and Masaru Todorik

ABSTRACTS
2024.MARCH 15TH

Organized by Kantaro Fujiwara, Hiroyuki Yasuda, and Masaru Todoriki
Hosted by Aihara Moonshot Project and IRCN, UTokyoISNSM2024

ProgramChair : Gouhei Tanaka / Nagoya Institute of Technology

09:30 - 10:05

Opening Remarks

10:05 - 10:40

Shun-ichi Amari / Teikyo University

10:50 - 11:25

Klaus-R. Müller / Technische Universität Berlin

11:25 - 12:00

James A. Yorke / University of Maryland

13:30 - 14:05

Gen Sobue / Aichi Medical University
Takao Hensch / The University of Tokyo
Kazuyuki Aihara / The University of Tokyo
Wasserstein Statistics

Machine Learning and AI
for the Sciences: toward understanding

What is the graph of a dynamical system?

Chair : Takamitsu Watanabe / The University of Tokyo

Leon Glass / McGill University

Using Mathematics to Diagnose, Cure,and Predict Cardiac Arrhythmias

14:05 - 14:40

Shigenobu Kanba / Kyushu University

14:40 - 15:15

Kohei Miyazono / The University of Tokyo

Multifactorial Nature of Psychiatric Disorders

and metastasis

15:30 - 16:05

Chair : Tetsushi Ueta / Tokushima University

Celso Grebogi / University of Aberdeen
Tipping Point Impacted by Multiplexity
in Mutualistic Ecological Networks

16:05 - 16:40
16:40 - 17:15

Henk Nijmeijer / Eindhoven University of Technology

Synchronization of multiple oscillators,
the role of coupling and geometry

Jürgen Kurths / Potsdam Institute for Climate Impact Research
Forcing of teleconnections among tipping elementsin the climate system

17:15 - 17:25

Closing of the talk session
Chair : Shinji Nakaoka / Hokkaido University

17:40 - 20:10

Poster Session with meal

20:10 - 20:20

Closing

International Symposiumon Nonlinear Science and Medicine

Shun-ichi Amari
Teikyo University

10:05 - 10:40

Wasserstein Statistics
Probability plays a fundamental role in information sciences. There are two
important geometrical structures introduced in the manifold of a statisticalmodel, that is a parametric family of probability distributions. One isinformation geometry based on the invariance principle of Chentsov, requestingthat the geometry depends only on the sufficient statistics. The other is
Wasserstein geometry related to the transportation problem. Roughly speaking,information geometry gives a distance or divergence measure between two
probability distributions p(x) and q(x) based on their vertical difference,
whereas Wasserstein uses their horizontal difference.
Modern statistics has so far been constructed based on the likelihood principle.
Information geometry gives a unified framework for statistical inference,
proving the superiority the maximal likelihood estimator. Wasserstein geometry
concerns with deformations and transformations of probability distributions andis useful for vision analysis and others.
We study Wasserstein statistical inference based on the Wasserstein geometry.
The Wasserstein estimator is the one that minimizes the Wasserstein distancefrom an observed empirical distribution to the model distribution. We apply the
results by Zhou and Li, establishing a unified framework between the twogeometries. We use the affine deformation statistical model (sometimes called
the location-scatter model), aiming at separating the effects of the waveform ofa probability distribution and its location and affine transformation.
The Wasserstein estimator is consistent but not Fisher efficient in general (it isFisher efficient only for the Gaussian case). The Wasserstein estimator is proved
to be the moment estimation in the case of the affine deformation model. It is
Wasserstein-efficient, implying that it is robust against waveform changes due
to observation noises, whereas the maximum likelihood estimator is not. Our
theory gives a characterization of the Wasserstein estimator, giving a starting
point for the study of Wasserstein statistics.

Klaus-R. Müller
Technische Universität Berlin

10:50 - 11:25

Machine Learning and AI
for the Sciences:
toward understanding
In recent years, machine learning (ML) and artificial intelligence (AI)methods have begun to play a more and more enabling role in thesciences and in industry. In particular, the advent of large and/or complex
data corpora has given rise to new technological challenges andpossibilities. In his talk, Müller will touch upon the topic of ML
applications in the sciences, in particular in chemistry and physics. He
will also discuss possibilities for extracting information from machinelearning models to further our understanding by explaining nonlinear ML
models. Finally, Müller will briefly discuss perspectives and limitations.

11:25 - 12:00

What is the graphof a dynamical system?
Shun-ichi Amari
Teikyo University

Frequently we describe a dynamical system in terms of its attractors, but thatcan leave out important information about unstable invariant sets.
A directed graph of a dynamical system is a picture that outlines how the various
invariant sets of a dynamical system are related. The simplest example is of thelogistic map. Its graph can be extremely complicated. There are dynamicalsystems with infinitely many attractors, and we want to know how these are
dynamically related.

James Yorke
University of Maryland

TS
Leon Glass

McGill University

http://www.medicine.mcgill.ca/physio/glasslab/

13:30 - 14:05

Using Mathematics
to Diagnose, Cure, andPredict Cardiac Arrhythmias
Advances in basic science often lead to new technologies. In this talk I will
discuss efforts to translate insights about cardiac dynamics derived fromexperiments and nonlinear dynamical mathematical models to cardiology. I will
focus on three different clinical problems: diagnosing arrhythmias using
wearable devices, curing arrhythmias, and predicting the risk and time of onset
of fatal arrhythmias. Despite the successes I will mention in my talk, we still do
not have a good understanding of how to decode the complex rhythms thathearts continuously generate. Tackling this problem is a potential direction for
the Moonshot Project.

14:05 - 14:40

Multifactorial Natureof Psychiatric Disorders
Shigenobu Kanba

Kyushu University

Everyone agrees that there is no doubt that mental disorders are multifactorial,
polygenic syndromes. It is old report but very important study (Clair D, Lancet 1990)
of a large Scottish family spanning four generations, with dominant inheritance of atranslocation occurring on the chromosome 11. A variety of psychiatric disorders
have been recognized, including recurrent depression, generalized anxiety,schizophrenia spectrum disorders, and so forth. Another example is the 22q deletion
syndrome (DiGeorge syndrome). In this syndrome, more than 40 genes are deleted,and patients develop abnormalities in multiple organs. The phenotype of psychiatricdisorders also varies with development. In childhood, these include ADHD, ASD, andanxiety disorders, and in adulthood mood disorders and schizophrenia.
The interplay between genes and the environment is often encapsulated in thephrase "genes load the gun, and the environment pulls the trigger." Today, thetechnology to study the pathophysiology of mental disorders has advanced
dramatically, spanning genomes, proteins, cells, and neural circuits. The obtained
data will be used to comprehensively elucidate pathological conditions byconnecting multiple layers of molecules, cells, circuits, and mental and behavioral
symptoms by making full use of novel integrated analysis techniques in mutualtranslation between clinical and basic research.
However, future research on mental disorders must also turn its focus to theenvironment. Environmental factors include those involved in gene expression at
the molecular level, such as cytokines, oxidative stress, trophic factors, and thecellular environment in the brain otherthan neurons, such as glial cells.
Natural environments, including
viral infections and starvation,
are recognized risk factors forschizophrenia. Equallyimportant is psychologicalenvironment.
A quantum leap in data sciencefacilitates the integrationof biological information into
psychosocial indexes,
unveiling the true picture of
psychiatric disorders.

14:40 - 15:15

in cancer invasion
and metastasis
Kohei Miyazono
The University of Tokyo

roles in cancer invasion and metastasis. We have recently studied the roles ofgrowth and differentiation of the glioblastoma-initiating cells (GICs). ThroughRNA-sequencing analysis of GICs treated or not with BMP-4, we have foundthat expression of herpes virus entry mediator (HVEM, also known as tumor
necrosis factor receptor superfamily member 14 or TNFRSF14) is suppressedby BMP-4 in certain glioblastoma cells. Glioblastoma is subclassified into
several subtypes. We have found that HVEM is highly expressed in themesenchymal subtype of human glioblastoma cells and tissues. Silencing of
HVEM expression resulted in reduced proliferation and neurosphere
formation of glioblastoma cells in cell culture and decreased tumorigenic
ability and prolonged survival of mice after intracranial injection of theglioblastoma cells. We have generated anti-human HVEM-VHH (variabledomain of heavy chain of heavy chain antibody). Our recent studies on thefunctions of HVEM on GICs, including pre-clinical studies, will be discussed.
Tissue clearing technology is a new technique that enables comprehensive
analysis of the process of cancer progression and metastasis as well as
visualization of blood and lymphatic vessels in adult mice. We applied "clear,
unobstructed brain/body imaging cocktails and computational analysis(CUBIC)" based cancer analysis to various mouse models and analyzed theprogression of cancer metastasis at spatio-temporal single cell resolution Our
experimental approach to analyze these data by topological data analysis
and non-homogeneous Poisson process model will also be shown.
http://beta-lab.umin.ac.jp; https://www.ims.riken.jp/laboratories/From a dynamical point of view, a tipping point presents perhaps the
single most significant threat to an ecological system as it can lead toabrupt species extinction on a massive scale.
Climate changes leading to parameter drifts can drive various ecologicalsystems towards a tipping point. Uncovering natural and biologicalmechanisms to mitigate or delay a tipping point is of considerable interest.
Focusing on the tipping-point dynamics in ecological mutualistic networksof pollinators and plants constructed from empirical data, I will examine
the phenomena of noise-induced collapse and noise-induced recovery
aiming at understanding the interplay between transients and stochasticity
in high dimensional ecological networks. I will discuss control strategies
that delay the extinction and advance the recovery by controlling the decayrate of pollinators, having implications to managing high-dimensional
ecological systems. I will unveil a natural mechanism by which theoccurrence of tipping points can be impacted by multiplexity that broadly
describes the diversity of the species abundances, the complexity of theinterspecific relationships, and the topology of linkages in ecologicalnetworks. For a double-layer system of pollinators and plants, coupling
between the network layers occurs when there is dispersal of pollinatorspecies. Multiplexity emerges as the dispersing species establish theirpresence in the destination layer and have a simultaneous presence inboth. The new mutualistic links induced by the dispersing species with theresidence species have fundamental benefits to the well-being of theecosystem in delaying the tipping point and facilitating species recovery.

15:30 - 16:05

Tipping Point Impacted
by Multiplexity in Mutualistic
Ecological Networks

Celso Grebogi
University of Aberdeen
https://www.abdn.ac.uk/people/grebogi
https://scholar.google.co.uk/citations?user=sdayej0AAAAJ&hl=en

16:05 - 16:40

Synchronization of multiple oscillators,
the role of coupling and geometry
Synchronization of oscillators may appear in various ways, ofwhich full synchronization of coupled identically oscillators is best
known and dates back to the earliest work of Christiaan Huygens in
the 17th century. However, also other types of synchronization havebeen observed, for example, anti-phase synchronization for two
coupled oscillators, or wave-like synchronization of multiple
oscillators has also been reported, but even other types ofcoordination are possible. The objective of this presentation is toseek underlying principles to understand the possible
synchronization mechanisms. Both some analysis tools as well as
experimental results will be discussed.

Henk Nijmeijer

Eindhoven University of Technology

16:40 - 17:15

Forcing of teleconnections
among tipping elementsin the climate system
Jürgen Kurths
Potsdam Institute for Climate Impact Researchhttps://www.pik-potsdam.de/members/kurths/homepage
Tipping elements are components of the Earth system that may shift abruptly
and irreversibly from one state to another at specific thresholds. An important
aspect is how different tipping points are interrelated and how the corresponding
teleconnections are changing due to external forcing. Here, we propose a climate
network approach to analyse the global impacts of a prominent tipping element,
the Amazon Rainforest Area (ARA). We find that the ARA exhibits strong
CoffeeBreakcorrelations with regions such as the Tibetan Plateau (TP) andthe West
Antarctic
ice sheet. Models show that the teleconnection propagation path between the ARA
and the TP is rather robust under climate change. We further uncover thatvarious climate extremes are synchronized between the ARA and the TP. Then weanalyze how different ENSO regimes influence this specific teleconnection andfind that there is indeed a substantial dependence on the ENSO state. Hence, thisinteraction of ARA and TP can only be understood as a non-autonomous system.
The implications of this basic feature will be discussed in detail.

Poster Presentations
2024.MARCH 15TH

1

2
3
4
5
6
7
8
9
10
11

12

13

Causal Control of Phenotype and State Transition
Chengming Zhang, Zexin Chen, Deyu Cai, Kazuyuki Aihara,
and Luonan Chen

Early Warning Signal for Random Dynamical
Systems with Bounded NoiseWei Hao Tey, Guillermo Olicón Méndez, Kazuyuki Aihara

Causal inference Based on Perturbed
Embedding Entropy
Yang Li, Jifan Shi, Kazuyuki Aihara

Effect of Time Series Length on Estimation of
Mouse Pupillometry Dynamics' Characteristics
Nina Sviridova, Pietro Artoni, Michela Fagiolini,
Takao K. Hensch, and Kazuyuki Aihara

Identification of atrial fibrillation in ECG
using machine learning methods
K. Sakai, T. Saji, K. Takeda, H. Kitajima, M. Ishizawa,T. Minamino

LSTM-based anomaly detection of pulse waves
for identifying atrial fibrillation
Kentaro Takeda, Hiroyuki Kitajima, Makoto Ishizawa,and Tetsuo Mimamino

Approximating nonlinear dynamics on networks
by sentinel nodes
Neil G. MacLaren, Baruch Barzel, Naoki Masuda

Dynamical Network Biomarker Theory
for Hierarchical Networks
Hampei Sasahara, Yuto Saito, Jun-ichi Imura

Theory of restabilisation of high-dimensional
data-driven network systems in very early treatment
Xun Shen, Hampei Sasahara, Jun-ichi Imura, Makito Oku,
Kazuyuki Aihara

A modified EKF approach to modeling GRNs via short
gene expression time series
Zhenhui Xu and Jun-ichi Imura

Data Analysis to Investigate the Process of EMT
in NMuMG Mouse Mammary Epithelial cells
A.S.M. Bakibillah, H. Sasahara, X. Zhenhui, J. Imura

Decrease in natural frequency as an overlooked
early warning signal
Makito Oku

Discrimination of Multiple Myeloma and Precancerous
State MGUS by Raman Microscopy
Akinori Taketani, Shota Koshiyama, Jun Tahara, Keiichi Koizumi,
Yusuke Oshima, Takayuki Haruki, Shota Yonezawa,
Kohei Kunimoto, Daiki Kirigaya, Akinori Wada, Tsutomu Sato,
Shigeru Saito

14

15
16
17

Identification of the Mebyo genes for metabolic syndrome
using the model organisms
Kazutaka Akagi, Yingjie Jin, Makito Oku, Jun-ichi Imura,
Keiichi Koizumi, Shigeru Saito

Development of a Quantitative Prognostic Approachfor Acute Liver Failure
Raiki Yoshimura

Quantitative evaluation of mucosal antibodies' rolein inhibiting infectious SARS-CoV-2 shedding
Takara Nishiyama, Sho Miyamoto, Park Hyeongki, Shingo Iwami

Detection of the changes in the depression state
under the COVID-19 pandemic
by using the energy landscape analysis (ELA)
Daiki Tatematsu, Naotoshi Nakamura, Shinsuke Koike, Shingo Iwami

18
19
20
21
22
23
24

25

26

Identifying distinct neuroanatomical subtypes
of Major depression disorder and Schizophrenia
Yinghan Zhu, Norihide Maikusa, Kiyoto Kasai, Shinsuke Koike

Multi-site large sample MRI dataset
for adolescent development and psychiatric disorders
Shinsuke Koike

On the uncertainty principle and geometrization
for spike coding models
Xun Li, Lang Cao, Kazuyuki Aihara

Computation harvesting from nature dynamics
for predicting wind speed and direction
Takumi Aita, Hiroyasu Ando, Yuichi Katori

Temporal information processing
using Mahalanobis distance of reservoir states
Hiroto Tamura, Kantaro Fujiwara, Kazuyuki Aihara,
and Gouhei Tanaka

Revisiting memory capacity of a reservoir computer
Swarnendu Mandal

Evolving multi-reservoir network
for understanding emotions
Anubhav and Kantaro Fujiwara

Data-driven modeling of time-varying GRN
for early disease prediction
Yosuke Inoue, Masaki Inoue

Introduction to the Data Analysis Function Integrated
with GakuNin RDM
Ikki Fujiwara, Yusuke Komiyama, Kazutsuna Yamaji

International Symposiumon Nonlinear Science and Medicine

Poster Presentations
Causal Control of Phenotype and State Transition
Chengming Zhang, Zexin Chen, Deyu Cai, Kazuyuki Aihara, and Luonan Chen
Understanding and purposefully manipulating cell states or phenotype transitions remains a persistent
challenge in the field of biology. In this study, we introduce CauFinder, an innovative interpretive
framework that employs neural networks to emulate the hierarchical control structure inherent in biologicalsystems. The primary objective is to identify a subset of features that exert causal influences during state or
phenotype transitions. By executing the "do" operation and optimizing causal information flow metrics, we
effectively isolate causal factors within the latent space, enabling the generative model to provide concise,precise, and causally informed explanations. Furthermore, after identifying causal features using SHAP
feature selection, we employ optimization techniques to manipulate these features, thereby facilitatingstate transitions. Through various simulated and real-world application scenarios, we have demonstrated theeffectiveness and applicability of our approach.

Early Warning Signal for Random Dynamical Systems
with Bounded NoiseWei Hao Tey, Guillermo Olicón Méndez, Kazuyuki Aihara
Critical transitions in a dynamical system have been a topic of discussion in different disciplines. The
understanding of its mechanisms and potential early warning signals are important for predictions andplannings of the sudden change. Traditional early warning signal is based on the concept of critical slowing
down where variance of the data increases as the system approaches critical transition. However, if thenoise magnitude is large, there exists some examples where monitoring variance is not sufficient for
detecting critical transition in data. We introduce an additive bounded noise model, where the random terms
in a random dynamical system are additive and bounded by an epsilon ball. In this framework, we illustratethat a potential early warning signal is by approximating derivative of the extremal map using the data nearto the boundary of the sample data. This is achieved by utilizing the asymptotic equation of the stationary
distribution of the random dynamical system and fitting them with the tail of histogram of the sample data.

Causal inference Based on Perturbed Embedding Entropy
Yang Li, Jifan Shi, Kazuyuki Aihara
Inference of causal relationships has a great potential in the analysis of various network systems. Comparedwith traditional methods such as Granger causality and transfer entropy, the methods based on time-delayed
embedding of dynamical systems have been attracting increasing attention. Here we propose an indicator for
Numerical investigation on simulated data (from chaotic neural networks) and real-world data (Japan
Covid19 dataset) demonstrate the effectiveness and usefulness of this method.

International Symposiumon Nonlinear Science and Medicine

Effect of Time Series Length on Estimation of Mouse
Pupillometry Dynamics' Characteristics
Nina Sviridova, Pietro Artoni, Michela Fagiolini, Takao K. Hensch, and Kazuyuki Aihara
Mouse models' spontaneous pupil size fluctuations are noninvasive data used in applied studies aiming at theearly detection of neurodevelopmental spectrum disorders. While pupillometry data are highly valuable andused in various studies, its complex dynamics properties are still to be thoroughly investigated. Various
studies use nonlinear time series analysis methods to investigate the dynamical characteristics of complex
biomedical signals, such as pupillometry. While this analysis toolkit proved useful in unveiling dynamical
features, it imposes various requirements on the experimental signals, such as signal length, sampling rate,
etc. Collecting sufficiently long experimental data from animals might be challenging, and as a result, inmany cases, signals recorded in the experiment might be of limited length. This study aims to investigate theeffect of pupillometry signal length on the estimation of its dynamical characteristics and to find the minimal
length of the time series that ensures the reliability of its results.

Identification of atrial fibrillation in ECG using machinelearning methods
K. Sakai, T. Saji, K. Takeda, H. Kitajima, M. Ishizawa, T. Minamino
Atrial fibrillation is the most commonly encountered clinical arrhythmia, which is caused by a disturbance ofthe electrical signals flowing in the atria. Atrial fibrillation is not immediately dangerous, but it is associatedwith an increased risk of myocardial infarction, heart failure, stroke, dementia, chronic kidney disease.
Therefore, early detection of atrial fibrillation is important. In this study, machine learning methods(Convolutional neural networks and Vision Transformer) were used to detect atrial fibrillation in theelectrocardiogram (ECG). We tried three types of input image data: the ECG waveforms, wavelet-transformed
images, recurrence plots of the waveforms. Stratified k-fold cross-validation was used for training andvalidation, and the average of five times was taken. The best result was the case of recurrence plots:
accuracy of 97.12%, recall of 97.52%, and specificity of 96.70%. Now, we are trying to mini-batch
optimization for unbalanced data and building more accurate models using ensemble learning. In the future,
we plan to deal with the multiple classification problem, including arrhythmias other than atrial fibrillation.

LSTM-based anomaly detection of pulse waves
for identifying atrial fibrillation
Kentaro Takeda, Hiroyuki Kitajima, Makoto Ishizawa, and Tetsuo Mimamino
A detection method for atrial fibrillation (AF) in pulse waves obtained with a blood pressure monitor ispresented. In the presented method, long short-term memory (LSTM) is used for time series forecasting ofsinus rhythm (SR), and its forecast anomalies are detected as AF. The presented method achieved highaccuracy with both high specificity and sensitivity.

Approximating nonlinear dynamics on networks
by sentinel nodes
Neil G. MacLaren, Baruch Barzel, Naoki Masuda
Many systems, including disease progression and transmission, can undergo sudden, drastic changes. Thesechanges often involve many variables and can be difficult to predict. Several dimensionality reduction
methods for nonlinear dynamics on networks assume that nodes with the most connections are the mostimportant in approximating the state of the system. While these nodes may be causally important in a
dynamical process, they may also be outliers in terms of current system state, biasing the approximation.
Given a model of the system which we have solved numerically over a critical range of a control parameter,
we select a small number of sentinel nodes by combinatorial simulated annealing. The mean state of thesentinel nodes should approximate the unweighted mean state of all nodes. We find that our method
achieves reasonable accuracy across several networks and dynamical models and is robust to someuncertainty in model parameters. Over many independent runs, our algorithm avoids hub nodes---precisely
the nodes assigned the greatest importance by previous methods---and instead includes a mix of small andlarge degree nodes in the vicinity of the mean degree. Node sets with the lowest approximation error tend toinclude nodes whose neighbors also have moderate degree. If we assign weights to the sentinel nodes using
quadratic programming optimization during simulated annealing, we find that our algorithm behaves similarly
in terms of node selection as when we do not assign weights, and additionally tends to assign maximum
weight to nodes near the mean degree. We thus conclude that although hub nodes are important in thetemporal evolution of dynamics on networks, the dynamical state of a network may be best approximated byaverage nodes.

Dynamical Network Biomarker Theory
for Hierarchical Networks
Hampei Sasahara, Yuto Saito, Jun-ichi Imura
Dynamical Network Biomarkers (DNB) theory has been proposed as a method for detecting diseases at a very
early stage. The progression of a disease can be regarded as a bifurcation phenomenon of the underlying
dynamical system associated with the corresponding gene network. By identifying large fluctuations of thegene expression level occurring just before the bifurcation, we can detect the pre-disease stage without
identifying the mathematical model of the dynamical system. However, the existing DNB theory mainly
focuses on a single gene network representing averaged dynamics of multiple cells. In this study, we consider
extending DNB theory to a cell-to-cell network composed of health and disease cells. It is numerically shownthat the pre-disease stage appears when the number of disease cells increases.

Theory of restabilisation of high-dimensional data-driven
network systems in very early treatment
Xun Shen, Hampei Sasahara, Jun-ichi Imura, Makito Oku, Kazuyuki AiharaInternational Symposium

The Dynamical Network Biomarkers (DNBs) theory has been proposed to detect
early-warning
signals
on Nonlinear
ScienceandofMedicine
critical transitions in gene regulatory networks only with High-Dimension Low-Sample-Size (HDLSS) data of
the system state. Towards giving a theoretical foundation for early medical treatment, this paper proposes adata-driven approach for the re-stabilization of gene regulatory networks based on HDLSS data. In the
proposed re-stabilization method, only the diagonal elements of the system matrix need to be adjusted.
Namely, only the self-feedback loops of mRNA expression for genes are intervened in, which reduces thecomplexity of the early medical treatment based on gene regulation and makes it practical to be
implemented. The proposed re-stabilization method is generalized to the systems with either saddle-nodebifurcation or Hopf bifurcation. Numerical simulations have been implemented to validate the effectivenessof the proposed method.

A modified EKF approach to modeling GRNs
via short gene expression time series
Zhenhui Xu and Jun-ichi ImuraIn this study, a modified extended Kalman filter (EKF) method is developed to model the gene regulatory
network from small size gene data. After specifying the model structure, the modified EKF method is applied
to identify parameters of gene expression levels. The proposed algorithm consists of a double-loop iteration.
The inner loop is the same as the traditional EFK iteration, while the outer loop allows the measurement
data to be reused, which greatly reduces the number of observation data requirement. It is shown that themodified EKF algorithm can identify a large number of parameters by using a small amount of observations. A
gene expression data set is employed to demonstrate the effectiveness of the algorithm.

Data Analysis to Investigate the Process of EMT
in NMuMG Mouse Mammary Epithelial cells
A.S.M. Bakibillah, H. Sasahara, X. Zhenhui, J. Imura
Epithelial-Mesenchymal Transition (EMT) is a process by which epithelial cells lose their cell polarity andcell–cell adhesion, and gain migratory and invasive properties to become mesenchymal stem cells. EMT
occurs in embryonic development, wound healing, organ fibrosis, and initiation of metastasis in cancer
analysis (PCA), differential expression gene (DEG) analysis, and dynamic network biomarker (DNB) analysis on
the data. From the analysis, we obtain some important genes that could be the possible biomarkers of EMT in
the pre-disease stage.

Decrease in natural frequency as an overlooked early
warning signal
Makito Oku

International Symposium

Several early warning signals such as an increase in fluctuations have been proposed to predict imminent
critical transitions in various complex systems in ecology, climatology, life sciences,and so Scienceon. Many andearlyMedicine
on Nonlinear
warning signals are also theoretically proven to emerge near a saddle-node or Hopf bifurcation under certain
conditions. However, early warning signals that emerge once but disappear again very close to thebifurcation point seem to be overlooked. One example is a decrease in natural frequency. This phenomenon
occurs when a pair of complex conjugate eigenvalues get closer to each other. After they collide, they
become a pair of real eigenvalues. After that, one of them moves toward the origin, resulting in asaddle-node bifurcation. Results of numerical simulations indicate that this phenomenon easily occurs in a
linear dynamical system when a non-diagonal element of the system matrix changes continuously. Thisphenomenon may extend the concept of critical slowing down and could be used for detecting pre-diseasestates when vital data from high-frequency measurements are available.

Discrimination of Multiple Myeloma and Precancerous
State MGUS by Raman Microscopy
Akinori Taketani, Shota Koshiyama, Jun Tahara, Keiichi Koizumi, Yusuke Oshima,
Takayuki Haruki, Shota Yonezawa, Kohei Kunimoto, Daiki Kirigaya, Akinori Wada,
Tsutomu Sato, Shigeru Saito
Multiple myeloma (MM) is a malignant disease in which plasma cells become "cancerous" and grow abnormallyin the bone marrow, producing large amounts of useless antibody M protein, which causes abnormalities
throughout the body. MM has a precancerous state Monoclonal Gammopathy of Undetermined Significance(MGUS) before becoming tumors. The MGUS state is characterized by abnormal proteins in the serum, but insmall amounts and without organ damage, and the patient is followed without treatment. In the current
situation, MM is differentiated based on bone marrow cell staining to determine whether they are in an MGUS
or MM when the ratio of normal cells reaches 10%, and the need for anticancer drug treatment isdetermined. Therefore, there is a need for the development of a more accurate differentiation method. We
were developing a new discrimination method for MM and MGUS using Raman microscopy. Raman
spectroscopy is a non-destructive method for obtaining molecular information from cells. This study aimed to
identify and clarify differences in MM and MGUS plasma cells' structures and conditions by measuring
non-destructively with Raman microscopy. In this study, we measured Raman spectra of 35 MM, 25 MGUS and
38 normal patient samples. The results of partial least squares (PLS), a form of multivariate analysis,suggested that MM, MGUS, and normal samples differ in lipid and DNA composition.

Identification of the Mebyo genes for metabolic syndrome
using the model organisms
Kazutaka Akagi, Yingjie Jin, Makito Oku, Jun-ichi Imura, Keiichi Koizumi, Shigeru Saito
Contemporary healthcare predominantly focuses on therapeutic interventions initiated post-disease onset,
often requiring prolonged treatment periods and substantial medical expenses. Consequently, there is apressing need to accurately identify and intervene in the pre-disease stage, known as "Mebyo", to achieveultra-early prevention of illnesses. However, the scientific definition of "Mebyo" remains elusive,necessitating the development of methodologies and experimental verification to precisely describe thisconcept. In our study, we aimed to elucidate the pre-disease state in the onset of metabolic syndrome using
a spontaneous model of metabolic syndrome in mice (TSOD mice). We performed microarray analysis using
the adipose tissue of TSOD mice at the stage of pre-disease onset and applied Dynamical Network Biomarkers(DNB) theory to analyze the data. Successfully detecting fluctuations in gene expression with high correlationindex, we defined this point as the "Mebyo" stage in our mouse model. Furthermore, utilizing DNB theory, wenarrowed down the Mebyo genes (DNB genes) to a set of 18 genes predicted to have a high efficacy ingenetic interventions using control theory. Intriguingly, these genes did not include those previously
reported in association with metabolic disorders. To elucidate the metabolic roles of these genes, we took
advantage of the genetically manipulable fruit fly, Drosophila melanogaster, and conducted RNAi screening.
Knocking down the Drosophila orthologs of the DNB genes specifically in the adipose tissue, we assessed themetabolic impact through starvation resistance tests. We found two genes, which shows large impact on
metabolic functions. This presentation discusses these findings, future prospective, and the utility of DNB
theory in combination with Drosophila research model.

Development of a Quantitative Prognostic Approachfor Acute Liver Failure
Raiki YoshimuraInternational Symposiumon Nonlinear Science and Medicine

As observed in recent cases of severe COVID-19, detecting biomarkers that indicate early signs in acutediseases with dramatic changes in condition is an urgent challenge for predicting future pathologicalchanges. In this study, using clinical data from a disease called acute liver failure, where the survival rate
significantly decreases after onset, we developed an approach to predict changes in condition and prognosis.
Specifically, by analyzing patient blood test data using supervised machine learning, we identified factorsthat could serve as biomarkers for predicting changes in condition. Developing a mathematical model
describing the time series changes of identified factors allowed us to extract parameters explaining the timechanges for each patient. Furthermore, by constructing a machine learning model to predict patientparameters from admission data, we successfully developed an early prognosis prediction model. The
approach developed in this study is expected to be utilized in clinical medicine as a tool for early response
decision-making.

Quantitative evaluation of mucosal antibodies' rolein inhibiting infectious SARS-CoV-2 shedding
Takara Nishiyama, Sho Miyamoto, Park Hyeongki, Shingo Iwami
As the COVID-19 pandemic is coming to an end, there is a crucial need to develop technologies for managing
future pandemics, particularly those caused by emerging Variants of Concern (VOCs) and other respiratory
viruses. Despite the effectiveness of mRNA vaccines in blocking SARS-CoV-2 entry, their role in preventing
viral shedding from infected individuals is less clear. Mucosal immunity, essential in respiratory infections,might be key in reducing viral shedding and human transmission. However, understanding of mucosal
immunity's role in this context is limited. This study aims to quantify the impact of mucosal antibodies on
viral shedding suppression using a mathematical model reconstructing SARS-CoV-2 dynamics. Nasopharyngeal
swab data from individuals infected with SARS-CoV-2 omicron variants were analyzed. These swabs werecollected in a cohort study by the Japanese Ministry of Health, Labor and Welfare. Utilizing mathematicalmodels, we extracted quantitative individual-level data on mucosal antibody and viral dynamics. We found asignificant correlation between virus shedding dynamics and mucosal antibody responses post-infection.
Notably, secretory IgA (S-IgA) was instrumental in suppressing infectious virus shedding, highlighting theimportance of its rapid production for control. These findings suggest a potential strategy to control the
spread of respiratory viral infections, emphasizing the role of mucosal immunity, especially S-IgA, inmanaging viral shedding and transmission.

Detection of the changes in the depression state
under the COVID-19 pandemic by using the energy
landscape analysis (ELA)
Daiki Tatematsu, Naotoshi Nakamura, Shinsuke Koike, Shingo Iwami
The COVID-19 pandemic changed our lifestyles. It is expected that the changes in depression state also
occurred because of these changes. In this study, we used the questionnaire that asked high school students
in Tokyo about their depression states before, during, and after the period of the COVID-19 pandemic andanalyzed the group characteristics of changes in depression state as a landscape using energy landscapeanalysis (ELA), a method of multidimensional (time-series) data analysis. As a result, we were able toquantitatively confirm the depression state changes as the energy barriers. We were also able to detect how
the energy barrier changed in the COVID-19 pandemic and found that the energy barrier was relatively highin the COVID-19 pandemic and that it was difficult to change to a depression state. These results are
consistent with those of previous studies and suggest that ELA can be used for the psychiatric questionnaires
analysis.

Identifying distinct neuroanatomical subtypes of Major
depression disorder and Schizophrenia
Yinghan Zhu, Norihide Maikusa, Kiyoto Kasai, Shinsuke Koike
Prior neuroimaging studies primarily focused on investigating neuroanatomical abnormalities in mental
disorders such as major depression disorder (MDD) or schizophrenia (SCZ) use binary case-control
approach.Brain structural abnormalities such as cortical thinning and subcortical volume increasing werereported in MDD or SCZ compare to heathy controls (HCs). We investigated neuroanatomical subtypes in
multi-disorder, multi-site multi-protocol , using semi-supervised machine learning methods heterogeneity
through discriminative analysis (HYDRA) to discover variations of anatomical alterations within disorders.
on Nonlinear
Scienceand Medicine
T1-weighted structural brain MRI scans from patients with MDD (n=544), SCZ (n=176), and 1,819
HCs, wereobtained from 9 sites. Regional cortical thickness (CT), surface area (SA) and subcortical volumes (SV) served
as features in building the classifier differentiating disorder subtypes from HCs. We used traveling subjects
dataset to harmonize measures of CT, SA and SV data. Next, we fitted general addictive models to only the
HC data (n=1148) to estimate non-linear effects of age and sex for every structural feature; then we applied
the fitted GAMs to obtain non-linear age- and sex-corrected features. Individuals those who were younger
than 65 years old (MDD, n=445; SCZ, n=158) and HC (n=599) data served as training, test, and external
validation datasets. Two distinct neuroanatomical subtypes were found for MDD and SCZ. In differentiating
Subtype 1 or subtype 2 from HCs, the accuracy of each classifier was higher than those built for disorderwithout subtypes (MDD subtype1 vs HCs 79%; MDD subtype2 vs HCs 76%; MDD vs HCs 67%; SCZ subtype1 vsHCs 83%; SCZ subtype2 vs HCs 86%; SCZ vs HCs 75%). Indicating that by identifying subtypes of MDD and SCZ
improved the performance in differentiating those from HCs. Discovering of neuroanatomical subtypes of
MDD and SCZ may be helpful to identify their prognosis and underlying neuropathological processes.

International SymposiumMulti-site large sample MRI dataset for adolescentdevelopment and psychiatric disorders
Shinsuke Koike
It is well-known about the heterogeneity of psychiatric disorders and each spectrum can include two or more
pathophysiological backgrounds, a part of which in turn can be shared with some of other spectra. Recent
large-scale MRI studies have found the MRI-based subtypes in schizophrenia (Okada et al. Mol Psychiatry2023; Chand et al. Brain 2020). Therefore, when seeing brain characteristics into the subtyping level, we
can look at a new insight for multiple psychiatric disorders and adolescent development. When using
multi-site dataset, we need to pay attention to a measurement bias from MRI machines and protocols, since
the measurement bias is greater effect size compared to disease-related difference from healthy controls(Maikusa et al. Hum Brain Mapp 2021; Koike et al. NeuroImage Clin 2021; Kurokawa et al. NeuroImage 2021;
Yamashita et al. PLOS Biol 2020). Recent Japanese MRI projects have targeted for cross-disorder dataset forneuropsychiatric disorders and high-quality harmonization techniques using a traveling subject method(Koike et al. NeuroImage Clin 2021). As of October 2023, we harmonized more than 3,000 samples of people
with schizophrenia, major depressive disorders, bipolar disorders, and autism spectrum disorders as well as
clinical high risk for psychosis and general adolescents. Here, I intend to introduce about the ongoingcollaborative studies for neuropsychiatric disorders and harmonization techniques.

On the uncertainty principle and geometrization
for spike coding models
Xun Li, Lang Cao, Kazuyuki Aihara
Spiking computation is a prominent branch of artificial intelligence that embraces the advantages of low
energy consumption due to its massively parallel, event-driven nature. Theoretical understanding of how
information can be coded and represented by sequences of spikes is a first step for the design of spike-based
computing systems towards their fully exploited potential. In studying models of spike coding from aphysicist's perspective, the familiar uncertainty principle can be drawn on the energy-time domain, giving alower bound on the product of the variances of spike counts and spike timing in the codes required bypreservation of information. Thus, we term a minimum uncertainty scheme the coherent coding. It can beobtained from the action principle by minimizing the Lagrangian, the difference between the kinetic andpotential energies of the coding, which correspond, respectively, to the complexity of the spike codes fromtheir temporal and rate components. Motivated by this, we further postulate a general geometrization
framework for spike coding models, where information is assumed to be represented by curved paths in ahigh-dimensional manifold, and spike codes are obtained by discretizing the paths with equally spaced points
and then projecting into a one-dimensional time axis. Finally, we illustrate our model with the application ofword embedding.

Computation harvesting from nature dynamics
for predicting wind speed and direction
Takumi Aita, Hiroyasu Ando, Yuichi Katori
Complex dynamics arise in natural phenomena due to nonlinear interactions among their components,
offering a potential computational resource. In our sensor-rich society, it has become feasible to capture andleverage these natural dynamics. For example, surveillance cameras indirectly record plant movement,
serving as sensor outputs that reflect the influence of the wind on the plant. In this context, based on theprinciples of physical reservoir computing, we introduce a method for estimating wind speed and direction
using data from naturally occurring sensors in movies. We validate its efficacy through experimentation.

Temporal information processing using Mahalanobis
distance of reservoir states
Hiroto Tamura, Kantaro Fujiwara, Kazuyuki Aihara, and Gouhei Tanaka
Reservoir computing is increasingly recognized as an efficient machine learning method for time-series
processing, notable for its low learning cost and reduced training data requirements. Departing from theconventional linear regression readout used in standard reservoir computing, this study introduces a"distance-based readout" method. This method is especially effective for tasks requiring differentiation, suchas anomaly detection and classification. First, we introduce the Mahalanobis Distance of Reservoir States(MD-RS) for online time-series anomaly detection. In the MD-RS method, the reservoir's responses to normal
time series are fitted to a multivariate Gaussian distribution. During testing, the Mahalanobis distance fromthis fitted distribution is used as a measure of anomaly. Notably, MD-RS outperforms both existing
reservoir-based and the most advanced techniques without reservoirs in this domain. Further, we extend
MD-RS for time series classification. In this method, the reservoir's responses to each class are fitted to a
multivariate Gaussian distribution, and classification is based on the distance (measured as Kullback–Leibler
divergence) between the distribution of each class and that of the response during testing. Our proposed
method demonstrates superior performance compared to readouts using linear regression or support vector
machines.

Revisiting memory capacity of a reservoir computer
Swarnendu Mandal
Reservoir computing (RC) has been proven to be overwhelmingly successful in handling a wide range ofcomplex tasks in nonlinear science. The root of its exceptional capability to process temporal information
lies in the dynamical nature of its reservoir layer. The nonlinearity and memory capacity the dynamicalsystem help to capture the temporal input information. However, being a intrinsic character of thereservoir, it not always easy to enhance the memory capacity. It has been long-standing problem in the
traditional RC architecture. Here, I discuss an alternate architecture for RC where nonlinearity and memory
capability is separated out from a single layer. Information of the previous reservoir states are manuallyinserted in in the current states to enhance memory capacity. The result leads to better performance intasks requiring higher memory.

Evolving multi-reservoir network
for understanding emotions
Anubhav and Kantaro Fujiwara
This work explores a metaheuristic search approach for developing an efficient multi-reservoir network
mapping physiological signals, namely electroencephalogram (EEG) signals to emotions. Deep Neural
Networks are trending for classification tasks such as emotion recognition. Although they obtain highprediction accuracy, DNNs require an extensive dataset and enormous computational resources for training.
Therefore, we examine a computationally inexpensive reservoir computing network as an alternative to
DNNs. We develop a multi-reservoir computing network using insights from on
works
such as Yang
and Mazon,
Nonlinear
Scienceand Medicine
Anubhav and Fujiwara, where a multi-reservoir network is noted to imitate brain information processing
closely. A Genetic Algorithm based search approach is used to simulate different configurations ofmulti-reservoir networks and evolve an efficient configuration for the emotion recognition task. In each
network configuration, a single reservoir represents a brain area, and connections between these reservoirs
indicate communication between different brain areas. In implementation, a chromosome in the searchalgorithm contains information about the number of reservoirs, EEG channels input to a reservoir and theconnections between these reservoirs. Using suitable mutation and crossover operators, we aim tounderstand interactions of different brain areas and their contributions towards emotions. For experiments,
we use a benchmark publicly available DEAP dataset consisting of participants' EEG signals while watchingmusic videos. In future, this work can be extended to understand interactions in brain areas leading to aparticular emotion or to develop an efficient multi-reservoir network for different applications.

International SymposiumData-driven modeling of time-varying GRN
for early disease prediction
Yosuke Inoue, Masaki Inoue
In this study, we focus on data-driven modeling of the state equation for a time-varying Gene Regulatory
Network (GRN). Assuming that the transition from a healthy to a diseased state is modeled by thedestabilization of equilibrium states and bifurcation as a dynamical system, we aim to predict the timing ofbifurcation only from data of the healthy state. Additionally, we introduce a demonstration device to visuallycomprehend the three states: healthy, diseased, and the intermediate pre-disease state during the poster
presentation.

Introduction to the Data Analysis Function Integrated
with GakuNin RDM
Ikki Fujiwara, Yusuke Komiyama, Kazutsuna Yamaji
The importance of research software is receiving greater attention in terms of research reproducibility with
FAIR data. With the aim to enable non-expert users to easily conduct reproducible data-driven research, wehave developed our JupyberHub service that is integrated with our research data management system,
namely GakuNin RDM. Technically, we have extended GakuNin RDM so as to enable users to build andmanage their own Jupyter containers on our Kubernetes cluster or on the JupyterHubs that have built by theusers themselves. From within the Jupyter environment the user can read/write his/her files managed by
GakuNin RDM via FUSE. This service is provided to the members of universities and institutions participatingin GakuNin, the Academic Access Management Federation in Japan. Unlike Google Colab or Code Ocean, we
aim to provide a quasi-persistent environment based on the trust provided by GakuNin. Beyond the
JupyterHub functionality, we aim to support GakuNin RDM users to easily utilize HPCs (e.g. supercomputers)
via workflow execution services or batch job schedulers.

International Symposiumon Nonlinear Science and Medicine