1], it isdifficult to understand their recurrence intervals and rupture zones of with the limited
instrumentally observations and historical documents of the past hundred years.
Therefore, it is necessary to extend the paleoseismic record to several thousand years
using geological archives such as tsunami deposits and seismic turbidites. Tsunami
deposits are mainly sand transported by tsunamis, and their distribution has been used
to estimate the inundation area of paleotsunamis. Tsunami simulations can provide therupture zone of earthquakes by inverting the tsunamigenic crustal deformation.
Estimated faults based on the tsunami deposits suggest the maximum magnitude and therecurrence intervals of the giant earthquake, and their tsunami inundation area is thebasis for disaster prevention and evacuation of residents.
Glacial sea-level fall over hundreds of meters constrains our understanding of thetsunami history on land; however, the seafloor sediments have the potential to reveal
seismic history even more ancient ages. Turbidites formed by seismic motions may serve
as “natural seismometer” [2].
The geological archives are the only evidence available to examine natural
hazards prior to historical documents; however, the data available are limited due toartificial alterations and environmental changes. The combination of deep learning andnumerical modeling has the potential to overcome problems such as little data andcomputational loads for inversion [3], and to advance paleoseismic reconstruction research
to a new stage.
References:[1] Philibosian, B., Meltzner, A.J., 2020. Segmentation and supercycles: A catalog ofearthquake rupture patterns from the Sumatran Sunda Megathrust and other wellstudied faults worldwide. Quaternary Science Reviews 241, 106390.
https://doi.org/10.1016/j.quascirev.2020.106390[2] Hayward, B., Sabaa, A., Howarth, J., Orpin, A., Strachan, L., 2022. Foraminiferal
evidence for the provenance and flow history of turbidity currents triggered by the 2016

Kaikōura Earthquake, New Zealand. New Zealand Journal of Geology and Geophysics 1–
14. https://doi.org/10.1080/00288306.2022.2103157[3] Mitra, R., Naruse, H., Abe, T., 2020. Estimation of Tsunami Characteristics from
Deposits: Inverse Modeling Using a Deep-Learning Neural Network. Journal ofGeophysicalResearch:
Earth
Surface125,
e2020JF005583.
https://doi.org/10.1029/2020JF005583
Glossary:- Turbidite: A turbidite is the sediment by a turbidity current, which is a type of
sediment gravity flow responsible for distributing vast amounts of clastic sediment into
the deep ocean.
Background Review Article:- Nakanishi, R., Okamura, S., Yokoyama, Y., Miyairi, Y., Sagayama, T., Ashi, J., 2020.
Holocene tsunami, storm, and relative sea level records obtained from the southern
Hidaka coast, Hokkaido, Japan. Quaternary Science Reviews 250, 106678.
https://doi.org/10.1016/j.quascirev.2020.106678- Nakanishi, R., Ashi, J., Miyairi, Y., Yokoyama, Y., 2022. Spatial Extent of Mid-To LateHolocene Sedimentary Record of Tsunamis Along the Southern Kuril Trench, Hokkaido,
Japan.
Geochemistry,
Geophysics,
Geosystems
23,
e2022GC010334.
https://doi.org/10.1029/2022GC010334

Field:Biology / Life Sciences

Session Topic:
Unlocking the Secrets: Exploring Neurotechnology

Speaker:
Léa PILLETTE, Univ. Rennes, Inria, CNRS, IRISA, Rennes, France

Title:

Neurotechnologies – From brain activity acquisition to neurofeedback

Advances in stem cell technology and advanced imaging of neuronal dendrites open new
perspectives for neurotechnologies research and in particular for neurofeedback. Neuronalorganoids derived from human stem cells enable the modeling of complex brain structuresin vitro, providing a platform to study neural development and associated dysfunctions.
Concurrently, the use of nonlinear 3D acousto-optic lens two-photon microscopes allows
real-time observation of dendritic activity in awake animals, revealing specificmodulations of neuronal activity in response to behaviors. These converging technologies
can be integrated into the field of neurofeedback, where real-time measured neural signals
are used to provide feedback to people in order to train them to modulate their brain
activity [1]. Neurofeedback is often used to improve cognitive abilities in clinicalapplications such as improving the motor abilities of patients after a stroke [2] or people
with Parkinson’s disease [3]. An in-depth understanding of neuronal dynamics fromorganoids and animal models could enhance the precision and efficacy of neurofeedback
protocols, paving the way for more targeted and personalized therapies for various
neurological and psychiatric conditions.
Review Article References:[1] Batail, J. M., Bioulac, S., Cabestaing, F., Daudet, C., Drapier, D., Fouillen, M., ... &Vialatte, F. (2019). EEG neurofeedback research: A fertile ground for psychiatry?.
L'encephale, 45(3), 245-255.
[2] Bai, Z., Fong, K. N., Zhang, J. J., Chan, J., & Ting, K. H. (2020). Immediate and longterm effects of BCI-based rehabilitation of the upper extremity after stroke: a systematic
review and meta-analysis. Journal of neuroengineering and rehabilitation, 17, 1-20.
[3] Matzilevich, E. U., Daniel, P. L., & Little, S. (2024). Towards therapeutic
electrophysiological neurofeedback in Parkinson's disease. Parkinsonism & Related
Disorders, 106010.

Field:Biology / Life Sciences

Session Topic:
Unlocking the Secrets: Exploring Neurotechnology

Speaker:
SAKAGUCHI Hideya, RIKEN Center for Biosystems Dynamics Research

Title:

Stem Cell-Derived Neural Organoids-History, Current Status, Future Perspectives, and Ethical aspects-

The advance of stem cell technology has enabled researchers to generateorganoids: stem cell-derived three dimensional (3D) tissues with biologicalstructures and functions(Eiraku et al. 2008, Kadoshima et al. 2013, Lancaster et
al. 2013). By application of organoid technology with human pluripotent stem cells,organoids can provide a novel platform to study human biology in a dish. Usinghuman embryonic stem cells, we have succeeded in the generation of 3D neuraltissues including cerebral cortex, hippocampus, choroid plexus, and spinal cord
(Kadoshima et al. 2013, Sakaguchi et al. 2015, Ogura and Sakaguchi et al. 2018,
Sakaguchi et al. 2019). The neural organoid technology enables to study several
aspects of neural development including neural function/dysfunction in humansystem. Thus, the neural organoids will bring us many advantages, such as
facilitating disease modeling, drug discovery, and regenerative medicine in the
future; on the other hand, the generation of human neural tissues with in vivo-likestructures and functions may induce ethical concerns such as possible emergenceof consciousness in cerebral organoids.
In this presentation, we first overview the history of neural organoid research,and then introduce our achievements of the generation of regionalized neuralorganoids with functional neural network. And lastly, we will share future
perspectives of neural organoid technology from multiple viewpoints related tobiology, medicine, and ethics.

References:[1] Eiraku M, et al：Cell Stem Cell, 3, 519-532, 2008[2] Kadoshima T, et al：Proc. Natl. Acad. Sci. USA, 110, 20284-20289, 2013
[3] Lancaster MA et al：Nature, 501(7467), 373-379, 2013[4] Sakaguchi H, et al：Nature Communications, 6, 8896, 2015[5] Ogura T & Sakaguchi H, et al：Development, 145(16):dev162214, 2018[6] Sakaguchi, H, et al：Stem Cell Reports 13(3): 458-473, 2019.

Glossary:- Organoid: Pluripotent/Tissue stem cell-derived structures generated in a dish that
display features of 3-dimensional architectures and physiologies of thecorresponding organ in vivo. Regarding neural organoids, cerebral, retinal,hippocampal, choroid plexus, thalamus, hypothalamus, pituitary, cerebellar,
midbrain, spinal cord organoids as well as organoids which have some of theregions in one aggregate have been reported.
- Pluripotent Stem Cell: Pluripotent stem cells are cells that have the capacity to
self-renew by cell division and to develop into the three primary germ cell layersof the early embryo which means all types of cells of the adult body. Embryonicstem cells and induced pluripotent stem cells are examples of pluripotent stem
cells.
Background Review Article:
Sasai Y：Cell Stem Cell, 12, 520-530, 2013
Sakaguchi H & Takata N, Handbook of Stem Cell Applications 1-19, 2023

Field:Biology / Life Sciences
Session Topic:
Unlocking the Secrets: Exploring Neurotechnology
Speaker:
Antoine VALERA, Institut des Neurosciences Cellulaires et Intégratives

Title:

Decoding Activity Patterns Across Pyramidal Cell Dendritic Trees During
Spontaneous Behaviors Using 3D Arboreal Scanning

Our understanding of how sensorimotor information is integrated across dendritic trees
is limited by our ability to image these distributed 3D structures in awake animals. We
overcome the existing limitations of rapidly measuring 3D dendritic activity andavoiding signal distortion by brain motion artefacts by utilizing our novel nonlinear 3D
acousto-optic lens two-photon microscope with real-time 3D brain motion correction andinnovative Ca2+ imaging analysis pipelines [1]. This enables the study of sensorimotor
information representation in dendritic activity patterns of awake mice during natural
behaviors. We imaged layer 2/3 neurons of primary motor cortex, a representationallyrich sensorimotor integration hub that plays a key role in modulating primitive
movements like locomotion and whisking. Consistent with previous work, we found thata strong, highly correlated cell-wide Ca2+ signal is the dominant activation mode in vivo.
We find independent branch-specific local events are rare, and when observed, arelargely accounted for by contaminating signals. Applying nonlinear dimensionality
reduction techniques to the global signals, we discovered specific dendritic regions thatcovary with uninstructed spontaneous movements. These modulations (spread over >20
μm compartments) were superimposed on the global signals. Dendritic modulations were
dynamic across behavioral epochs including quiet-rest, exploration, active whisker touch,and unexpected sensory surprise. Remarkably, regression analyses suggest that thesemodulations in the activity of dendritic segments are more informative about specificbehaviors than the activity at the soma in the majority of cells. Our results indicate thatL2/3 pyramidal cell dendritic activity patterns are multidimensional and represent
several innate behavioral features simultaneously

References:[1] Griffiths VA, Valera AM, Lau JY, et al. Real-time 3D movement correction for twophoton imaging in behaving animals. Nat Methods. 2020;17(7):741-748.
Glossary:- Calcium Imaging: An optical technique that uses fluorescent indicators to visualize
calcium ions in cells, often used as a proxy to study neuron electrical activity.
- Dendritic Tree: Neurons are made of compartments. Information enters the neuron
from neighboring neurons through the dendrites, converges at the neuron soma, and issent (or not) to downstream neuronal targets through the axon.
- Acousto-Optic Lens: A device that uses sound waves to control the direction of a laser
beam, enabling precise focusing for imaging.
- Action Potential: A rapid rise and subsequent fall in voltage or membrane potential
across a cellular membrane, the primary way neurons communicate.
- Back-Propagating Action Potential: An action potential that travels back into thedendritic tree of a neuron, rather than just along the axon.
Background Review Article:
Spruston, N. Pyramidal neurons: dendritic structure and synaptic integration. Nat Rev
Neurosci 9, 206–221 (2008).