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Organizers: Takashi Sumikama, Damien Hall, Holger Flechsig
NanoLSI, Kanazawa University

Support:International Union for Pure and Applied Biophysics (IUPAB)
Nano Life Science Institute (WPI-NanoLSI), Kanazawa University

Venue:

Main Conference Room, NanoLSI Building 4th floor, Kanazawa University,
Kakuma Campus & Online via Zoom

Administration Contact:nanolsi-office@adm.kanazawa-u.ac.jp

Cover page image credit: Takashi Sumikama, J Phys Chem Lett (2022)

2

List of Content
1. About NanoLSI (page 4)
2. About IUPAB (page 5)
3. Special Issue in Biophysical Reviews (page 6)
4. Map of Kakuma Campus (page 7)
5. Helpful Information (page 7)
6. Wi-Fi Access (page 7)
7. Catering and Workshop Banquet (page 8)
8. Workshop Schedule (pages 9-10)
9. List of Presentation Abstracts (pages 11-34)
10. IUPAB Fellowship Awards (pages 35)
11. Final Remarks (page 36)3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+,&-$%123+4

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Special Issue in Biophysical Reviews
Biophysical Reviews is the official journal of IUPAB and publishes
critical and timely reviews from key figures in the field of biophysics.
Interested authors are encouraged to discuss the possibility ofcontributing a review with the Editor-in-Chief. Biophysical Reviews
covers the entire field of biophysics, generally defined as thescience of describing and defining biological phenomenon using theconcepts and the techniques of physics.
This includes but is not limited by such areas as bioinformatics,
biophysical methods and instrumentation, medical biophysics,
biosystems, cell biophysics and organisation, macromolecules:
dynamics, structures and interactions, and membrane biophysics,
channels and transportation.

Collated and edited by the three conference organizers (Takashi Sumikama, Damien Hall and Holger
Flechsig) a Special Issue of Biophysical Reviews dedicated to the Computational Biophysics of Atomic ForceMicroscopy is scheduled to be published in December of 2023. Based around the contributions from theworkshop (but also accepting submissions from outside interested parties) those wishing to participate areasked to contact
Takashi Sumikama
Damien Hall
Holger Flechsig

sumi@staff.kanazawa-u.ac.jp
hall.damien@staff.kanazawa-u.ac.jp
flechsig@staff.kanazawa-u.ac.jp

Journal Website

https://www.springer.com/journal/12551

6

Access to the NanoLSI building at Kakuma Campus
For information how to access Kanazawa university (Kakuma campus) from Kanazawa station pleasecheck the website https://nanolsi.kanazawa-u.ac.jp/en/access/
The Nano Life Science Institute is the building framed in red in the map below.
The conference venue is located on the 4th floor.

Helpful Information
TAXI companies
Ishikawa Kintetsu Taxi
Fuji Taxi
Kamome Taxi

phone 076-221-3265
phone 076-237-1020
phone 076-231-1128

NanoLSI Administration
Office hours:
8:30am-5pm
Contact:
nanolsi-office@adm.kanazawa-u.ac.jp

Wi-Fi Access at NanoLSI

Wi-Fi access is available via the Eduroam service. We will provide individual login credentials with a noteof instructions on how to connect at the workshop reception desk. In case of trouble we are happy to
help.

7

(/$*B")>%/)=%C&B@#8&5%4/)D-*$:%;"+'),'&<"'=!" 9-> 5'<<%;)%".%.:)%!"#$%"& 8)-."/8"#.%<$+".)=%$#%.:)%>#$ (<$$8%'#%.:)%'"%$(")*+,-.&,.*"&/*
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T"#Q/).%B"8.'+'B".'$#%())%'- =C@GAA'B",LWORKSHOP SCHEDULE
DAY 1 MONDAY – September 19th
Session theme: Introduction to AFM experiment, Theory and Simulation
Session chair: Takashi Sumikama
1:00 pm
3:00 pm

3:30 pm
KEYNOTE
4:30 pm
4:45 pm
5:15 pm
6:30 –

Poster/Paper display setup (continues for 3 days)
Introduction to Workshop by NanoLSI Director
Takeshi Fukuma (Director of NanoLSI, Kanazawa University)
Introduction to research activities at Nano Life Science Institute(WPI-NanoLSI)First Keynote Lecture (Toshio Ando)(Chairperson Introduction)
Toshio Ando (Special Professor, Kanazawa University)
High-speed atomic force microscopy in biology
BreakShoji Takada (Kyoto University)
Coarse-grained biomolecular simulations for geometric modelingof HS-AFM data
George Heath (Leeds University)
Extracting high-resolution information through image analysis
Mixer Event at Yabutan Restaurant
IUPAB Travel Fellowship Presentation

DAY 2 TUESDAY – September 20th
Session theme: Experimental Overview
Session chair: Damien Hall
9:00 am
9:30 am
10:00 am
10:30 am

Takayuki Uchihashi (Nagoya University)
Local mechanical indentation combined with high-speed AFM
Ayhan Yurtsever (NanoLSI, Kanazawa University)
3D-AFM investigations of solvation structures on polysaccharide-based
nanocrystals and self-assembled peptide nanostructures
Shingo Fukuda (NanoLSI, Kanazawa University)
Control systems for high-speed AFM and its advances toward
ultra-high-speed AFM
Noriyuki Kodera (NanoLSI, Kanazawa University)
Historical description of some of the famous model systems studied in
AFM developments and current limitations in high-speed bioAFM

11:15 – 12:15 pm Lunch
Session theme: Computation and Theory Overview (1)Session chair: Holger Flechsig
12:30 pm
1:00 pm

Tamiki Komatsuzaki (WPI ICReDD & Hokkaido University)
Single Molecule Biophysics: What do Single Molecules Experience its
Energy Landscape?
Damien Hall (NanoLSI, Kanazawa University)
High speed atomic force microscopy of dynamic samples

91:30 pm

Takashi Sumikama (NanoLSI, Kanazawa University)
Computation of three-dimensional atomic force microscopy images

2:00 pm

Holger Flechsig (NanoLSI, Kanazawa University)
Molecular modeling of protein machinery and simulation atomic
force microscopy for interpretation of limited-resolution Bio-AFM imaging
Florence Tama (WPI ITbM & Nagoya University)
Integrative modeling to characterize the structure and dynamics of
biomolecules

2:30 pm

Session theme: Computation and Theory Overview (2)Session chair: Takashi Sumikama
3:15 pm
3:45 pm
4:15 pm
4:45 pm
5:15 pm
5:45 pm

6:00 pm
KEYNOTE

Adam Foster (Aalto University)
Machine learning in scanning probe microscopy
Niko Oinonen (Aalto University)
Machine learning models and data representations for atomic force
microscopy images
Fabio Priante (Aalto University)
Neural network potentials for molecular dynamics
Orlando Silveira (Aalto University)
Simulations of high-resolution Scanning Probe Microscopy
Filippo Canova (Nanolayers Inc.)
Theoretical methods for non-contact atomic force microscopy
Break
Second Keynote Lecture (Toshio Yanagida)(Introduction by Chairperson Takehiko Ichikawa)Toshio Yanagida (Osaka University)
Biomolecular motors convert information into motion: the secret of livingorganisms' extraordinary energy savings

7:10 pm
Chartered bus to banquet venue
7:30 – 10:00 pm Workshop Banquet at Shogyotei Restaurant
DAY 3 Wednesday – September 21st
Session theme: Computation and Theory Overview (3)Session chair: Damien Hall, Holger Flechsig
10:00 am
10:30 am
11:00 am
11:30 am
12:00 – 12:30

Thomas Huber (Australian National University)
Protein structures are predictable. How?
Satoru Okuda (NanoLSI, Kanazawa University)
Computational simulations of cell and tissue dynamics
Kenichi Umeda (NanoLSI, Kanazawa University)
Controlling and data acquisition methods for high-speed AFM &development of next-generation high-speed AFM
Romain Amyot (NanoLSI, Kanazawa University)
BioAFMviewer: a tutorial for simulated AFM scanning
Closing Remarks
Holger Flechsig – Scientific Summary
Takashi Sumikama – Conference Summary (Activities and Awards)
Damien Hall – Special Issue in Biophysical Reviews

10

Presentation Abstracts

11

Introduction to Research Activities at Nano Life Science Institute (WPI-NanoLSI)
Takeshi Fukuma
WPI Nano Life Science Institute, Kanazawa University
fukuma_atsign_staff.kanazawa-u.ac.jp

Nano Life Science Institute was established in 2017 as one of the centers for World PremierInternational Research Center Initiative (WPI) program. At NanoLSI, we aim to develop novel
nanoprobe technologies for visualizing unseen nanoscale biological phenomena andelucidating underlying mechanisms. Major SPM techniques that we have been developing
include high-speed atomic force microscopy (HS-AFM) (Ando and Kodera groups), frequency
modulation AFM (FM-AFM) and three-dimensional AFM (3D-AFM (Fukuma group), andhigh-speed and functional scanning conductance microscopy (Korchev and Ando groups).
With these techniques, we have been investigating various functions of proteins, DNAs andcells and the related diseases. In the first part of this talk, I would like to provide an overview
of our research activities at NanoLSI.
In the second half of the talk, I would like to present our recent work on the development andapplications of nanoendoscopy AFM [1]. Atomic force microscopy (AFM) is the only
technique that allows label-free imaging of nanoscale biomolecular dynamics, playing a crucialrole in solving biological questions that cannot be addressed by other major bioimaging tools(fluorescence or electron microscopy). However, such imaging is possible only for systems
either extracted from cells or reconstructed on solid substrates. Thus, nanodynamics insideliving cells largely remain inaccessible with the current nanoimaging techniques. Here, we
overcome this limitation by nanoendoscopy-AFM, where a needle-like nanoprobe is inserted
into a living cell, presenting actin fiber three-dimensional (3D) maps, and 2D nanodynamics
of the membrane inner scaffold, resulting in undetectable changes in cell viability. Unlike
previous AFM methods, the nanoprobe directly accesses the target intracellular components,
exploiting all the AFM capabilities, such as high-resolution imaging, nanomechanical mapping,and molecular recognition. These features should greatly expand the range of intracellular
structures observable in living cells.
1. Penedo, M.; Miyazawa, K.; Okano, N.; Furusho, H.; Ichikawa, T.; Alam Mohammad, S.; Miyata, K.;
Nakamura, C.; Fukuma, T., Visualizing intracellular nanostructures of living cells by nanoendoscopyAFM. Science Advances 2021, 7 (52), eabj4990.

12

KEYNOTE
High-speed atomic force microscopy in biology
Toshio Ando
Distinguished Professor, Kanazawa University
tando_atsign_staff.kanazawa-u.ac.jp

High-speed (HS-) AFM has now established a unique position in experimental biophysics, as the sole tool
able to present real motion pictures of the biological nanoworld [1]. Its history can be traced back to theearly 1990th when I and Paul Hansma started developing HS-AFM independently [2]. Around that time,
pioneers were exploring potentials of newly emerged AFM in biological studies; they attempted not only
high-resolution topography imaging but also recognition and localization of specific molecules as well as
force measurements to estimate the strength of intra- and intermolecular bonds at the single-molecule level
and the elasticity of biological surfaces. In contrast to these pioneering works, the development of practicallyuseful HS-AFM went through with hitches and therefore progressed slowly. Among several issues to solve,
the most difficult issue was no damages of fragile molecules during their fast imaging. By great fortune, thisissue was solved by a flash of a new idea, leading to the establishment of HS-AFM in 2008 [3].
HS-AFM studies of biological samples have been published more than 300 to this day. Among these, thestudy of walking myosin V [4] is a milestone in the history of bio-AFM research. This study is a paragon
illustrating the power of HS-AFM. The HS-AFM study of the rotary motor F1-ATPase brought a revelation[5]. Interestingly, the initial aim of this study was simple; visualizing conformational changes of this protein
during the ATPase reaction. However, the protruding central shaft hampered clear imaging of the stator ring
due to the tip size effect. Then, the stator ring alone was imaged, leading to a surprising discovery. As seenin the program of this workshop, we have now been experiencing interactions between HS-AFM
experiments and theoretical analyses (molecular dynamics simulations and reconstruction of dynamic
pseudo-AFM images). This scientific endeavor and synergy will unleash technical restrictions and burgeon
into more profound understanding of the functional mechanism of proteins.
1. Ando T. (2022) High-speed atomic force microscopy in biology. 319 pages, Springer Berlin (Verlag),ISBN 978-3-662-64783-7.
2. Ando T, Kodera N, Takai E, Maruyama D, Saito K, Toda A. (2001) A high-speed atomic force
microscope for studying biological macromolecules. Proc Natl Acad Sci U S A 98, 2468‒12472.
3. Ando T, Uchihashi T, Fukuma T. (2008) High-speed atomic force microscopy for nano-visualization of
dynamic biomolecular processes. Prog Surf Sci 83, 337‒437.
4. Kodera N, Yamamoto D, Ishikawa R, Ando T. (2010) Video imaging of walking myosin V by highspeed atomic force microscopy. Nature 468, 72‒76.
5. Uchihashi T, Iino R, Ando T, Noji H. (2011) High-speed atomic force microscopy reveals rotary
catalysis of rotorless F1-ATPase. Science 333, 755‒758.

13

Coarse-grained biomolecular simulations for geometric modelingof HS-AFM data
Shoji Takada
Professor, Department of Biophysics, Graduate School of Science,
Kyoto University
takada_atsign_biophys.kyoto-u.ac.jp

High-speed AFM is a unique and prominent method to observe structural dynamics of biomolecules at single
molecular level at neat physiological condition. However, since AFM measures the surface envelop at which
the probe tip interacts with target molecules, the structure underneath the surface need to be interpreted.
To interpret static AFM images, several types of geometric modeling have been utilized as concise
means. When we have prior structure information on the target biomolecules, we can fit them into the AFMmeasured surface envelop, assuming the shape of the probe tip. We can apply either the rigid-body fitting toa fairly rigid molecule (1), or the flexible fitting to more flexible targets (2,3). For the case that we do nothave prior structure information of the target, we can employ the erosion/dilation processing to convert
between the AFM-measured surface envelop and the target molecular surface envelop, assuming the shapeof the tip (4). Furthermore, we can apply the de novo blind tip reconstruction method to infer the shape ofthe tip solely from the measured AFM data.
Since HS-AFM movie provides molecular motions, such motions are also expected to beinterpreted. Coarse-grained biomolecular simulations enable us to simulate large-scale molecular motions
near the time-scale of the HS-AFM measurement, thus providing complementary information. We
developed two types of Bayesian inference approaches to assimilate the HS-AFM movie data with coarsegrained molecular simulations; the particle filter method (5) and the hidden Markov modeling. Thesemethods give general frameworks to integrate the HS-AFM movie with computer simulations.
(1) T Niina, Y Matsunaga, S Takada (2021) PLoS Computational Biology 17 (7), e1009215(2) T Niina, S Fuchigami, S Takada (2020) Journal of Chemical Theory and Computation 16 1349-1358.
(3) H Koide, N Kodera, S Bisht, S Takada, T Terakawa (2021) PLoS computational biology 17 (7),e1009265S(4) J.S. Villarrubia (1997) J. Res. Nat. Inst. Stand. Tech. 102 425.
(5) Fuchigami, T Niina, S Takada (2020) Journal of Chemical Theory and Computation 16, 6609-6619

14

Extracting High-Resolution Information Through Image Analysis
George Heath
University Academic Fellow, University of Leeds
g.r.heath_atsign_leeds.ac.uk

Understanding the structure and dynamics of biomolecules at the single molecule level is vital to advancing
our knowledge of medicine and diseases, yet there are few techniques which can directly capture these
movements at the relevant spatiotemporal scales. High-Speed AFM allows dynamic molecules to be studied
at high resolution and in ambient conditions to produce large volumes of data at rates >100,000Hz for many
seconds up to hours. Each pixel/data point contains rich data about structure and dynamics of the surface
being studied and therefore data processing and analysis needs to be designed for high throughput whilst
also taking careful consideration to the noise, drift, varying tip convolution and molecule dynamics that may
be present.
Here, I will give short history of digital image analysis and then describe how image data is stored
in general, how pixelated image data is created from AFM experiments and different ways it can beinterrogated. I will then build on this to show methods to improvement AFM spatial resolution using
Localization AFM, an image analysis method which takes concepts from super resolution microscopy. By
applying localization algorithms to AFM data, Localization AFM significantly increases resolution beyond
the limits set by the tip radius. This technique enables the calculation of high-resolution maps from either
images of many molecules or many images of a single molecule acquired over time, facilitating singlemolecule structural analysis. Localization AFM is a post-acquisition image reconstruction method that canbe applied to any biomolecular AFM dataset.
References
1. Heath G R, Kots E., Robertson J L, Lansky S, Khelashvili G, Weinstein H & Scheuring S. Localization
Atomic Force Microscopy. Nature, 594, 385–390, (2021).

15

Local mechanical indentation combined with high-speed AFM
Takayuki Uchihashi
Department of Physics, Nagoya University/ExCELLS
uchihast@d.phys.nagoya-u.ac.jp

Atomic force microscopy (AFM) can not only visualize the surface structure of a sample but also manipulate
the local structure of the sample, such as indentation or fracture by applying a controlled force from theprobe to a local area of the sample. In the first part, an overview is given on the methods and analysis ofnanoindentation and mechanical property measurements by conventional AFM. In the second part, I will
present recent developments and applications of a high-speed AFM combined with local indentation andforce curve measurement.
HS-AFM is widely used for the observation of single protein dynamics and visualizing surface structuresof biological samples. So far, HS-AFM is mainly used for topographical imaging. However, recent
developments have allowed us to apply a force from the probe to a local position of the sample during HSAFM imaging and continuously monitor subsequent changes in the sample. This mode, called the interactivemode, was used to apply a force to a localized position of a biological sample to disrupt its structure, examine
the detailed structure of broken fragments [1] and assess the fragility of the biological sample [2]. It has also
been applied to artificial supramolecular nanofibers, successfully monitoring fiber digestion by the AFM
probe and subsequent self-repair processes and manipulating local structures [3]. Furthermore, recent in-line
measurement of force curves during HS-AFM imaging has enabled quantitatively assessment of mechanicalproperties by acquiring force curves, in addition to applying indentations at local positions on the sample.
The in-line-force-curve mode was applied to the local mechanical indentation of microtubules to observe
the formation of tubulin dimer defects in microtubules, and the binding energy between tubulins was
experimentally evaluated from the hysteresis of the force curve during the indentation process on the
deforming microtubules [4].
Taking this in-line force mapping a step further, a force mapping mode was developed that simultaneously
records force information and topography. Depending on the size of the force map, a single image frame
may be recorded in as low as 3 s. To demonstrate this high-speed force mapping, we apply it to two types ofbacteria: B. subtilis and E. coli. For the former, we will present the dynamic breakdown of cell walls uponlysozyme addition. It was found that the cell wall stiffness is reduced up to a factor of 10 within typically 10
min. In the case of E. coli, we observed the growth of a single bacterium and its subsequent division not onlywith HS-AFM force mapping but also with optical microscopy. It was possible to capture the formation of
the septum, which was significantly stiffer than the rest of the cell.
These examples show that HS-AFM force mapping can be used to study dynamic phenomena also from aviewpoint of mechanical properties.
1. T. Haruyama, T. Uchihashi, Y. Yamada, N. Kodera, T. Ando and H.Konno (2018) Negatively charged
lipids are essential for functional and structural switch of human 2-Cys peroxiredoxin II", J. Mol. Biol.
430, 602-610.
2. M. Owa, et al (2019) Inner lumen proteins stabilize doublet microtubules in cilia and flagella", Nat.
Commu. 10, 1143.
3. T. Fukui, T. Uchihashi ,N. Sasaki, H. Watanabe, M. Takeuchi, K. Sugiyasu (2018) Direct observationand manipulation of supramolecular polymerization by using high-speed atomic force microscopy,
Angew. Chem. Int. Ed. 57, 15465-15470.
4. C. Gasner and T. Uchihashi (2019) Microtubule self-healing and defect creation investigated by in-line
force measurements during high-speed atomic force microscopy, Nanoscale 11, 125-135.

3D-AFM investigations of solvation structures on polysaccharide-based nanocrystals
and self-assembled peptide nanostructures
Ayhan YurtseverAssistant Professor, WPI-NanoLSI
yurtsever_atsign_staff.kanazawa-u.ac.jp

The atomic force microscope (AFM) is mainly known for its high-resolution imaging capabilities, but it alsooffers the possibility of measuring the interaction forces and energies between the tip and surfaces, thereby
providing more insights into the underlying mechanisms of many biological and physical processes down to
the single atom/molecule level. In this seminar, I will talk about the AFM force measurements, quantificationof forces, and 3D force mapping methods. The results of three-dimensional AFM (3D-AFM) measurementson the characterization of 3D local hydration structures at the chitin/cellulose nanocrystals-water interfaces
will be presented and discussed. Also, I will talk about the 3D-AFM investigations of solvation structures
on biomolecules (peptides) and biomolecular assemblies on graphite/HOPG.
1. Yurtsever et al. 2021. Structural and mechanical characteristics of exosomes from osteosarcoma cells
explored by 3D-atomic force microscopy. Nanoscale 13, 6661.
2. Yurtsever et al. 2022. Probing the structural details of chitin nanocrystal-water interfaces by threedimensional atomic force microscopy. Small Methods 2200320.

17

Control systems for high-speed AFM and its advances toward
ultra-high-speed AFM
Shingo Fukuda
Assistant professor, WPI-NanoLSI Kanazawa University, Japan
shingofukuda_atsign_staff.kanazawa-u.ac.jp

High-speed atomic force microscopy (HS-AFM) has been established by tremendous efforts, which
introduces a new dimension to structural biology, enabling the direct observation of biological systems
ranging from purified proteins to live-cells [1,2]. In the HS-AFM operating in the amplitude modulation(tapping) mode, a cantilever is excited at its near-resonance frequency. The resulting oscillating tip isintermittently contacting the surface, which consequently damps the oscillation amplitude. The surface
topography is reconstructed by maintaining the cantilever amplitude constant (the set point value) by the
feedback regulation for the Z-axis while scanning XY plane. In the first half of this talk, we describe basic
control systems used for HS-AFM; how the cantilever and the sample stage are regulated to accurately
acquire the topographic features of sample. We also introduce key developments including the dynamic PID
controller [3] and the inverse-based feedforward damping method [4], to realize the gentle and precise
scanning. In the second half of this talk, we will discuss our latest progress on further speeding up of HSAFM to meet the increasing demands for the great expansion of observable dynamic molecular processes.
We implemented the only trace imaging mode [5] and the dynamic PID control method with lower threshold.
These developments can lower the feedback control error and thereby enhance the imaging rate. Further we
implemented fast-axis (X-axis) scanning methods that allow the linear motion without any undesired
vibrations even at rates of 100 frames/seconds. By the advent of these technologies, the maximum imaging
rate of HS-AFM is now pushed beyond video rate. Such an ultra-high-speed imaging capability can resolve
the molecular dynamics at unpreceded resolutions and deepen our understanding on the biologicalphenomena.
References[1] T. Ando, High-speed atomic force microscopy in biology, Splinger Berlin, Heidelberg (2022)[2] T. Ando, T. Uchihashi, T. Fukuma, Prog. Surf. Sci. 83: 337-437 (2008)[3] N. Kodera, M. Sakashita, T. Ando, Rev. Sci. Instrum.,77, 083704 (2006)[4] Yang Li, John Bechhoefer, Rev. Sci. Instrum.. 78, 013702 (2007)[5] S. Fukuda, T. Ando, Rev. Sci. Instrum,. 92, 033705 (2021)

18

Historical description of some of the famous model systems studied in AFM
Developments and current limitations in high-speed bioAFM
Noriyuki Kodera
Professor, WPI-NanoLSI, Kanazawa University
nkodera_atsign_staff.kanazawa-u.ac.jp

High-speed atomic force microscopy (HS-AFM) allowed us to directly visualize the dynamic behaviors
of biological molecules in action at nanometer spatial and sub-second temporal resolution. The power of
HS-AFM has been demonstrated by an increasing number of imaging studies on biological molecules [1,2].
In the first half of the presentation, I will introduce some of the famous model systems that have been
captured by HS-AFM so far, and then present the recent results obtained in our research group (factor poolformation around the ribosomal P-stalk [3], determination of the molecular shape of an actin-binding protein
CAP1 [4] and interactions between CAP1 and actin filaments, and long-range dsDNA degradations by
CRISPR-Cas3 [5]).
In recent years, we have been carrying out research and development to improve the temporal resolutionof HS-AFM system, in order to apply HS-AFM to a wider range of biological phenomena. In the second
half of the presentation, developments and current limitations in high-speed bioAFM will be explained.
References
1. T. Ando, High-speed atomic force microscopy, Curr. Opin. Chem. Biol. 51, 105-122 (2019).
2. G. R. Heath & S. Scheuring, Advances in high-speed atomic force microscopy (HS-AFM) reveal
dynamics of transmembrane channels and transporters, Curr. Opin. Struct. Biol. 57, 93 (2019).
3. H. Imai, T. Uchiumi & N. Kodera, Direct visualization of translational GTPase factor pool formed
around the archaeal ribosomal P-stalk by high-speed AFM, PNAS 117, 32386-32394 (2020).
4. N. Kodera, H. Abe, PDN. Nguyen, S. Ono, Native cyclase-associated protein and actin from Xenopus
laevis oocytes form a unique 4:4 complex with a tripartite structure, J. Biol. Chem. 296, 100649 (2021).
5. K. Yoshimi, K. Takeshita, N. Kodera, S. Shibumura, Y. Yamauchi, M. Omatsu, K. Umeda, Y. Kunihiro,
M. Yamamoto & T. Mashimo, Dynamic mechanisms of CRISPR interference by Escherichia coli
CRISPR-Cas3, Nat. Commun. (Accepted).

19

Single Molecule Biophysics: What do Single Molecules Experience its Energy
Landscape?
Tamiki Komatsuzaki
Research Center of Mathematics for Social Creativity,
Research Institute for Electronic Science, Hokkaido University, Japan
Graduate School of Chemical Sciences and Engineering Materials
Chemistry and Engineering Course, Hokkaido University, Japan
Institute for Chemical Reaction Design and Discovery (WPI-ICReDD),
Hokkaido University, JapanSANKEN, Osaka University, Japan
Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Japan
tamiki_atsign_es.hokudai.ac.jp
How can one learn from single molecule time series concerning the underlying conformation network orenergy landscape? What a single molecule actually experiences is not so well recognized, even while thistheme can be one of the most striking consequences people can unveil from single molecule biology. In part
1 of my talk I will briefly talk about the mathematical background to derive the underlying energy landscapesolely from single molecule time series. The core in constructing the reaction network from single-molecule
time-series is the application of rate-distortion theory in information theory, which naturally considers theeffects of measurement noise and sampling error, in combination with the quantification of the FRET
efficiency-dependent photobleaching behavior. In part 2 of my talk I will talk about hierarchical features ofreaction network and energy landscape for the folding/unfolding behavior of adenylate kinase, which arerespectively elucidated in terms of single-molecule fluorescence resonance energy transfer (smFRET)
measurements. Energy landscapes are constructed as a function of observation time scale, revealing multiple
partially folded conformations at small time scales that are situated in a superbasin. Because thephotobleaching time scale is dependent on the conformational state of the protein, possible nonequilibriumfeatures are discussed, and a statistical test for violation of the detailed balance condition is developed basedon the state sequences.
References:
1) James Nicholas Taylor, Menahem Pirchi, Gilad Haran, Tamiki Komatsuzaki, “Deciphering hierarchicalfeatures in the energy landscape of adenylate kinase folding/unfolding” The Journal of Chemical
Physics 148, 123325 (2018)2) Meysam Tavakoli, J. Nicholas Taylor, Chun-Biu Li, Tamiki Komatsuzaki, Steve Pressé
Advances in Chemical Physics: "Single Molecule Data Analysis: An Introduction" Edited by Stuart A.
Rice, Aaron R. Dinner (Wiley)
3) T. Komatsuzaki, S. Takahashi, M. Kawakami, Haw Yang, Robert Silbey. (Edition)Single Molecule
Biophysics: Experiment and Theory. Advances in Chemical Physics 146, (2012)
John-Wiley& Sons, Inc. ISBN: 978-1-1-180-5780-3.
4) T. Komatsuzaki, R. S. Berry, D.M. Leitner. (Edition)"Advancing Theory for Kinetics and Dynamics
of Complex, Many-Dimensional Systems: Clusters and Proteins" Advances in Chemical Physics 145,
(2011) John-Wiley & Sons, Inc. ISBN: 978-0-470-64371-6 (2011).

20

High Speed Atomic Force Microscopy of Dynamic Samples
Damien Hall
Assistant Professor, Kanazawa University
hall.damien_atsign_staff.kanazawa-u.ac.jp

In the analysis of static samples High Speed Atomic Force Microscopy (HS-AFM) is capable of providing
nanometer resolution (or better) in both the lateral (xy) and vertical (z) planes (1). However, biologicalsamples are often not static, with significant movement of the sample potentially occurring over the timescale of the HS-AFM measurement. In such cases two problems can arise that relate to (i.) assignment of theindividual components observed within the image, and (ii.) image distortion caused by incongruities between
redistribution and measurement timescales (2). In the first part of my lecture, I will provide a basic overview
of coarse-grained approaches capable of describing the dynamics of biological processes such as diffusionin the cellular membrane, motor protein migration along protein fibers, and protein binding and unbinding
to adsorbed DNA. In the second part, I will describe methods for overcoming both the assignment problem
and for assessing the degree of distortion imparted by differences in the rate of sample redistribution and
AFM measurement. To help in the performance of these methods I introduce and describe two executablesoftware tools HS-AFM UGOKU (User Graphical Optimization tool developed at Kanazawa University)(2) and HS-AFM MIREBA (Methodology for Inferring REsolution in Biological Applications) (3).

1. Ando T. (2022) High-speed atomic force microscopy in biology. 319 pages, Springer Berlin (Verlag),ISBN 978-3-662-64783-7.
2. Hall, D. and Foster A.S. (2022) Practical considerations for feature assignment in high-speed AFM oflive cell membranes. Biophysics and Physicobiology. e190016, 1-21
3. Hall, D. and Foster A.S. (2022) Quantification of dynamic cell membrane features by high-speed atomic
force microscopy. (Manuscript in preparation).

21

Computation of three-dimensional atomic force
microscopy images
Takashi Sumikama
PRESTO researcher, Japan Science and Technology Agency
Assistant professor, Kanazawa University
sumi_atsign_staff.kanazawa-u.ac.jp
Three-dimensional atomic force microscope (3D-AFM) is a technology to resolve molecular
distributions by scanning a probe in the x, y, and z-directions1, which was originally developed to view
solvent molecules at the solid-liquid interfaces. Recently, it was extended to the imaging of intracellularbiomolecules with thin and long probes, and it was shown to visualize nanostructures of biopolymers eveninside cells2.
This talk will begin with an introduction to the calculation of 3D-AFM images of solvent molecules atthe solid-liquid interface3,4. The free energy or potential mean force (PMF) profiles on the solid surface arecomputed using the umbrella sampling or perturbation method, and the derivative of PMF yields the socalled force-distance curves at a given x and y position. Merging force-distance curves at all x, y positions
gives a 3D-AFM image. Note that this methodology, computing the free energy, assumes that solvent
molecules move much faster than the penetrating probe and distribute almost all possible arrangements thatthey can take.
On the other hand, the motions of some biopolymers, especially large complexes, are not as fast as thevelocity of penetrating probe, and thus they cannot take all possible configurations during probe penetration.
That is, biomolecules do not reach equilibrium during probe scanning. Accordingly, the above approach tocompute force-distance curves is not applicable to biopolymers.
In this talk, I will also present our recently developed method for computing 3D-AFM images of
biopolymers in such non-equilibrium processes5. The biopolymers and probes are modeled by the beadspring polymer model. The force-distance curves are computed by using the Jarzynski equality, which relates
external work done while scanning to the free energy difference. Some biopolymer structures are actuallyresolved in the computed 3D-AFM images. Finally, I will talk about some comparisons with experiments tovalidate this computational method.
[1] T. Fukuma & R. Garcia, ACS Nano 12, 11785 (2018).
[2] M. Penedo et al., Sci. Adv. 7, eabj4990 (2021).
[3] M. Watkins & A. L. Shluger, Phys. Rev. Lett. 105, 196101 (2010).
[4] B. Reischl, M. Watkins, & A. S. Foster, J Chem. Theory Comput. 9, 600 (2013).
[5] T. Sumikama et al., J Phys. Chem. Lett. 13, 5365 (2022).

22

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Integrative modeling to characterize the structure and dynamics of biomolecules
Florence Tama
Professor, Department of Physics, Graduate School of Science &Institute of Transformative Bio-Molecules, Nagoya University,
Japan
Team Leader, RIKEN Center for Computational Science, Japan
florence.tama_atsign_nagoya-u.jp

The exploration of molecular motions by simulation approaches has provided significant insights into
biomolecules’ structure, function, and dynamics. Because biomolecular structures are complexes anddynamic, interpretation of simulation results can be challenging. Therefore, to characterize essential features
regarding the dynamics of these biomolecules, methods such as principal component analysis or normal
mode analysis (NMA) have been employed to reduce the problem's dimensionality. These methods often
focus on low-frequency (energy) modes, as those are known to describe quite well experimentally observed
functional motions. As experimental data does not always provide high-resolution information, hybrid andintegrative modeling methods that combine computational molecular mechanics simulations withexperimental data have been developed to describe the structure and dynamics of biomolecules. One
example is the integration of NMA with experimental data such as 2D and 3D cryo-electron microscopy orsmall-angle scattering data. Such integrative modeling methods have also been extended to describe
conformational states observed by AFM experiments. For example, starting from a known 3D structure, one
can generate a coarse-grained representation of the biomolecule, and the shape of this representation can be
optimized, via Monte Carlo sampling, for a given AFM image. As a result, a low-resolution 3D model canbe built from the 2D AFM image. Therefore, such optimizations can be performed for a set of AFM images
to describe in terms of 3D models the conformational variety embedded within the 2D AFM data. Such an
approach was used to annotate the different conformational states of the ClpB chaperone observed
experimentally.
References
F. Tama, O. Miyashita, and CL. Brooks, III. Flexible multi-scale fitting of atomic structures into lowresolution electron density maps with elastic network normal mode analysis. J. Mol. Biol. 2004 337:
985-999
Q Jin, CO Sorzano, JM de la Rosa-Trevín, JR Bilbao-Castro, R. Núñez-Ramírez, O. Llorca , F. Tama, S.
Jonić. Iterative elastic 3D-to-2D alignment method using normal modes for studying structuraldynamics of large macromolecular complexes. Structure 2014 22:496-506
R Vuillemot, O Miyashita, F Tama, I Rouiller and S Jonic. NMMD: Efficient cryo-EM flexible fitting
based on simultaneous Normal Mode and Molecular Dynamics atomic displacements. J. Mol. Biol. 2022
B. Dasgupta, O. Miyashita & F. Tama. Reconstruction of Low-resolution Molecular Structures from
Simulated Atomic Force Microscopy Images. BBA – General Subjects 2020
B. Dasgupta, O. Miyashita, T. Uchihashi, and F. Tama. Reconstruction of three-dimensional
conformations of bacterial ClpB from high-speed atomic- force-microscopy images. Frontiers Mol.
BioSci. 2021

24

Machine Learning in Scanning Probe Microscopy
Adam Foster
Professor, Aalto University
adam.foster_atsign_aalto.fi

Machine learning methods are increasingly being applied to data challenges in Scanning Probe Microscopy(SPM). In particular, the success of deep learning in image recognition tasks has led to their application to
the analysis of SPM images, especially in the context of surface feature characterisation and techniques forautonomously-driven SPM [1]. In this work, we first outline some of the key concepts in applying machinelearning methodologies, before exploring the potential for using deep learning to aid in the interpretation ofhigh resolution SPM images [2]. In general, we find high success rates in predicting the atomic and chemical
structure of molecules, and the method can also be used for quantitative predictions of electrostatic properties[3]. This approach opens the door to apply high-resolution AFM to a large variety of systems for whichroutine atomic and chemical structural resolution on the level of individual objects/molecules would be amajor breakthrough. We also look at applications of similar approaches to the imaging of biomaterials and
AFM imaging in liquids [4]. More generally, we develop some of the ideas behind a future of active learning
measurements, introducing methods to automate tip preparation [5] and atomic manipulation.
[1] O.M. Gordon and P.J. Moriarty, Mach. Learn.: Sci. Technol. 1, 023001 (2020).
[2] B. Alldritt, P. Hapala, N. Oinonen, F. Urtev, O. Krejci, F. F. Canova, J. Kannala, F. Schulz, P.
Liljeroth, and A. S. Foster, Sci. Adv. 6 (2020) eaay6913[3] Niko Oinonen, Chen Xu, Benjamin Alldritt, Filippo Federici Canova, Fedor Urtev, Ondřej
Krejčí, Juho Kannala, Peter Liljeroth and Adam S. Foster, ACS Nano 16 (2022) 89[4] Yashasvi S. Ranawat, Ygor M. Jaques and Adam S. Foster, Nanoscale Adv. 3 (2021) 3447
[5] Benjamin Alldritt, Fedor Urtev, Niko Oinonen, Markus Aapro, Juho Kannala, Peter Liljerothand Adam S. Foster, Comp. Phys. Comm. 273 (2022) 108258

25

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*#9&':2!62#3:':NpB!UR(!J)6621':B!P-PPW#'Neural Network Potentials for Molecular Dynamics
Fabio Priante
PhD student, Aalto University
fabio.priante_atsign_aalto.fi

The first Molecular Dynamics (MD) simulations were carried out in the 1950s [1,2], following thedevelopment of early electronic computers. Since then, MD has become an essential investigative tool in awide range of scientific fields, from material science to biology. Designing a MD simulation requires the
choice of a potential, or force field (FF), which defines how the atoms in a system will interact with eachother. In principle, this can be done very accurately using ab-initio techniques [3], where interatomic forces
are calculated after solving the electronic structure of the frozen nuclei. However, this is feasible only forsmall systems (up to a few hundreds of atoms), and for limited timescales. To simulate more complex
scenarios, one has to resort to classical FFs, which trade-off accuracy for computational speed.
In the last decade, thanks to machine learning, many efforts have been made to overcome these limitations,in an attempt to reach close to ab-initio accuracy at a fraction of the computational cost. In particular, thedevelopment of neural network potentials (NNP) has paved the way for a new revolution in the field.
In this talk, I will provide an historical perspective on the methods used to construct and parametrize FFs for
MD. Particular attention will be given to the recent machine learning approaches, up to equivariant NNP [4],which constitute the current state of the art. I will discuss current and future applications of NNP, and, finally,
I will present the development of a NNP for water molecules on a Cu surface, taken from an ongoing research
effort.
[1] Fermi E., Pasta J., Ulam S., Los Alamos report LA-1940 (1955).
[2] Alder, B. J., Wainwright, T. E. Studies in Molecular Dynamics. I. General Method. J Chem Phys. 31(2): 459–466 (1959).
[3] Car, R., Parrinello, M. Unified Approach for Molecular Dynamics and Density-Functional Theory.
Phys Rev Lett. 55 (22): 2471–2474 (1985).
[4] Batzner, S., Musaelian, A., Sun, L. et al. E(3)-equivariant graph neural networks for data-efficient andaccurate interatomic potentials. Nat Commun 13, 2453 (2022).

27

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8P-+I=

Theoretical methods for non-contact atomic force microscopy
Filippo Federici Canova
CTO, Nanolayers Research Computing Ltd.
felix_atsign_nanolayers.com

Non-contact atomic force microscopes (ncAFM) are capable of imaging samples at extremely highresolution, enabling us to see the very atomic fabric of surfaces [1]. Unfortunately, these microscopes havequite little in common with optical microscopes we are all familiar with. The probe is usually an atomically
sharp tip, rather than our eyes, and the sampling mechanism relies on interatomic forces between the tip andthe sample, rather than light diffusing from it; the instrument itself is a very complex piece of machinery,with several electronic feedback controls acting on the probe dynamics. While it is often easy to understandthe patterns seen in ncAFM images qualitatively (mostly because the nature of the sample is already known
before the experiment begins), the correct interpretation of defects and subtle features in the measured signals
requires extensive theoretical work [2]. In general, ncAFM simulations require a model for the tip-sample
atomic interactions, and a model of the instrument response to those interactions [3], making this a multiscalemodelling effort.
Here we will give a broad overview of the theoretical methods involved in the interpretation of ncAFM
images, including classical and ab-initio techniques to calculate tip-sample interactions, and circuit-level
instrument simulators, and show how they were applied to solve research problems [4, 5].
[1] T. R. Albrecht, P. Grütter, D. Horne and D. Rugar: "Frequency modulation detection using high-Q
cantilevers for enhanced force microscope sensitivity",
Journal of Applied Physics 69 (2), 668 (1991)[2] F. Schulz, J. Ritala, O. Krejčí, A. P. Seitsonen, A. S. Foster and P. Liljeroth: “Elemental Identification
by Combining Atomic Force Microscopy and Kelvin Probe Force Microscopy”,
ACS Nano 12 (6), 5274 (2018)[3] J. Tracey, F. Federici Canova, O. Keisanen, D. Z. Gao, P. Spijker, B. Reischl, A. S. Foster: “Flexible
and modular virtual scanning probe microscope”,
Computer Physics Communications 196 429 (2015)[4] S. Kawai, F. Federici Canova, Th. Glatzel, A. S. Foster, and E. Meyer: “Atomic-scale dissipation
processes in dynamic force spectroscopy”,
Physical Review B 84 115415 (2011)[5] F. Federici Canova, S. Kawai, Ch. de Capitani, K. Kan’no, Th. Glatzel, B. Such, A. S. Foster and E.
Meyer, “Energy Loss Triggered by Atomic-Scale Lateral Force”,
Physical Review Letters 110 203203 (2013)

29

KEYNOTE
Biomolecular motors convert information into motion: the secret of living organisms'
extraordinary energy savings
Toshio Yanagida
Professor, Osaka University

Using optical microscopy and high-speed AFM, we have analyzed how myosin motors that work in muscles,
convert the chemical energy of ATP into mechanical energy. We found that the myosin motor generates
movement by selecting forward movement from random Brownian motion. That is, it shows that the
movement is caused by Brownian motion, and the chemical energy of ATP is not used to cause movement,
but to select forward movement from random Brownian motion (acquisition of information). That is, themyosin motor converts information into movement. This discussion has been made a lot in thought
experiments of Maxwell's demons, but it is surprising that it was discovered that the demons are actuallyenergized and worked in living things. This is a big discovery from an information point of view. That is, it
is suggested that the relationship of 1 bit (information) = 0.7 kBT (energy) actually holds for biomolecules.
Since 1 bit = 109 kBT is required in the electronic circuit of a computer, biomolecules are processing
information with energy saving by orders of magnitude. The cells work at approximately 10 picowatts. Even
with the latest ICs, this energy can only process 1 bit per second. On the other hand, the biomoleculealgorithm using Brownian motion can process 1 gigabit / sec and is reasonable. Recently, it has been reported
that many biomolecules work using Brownian motion, and our discovery has the essence of information
processing in living organisms.
1. Fujita K, Ohmachi M, Ikezaki K, Yanagida T, Iwaki M. 2019. Direct visualization of human myosin IIforce generation using DNA origami-based thick filaments. Commun. Biol. 2, 437.
2. Kitamura K, Tokunaga M, Iwane A, Yanagida T. 1999. A single myosin head moves along an actin
filament with regular steps of 5.3 nanometers. Nature 397, 129.
3. Funatsu T, Harada Y, Tokunaga M, Saito K, Yanagida T. 1995. Imaging of single fluorescent molecules
and individual ATP turnovers by single myosin molecules in aqueous solution. Nature 374, 555.

30

Protein structures are predictable. How?
Thomas Huber
Professor, Australian National University
Research School of Chemistryt.huber_atsign_anu.edu.auProteins are arguably the most important macromolecules in organisms. They are one of the most abundantclass of organic molecules in all life forms and have essential roles in a diverse range of cellular function.
The function of a protein is largely dictated by their chemical composition, the primary sequence of aminoacids, and the 3-dimensional structure the polypeptide chain adopts in a cellular environment. We therefore
typically assume that by knowing the 3D structure of a protein we are in a better position to understand(biological) function.
The premise of this talk is that protein structures are predictable and also computable by application of
fundamental principles. In my talk I will discuss the difference between predictability and computability,
explain some of the fundamental principles to compute protein structures efficiently and illustrate some more
popular approaches. I will further demonstrate how sparse experimental data, in example pseudocontact
shifts from NMR measurements, can be used to aid 3D structure computations, and conclude with potential
pitfalls one can encounter when over-interpreting experimental data from a protein system as rigid 3D
structure.
References
Leman, J.K. et al. Macromolecular modeling and design in Rosetta: recent methods and frameworks. Nat
Methods 17, 665–680. https://doi.org/10.1038/s41592-020-0848-2
Pan, B.-B. et al. 3D structure determination of a protein in living cells using paramagnetic NMR
spectroscopy. Chem.Comm. 52 10237-10240. https://doi.org/10.1039/c6cc05490k
Schmitz, C. et al. Protein structure determination from pseudocontact shifts using ROSETTA. J. Mol.
Biol. 416 668-677. https://doi.org/10.1016/j.jmb.2011.12.056

31

Computational simulations of cell and tissue dynamics
Satoru Okuda
Associate Professor, WPI-NanoLSI, Kanazawa University
satokuda_atsign_staff.kanazawa-u.ac.jp

In multicellular organisms, the multiscale dynamics of cells from the molecular to the organ level arecoordinated with each other, causing functional structures and behaviors as a system. To address theunderlying mechanisms, mechanics-based computational simulations have been the focus of efforts. Becauseof the large gap from the molecular to the organ scale, the targets of computational methods vary greatly
depending on the structure and spatio-temporal scale. In general, each method has an elementary structure
describing the cell structures, which determines the characteristic length scale of each method. For example,
molecular dynamics methods, which have been used for proteins, are based on each atom or each populationof atoms. Continuum membrane models, which have been used for organelles and cell membranes, are basedon a continuum element of cell membranes. Vertex models, which have been used for multicellular tissues,
are based on each cell. These methods are powerful tool to predict structures, dynamics, interactions, andproperties of each target. However, because of the variety of cellular structures and the very broad spatiotemporal scale of their dynamics, the phenomena that can be handled by existing methods remain very
limited. In particular, few methods can applied to mesoscopic-scale phenomena such as intracellular
structures such as organelles and tissues composed of cells and extracellular matrix. In this lecture, themechanics-based computational methods for such mesoscopic cell dynamics will be presented, along withthe author's research.
References
1. Martin Karplus, Petsko A Gregory, "Molecular dynamics simulations in biology." Nature 347.6294(1990): 631-639.
2. Masashi Tachikawa, Atsushi Mochizuki, Golgi apparatus self-organizes into the characteristic shape
via postmitotic reassembly dynamics. A Proceedings of the National Academy of Sciences of the UnitedStates of America 114(20) 5177-5182, 2017.
3. Satoru Okuda, Yasuhiro Inoue, Mototsugu Eiraku, Taiji Adachi, Yoshiki Sasai, "Vertex dynamics
simulations of viscosity-dependent deformation during tissue morphogenesis," Biomechanics andModeling in Mechanobiology 14(2), 413-425 (2015)32

Controlling and Data Acquisition Methods for High-Speed AFM &Development of Next-Generation High-Speed AFM
Kenichi Umeda
Assistant Professor at WPI NanoLSI, Kanazawa University
umeda.k_atsign_staff.kanazawa-u.ac.jp

In the first section, I will explain about the detail specification of controlling, data acquisition and analysis
method of high-speed atomic force microscopy (HS-AFM) while comparing the normal AFM instruments.
In most of the commercial AFM, digital signal processor (DSP) is used for controlling AD/DA boards while
our HS-AFM instrument does not used DSP to reduce the machine cost while increasing the scanningbandwidth. This is important for increasing the number of the instruments and facilitates the collaboration
researches. For HS-AFM, since a vast of frames is saved in the storage, an automation of image processing(e.g., constant color scaling with respect the substrate and polynomial flattening filter) is important.
In the second section, I will explain about recent progress in the development of high-speed AFM in termsof high-speed, quantitativeness, and functionality so that it can be applied to more diverse range ofbioresearches. For measuring the molecular distance quantitatively, the nonlinearity and memory effect ofthe piezo scanner causes 20~30% errors. I developed nonlinear feedforward system to compensate the nonideal characteristics. I also developed zero-latency amplitude detection method to increase the scanningbandwidth tremendously [1]. These components will be useful for further advancement of HS-AFM
researches.
References[1]
Kenichi Umeda, et al., Applied Physics Letters, 119, 181602-1–5 (2021).

33

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IUPAB Travel Fellowship Awards
IUPAB granted eight travel fellowship awards to attend the workshop.

Domestic Fellowship winners – 300 EUR each
Yui Kanaoka
Nagoya University, Japan
Jiaqi Wang
Hokkaido University, Japan
Keiju Suda
Kyushu University, Japan
Saahil Acharya
Okinawa Institute of Science and Technology (OIST), Japan
Dr. Yuya Hanazono
Tokyo Medical and Dental University, Japan

International Fellowship winners – 1000 EUR each
Joohyun Lee
Sungkyunkwan University, South Korea
Dr. Witchukorn Phuthong
Kasetsart University, Thailand
Dr. Nguyen Thanh Hoa
Hanoi University of Science & Technology, Vietnam

35

Final Remarks
We thank all participants for attending the workshop. We invite you to join our mailing list to receive
announcements for upcoming workshops. Please use the following registration form:
https://forms.gle/VMa7rfMoJtCFQ5Gq7
We are looking forward seeing you in future events!

36