organized and sponsored by -...

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Organized and Sponsored by

55th Anniversary of the Faculty of Science, Chiang Mai University

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The 23rd International Annual Symposium on Computational Science and Engineering (ANSCSE23)

WELCOME MESSAGE

Associate Professor Dr. Vudhichai Parasuk

President of Computational Science and Engineering

Association (CSEA), Thailand

Dear Friends and Colleagues,

It is a great pleasure and an honor to extend to you a warm invitation to attend the ANSCSE23, the 23rd International Annual Symposium on Computational Science and Engineering, to be held on June 27-29, 2019. This year the symposium is organized by Faculty of Science, Chiang Mai University, Computational Science and Engineering Association (CSEA) and National e-Science Infrastructure Consortium, National Nanotechnology Center (NANOTEC), and National Electronics and Computer Technology Center (NECTEC). ANSCSE23 has always been one of the greatest gatherings of computational scientists, computer science, and engineering researchers. After 23 years, we have seen many signs of progress and so many interesting researches being conducted in this area. In this digital age, rapid progress has been driven by artificial intelligence, big data, and much higher computing power enabled by new technology such as GPU, FPGA. Thus, the vital role that computational science plays in human social development becomes clearer and clearer every day.

One of the great spirits of ANSCSE is the live discussion among fellow international researchers. After a few days of intense discussion on our works, the organizer kindly arranges an excursion to the Doi Inthanon National Park, the highest mountain in Thailand. I am certain that everyone will enjoy the talk along with the beauty of Chiang Mai.

Finally, I look forward to meeting all of you. Thank you for sharing your thoughts and ideas in ANSCSE23.

Best Wishes,

Associate Professor Dr. Vudhichai Parasuk

Chulalongkorn University President of Computational Science and Engineering Association (Thailand)

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WELCOME MESSAGE

Professor Dr. Suthep Suantai

Department of Mathematics, Faculty of Science, Chiang Mai

University,

Thailand

Dear Colleagues,

On behalf of the organizing committee, I am honored and delighted to welcome you to the 23rd International Annual Symposium on Computational Science and Engineering (ANSCSE23). It is a great honor for Faculty of Science, Chiang Mai University to be the host of ANSCSE23 and this conference is one of many conferences to celebrate 55th year of Faculty of Science, Chiang Mai University. Our co-hosts are Computer Science and Engineering Association (CSEA), National e-Science Infrastructure Consortium, Materials Science Research Center (MRS), Chiang Mai University, National Nanotechnology Center (NANOTEC) and National Electronics and Computer Technology Center (NECTEC).

Over twenty-two years, ANSCSE has a long history of gathering researchers who are in the field of computational science and engineering to cross-fertilize ideas and to strengthen both local and international networks. The theme of this year is “Expand Your Mind”. Under this theme, ANSCSE23 covers not only various disciplines of computational science and engineering including fields of Biology, Chemistry, Physics, Fluid Dynamics, Solid Mechanics, High Performance Computing, Cloud Computing, and Computer Science and Engineering but also experimental studies particularly material sciences.

There are 2 plenary lectures, 3 special talks, 68 invited talks and about 69 oral and poster presentations. This year, the scientific programs are accompanied with the “Workshop on e-Science and HighPerformance Computing: eHPC2019” workshop. This conference aims to provide an exciting venue for scientists to present and exchange ideas, as well as to strengthen existing collaborations and developing new ones.

As a conference chair of ANSCSE23, I would like to express my sincere appreciation to the steering committee, the honorary chairs, the international advisory board, the scientific committee chair, the program chairs, the scientific committee, the reviewers, our sponsors and the organizing team. Last but not the least; recognition and thank should also go to the local organizing committee team who has really worked hard in organizing the technical programs and supporting social arrangements.

Finally, ANSCSE23 truly serves the venue for networking and knowledge sharing among the participants which is an outcome of the comprehensive presentations as well as high-level plenary and panel sessions. We hope you will take the utmost advantage of this event to start your future collaborations.

Sincerely yours,

Professor Dr. Suthep Suantai

Conference Chair Department of Mathematics, Faculty of Science, Chiang Mai University, Thailand

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Map of Chiang Mai University

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Floor Plan for ANSCSE23

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Committees

Steering Committee

• Asst. Prof. Putchong Uthayopas Kasetsart University and Acting President of CSEA, Thailand • Assoc. Prof. Vudhichai Parasuk Chulalongkorn University and President-Elect of CSEA,

Thailand • Prof. Supa Hannongbua Kasetsart University, Thailand • Assoc. Prof. Waraporn Parasuk Kasetsart University, Thailand • Dr. Piyawut Srichaikul National Electronics and Computer Technology Center

(NECTEC), NSTDA, Thailand Honorary Chair

• Prof. Torranin Chairuangsri Dean of Faculty of Science, Chiang Mai University, Thailand • Asst. Prof. Schradh Saenton Associate Dean for Academic Affairs, Faculty of Science,

Chiang Mai University, Thailand • Assoc. Prof. Prasit Wangpakapattanawong Associate Dean for Research and International Relations,

Faculty of Science, Chiang Mai University, Thailand • Asst. Prof. Sittichai Wirojanupatump Head of Chemistry Department, Faculty of Science,

Chiang Mai University, Thailand • Asst. Prof. Winita Punyodom Head of Materials Science Research Center, Faculty of Science,

Chiang Mai University, Thailand • Dr. Uracha Ruktanonchai Deputy Executive Director, National Nanotechnology Center

(NANOTEC), NSTDA, Thailand • Dr. Kajornsak Faungnawakij Research Unit Director of Nanomaterials and Nanosystems

Engineering Research Unit, National Nanotechnology Center (NANOTEC), NSTDA, Thailand

Scientific Committee Chair

• Prof. Suthep Suantai Chiang Mai University, Thailand

Computational Chemistry Program Chair

• Dr. Supawadee Namuangruk National Nanotechnology Center (NANOTEC), NSTDA, Thailand

• Asst. Prof. Nawee Kungwan Chiang Mai University, Thailand

Computational Biology and Bioinformatics Program Chair

• Asst. Prof. Thanyada Rungrotmongkol Chulalongkorn University, Thailand • Assoc. Prof. Panida Surawatanawong Mahidol University, Thailand

Computational Physics, Computational Fluid Dynamics and Solid Mechanics Program Chair

• Assoc. Prof. Yongyut Laosiritaworn Chiang Mai University, Thailand • Assoc. Prof. Anucha Yangthaisong Ubon Ratchathani University, Thailand • Asst. Prof. Worasak Sukkabot Ubon Ratchathani University, Thailand

High Performance Computing, Computer Science, and Engineering Program Chair

• Dr. Piyawut Srichaikul National Electronics and Computer Technology Center (NECTEC), NSTDA, Thailand

• Dr. Manaschai Kunaseth National Electronics and Computer Technology Center (NECTEC), NSTDA, Thailand

Mathematics and Statistics Program Chair • Prof. Suthep Suantai Chiang Mai University, Thailand • Asst. Prof. Thanasak Mouktonglang Chiang Mai University, Thailand

Experiment Meets Theory Program Chair

• Assoc. Prof. Siriporn Jungsuttiwong Ubon Ratchathani University, Thailand • Assoc. Prof. Vuthichai Ervithayasuporn Mahidol University, Thailand • Assoc. Prof. Phornphimon Maitarad Shanghai University, China • Dr. Pinit Kidkhunthod Synchrotron Light Research Institute, Thailand

A Joint Workshop on e-Science and High Performance Computing: eHPC2019

• Dr. Piyawut Srichaikul National Electronics and Computer Technology Center (NECTEC), NSTDA, Thailand

• Dr. Manaschai Kunaseth National Electronics and Computer Technology Center (NECTEC), NSTDA, Thailand

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International Scientific Committee

• Prof. Xin Xu Fudan University, China • Prof. Koichi Kato Institute of Molecular Sciences, Japan • Prof. Ras B. Pandey University of Southern Mississippi, USA • Prof. Steven R. Kirk Hunan Normal University, China • Dr. Kaito Takahashi Academia Sinica, Taiwan • Dr. Jer-Lai Kuo Academia Sinica, Taiwan • Prof. Jun-Ya Hasegawa Hokkaido University, Japan • Prof. Jianwen Liu Shenzhen University, China • Prof. Akihito Ishizaki National Institutes of Natural Sciences, Japan • Prof. Tetsuya Taketsugu Hokkaido University, Japan • Prof. Yasuteru Shigeta University of Tsukuba, Japan • Prof. Jen-Shiang K. Yu National Chiao Tung University, Taiwan • Prof. Seiji Mori Ibaraki University, Japan • Prof. Deva Priyakumar International Institute of Information Technology, India • Prof. Jhih-Wei Chu National Chiao Tung University, Taiwan • Prof. Samantha Jenkins Human Normal University, China • Prof. Thorsten Dickhaus University of Bremen, Germany • Prof. Shuai Yuan Shanghai University, China • Prof. Richard M. Laine University of Michigan, USA • Prof. Hidehiro Sakurai Osaka University, Japan • Prof. Lei Huang Shanghai University, China • Prof. Yong-Hyun Kim Korea Advanced Institute of Science and Technology (KAIST),

Republic of Korea • Prof. Akira Nakayama University of Tokyo, Japan • Assoc. Prof. Lam K. Huynh International University-Vietnam National University, Vietnam • Assoc. Prof. Malgorzata Biczysko Shanghai University, China • Assoc. Prof. Norio Yoshida Kyushu University, Japan • Assoc. Prof. Hisashi Okumura Institute for Molecular Science, Japan • Asst. Prof. Satoru Itoh Institute for Molecular Science, Japan • Asst. Prof. Min-Yeh Tsai Tamkang University, Taiwan • Assoc. Prof. Phornphimon Maitarad Shanghai University, China

National Scientific Committee

• Prof. Suthep Suantai Chiang Mai University, Thailand • Assoc. Prof. Siriporn Jungsuttiwong Ubon Ratchathani University, Thailand • Assoc. Prof. Panida Surawatanawong Mahidol University, Thailand • Assoc. Prof. Yongyut Laosiritaworn Chiang Mai University, Thailand • Assoc. Prof. Anucha Yangthaisong Ubon Ratchathani University, Thailand • Assoc. Prof. Vuthichai Ervithayasuporn Mahidol University, Thailand • Asst. Prof. Nawee Kungwan Chiang Mai University, Thailand • Asst. Prof. Thanyada Rungrotmongkol Chulalongkorn University, Thailand • Asst. Prof. Worasak Sukkabot Ubon Ratchathani University, Thailand • Asst. Prof. Thanasak Mouktonglang Chiang Mai University, Thailand • Dr. Supawadee Namuangruk National Nanotechnology Center (NANOTEC), NSTDA,

Thailand • Dr. Pussana Hirunsit National Nanotechnology Center (NANOTEC), NSTDA,

Thailand • Dr. Chompoonut Rungnim National Nanotechnology Center (NANOTEC), NSTDA,

Thailand • Dr. Piyawut Srichaikul National Electronics and Computer Technology Center

(NECTEC), NSTDA, Thailand • Dr. Manaschai Kunaseth National Electronics and Computer Technology Center

(NECTEC), NSTDA, Thailand • Dr. Pinit Kidkhunthod Synchrotron Light Research Institute, Thailand • Dr. Supareak Praserthdam Chulalongkorn University, Thailand

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Organizing Committee Chair

• Asst. Prof. Nawee Kungwan Chiang Mai University, Thailand

Organizing Committee

• Dr. Supawadee Namuangruk National Nanotechnology Center (NANOTEC), NSTDA, Thailand

• Assoc. Prof. Siriporn Jungsuttiwong Ubon Ratchathani University, Thailand • Asst. Prof. Thanyada Rungrotmongkol Chulalongkorn University, Thailand • Assoc. Prof. Jaroon Jakmunee Chiang Mai University, Thailand • Asst. Prof. Nawee Kungwan Chiang Mai University, Thailand • Asst. Prof. Pitchaya Mungkornasawakul Chiang Mai University, Thailand • Asst. Prof. Kritsana Jitmanee Chiang Mai University, Thailand • Asst. Prof. Narin Lawan Chiang Mai University, Thailand • Asst. Prof. Nuttee Suree Chiang Mai University, Thailand • Asst. Prof. Burapat Inceesungvorn Chiang Mai University, Thailand • Assoc. Prof. Piyarat Nimmanpipug Chiang Mai University, Thailand • Asst. Prof. Pipat Ruankham Chiang Mai University, Thailand • Assoc. Prof. Chulin Likasiri Chiang Mai University, Thailand • Asst. Prof. Kanyuta Poochinapan Chiang Mai University, Thailand • Asst. Prof. Kontad Ounnunkad Chiang Mai University, Thailand • Asst. Prof. Thanasak Mouktonglang Chiang Mai University, Thailand • Asst. Prof. Morrakot Khebchareon Chiang Mai University, Thailand • Asst. Prof. Somchai Sriyab Chiang Mai University, Thailand • Asst. Prof. Thaned Rojsiraphisal Chiang Mai University, Thailand • Asst. Prof. Thongchai Dumrongpokaphan Chiang Mai University, Thailand • Asst. Prof. Thunwadee Limtharakul Chiang Mai University, Thailand • Dr. Chanisorn Ngaojampa Chiang Mai University, Thailand • Dr. Thapanar Suwanmajo Chiang Mai University, Thailand • Dr. Natthawat Semakul Chiang Mai University, Thailand • Dr. Wasut Pornpatcharapong Chiang Mai University, Thailand • Dr. Wan Wiriya Chiang Mai University, Thailand • Dr. Saranphong Yimklan Chiang Mai University, Thailand • Dr. Pumis Thuptimdang Chiang Mai University, Thailand • Dr. Yothin Chimupala Chiang Mai University, Thailand • Dr. Ben Wongsaijai Chiang Mai University, Thailand • Dr. Chanida Puangpila Chiang Mai University, Thailand • Dr. Nattapol Ploymaklam Chiang Mai University, Thailand • Dr. Nawinda Chutsagulprom Chiang Mai University, Thailand • Dr. Supanut Chaidee Chiang Mai University, Thailand • Mr. Thanakorn Suwanprasert Chiang Mai University, Thailand • Ms. Amporn Tapburee Chiang Mai University, Thailand • Mr. Apiroj Lekyong Chiang Mai University, Thailand • Ms. Jarunee Ngeonphacho Chiang Mai University, Thailand • Mr. Krisanat Nakthong Chiang Mai University, Thailand • Mr. Pichai Nakpathom Chiang Mai University, Thailand • Mr. Pichet Thepsuwan Chiang Mai University, Thailand • Ms. Rachada Wongsuwan Chiang Mai University, Thailand • Ms. Sirichan Wongkaew Chiang Mai University, Thailand • Ms. Thanikan Yamano Chiang Mai University, Thailand

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Session Summary

Plenary Lecture

PL-1 Prof. Xin Xu

Department of Chemistry, Fudan University, China

PL-2 Prof. Koichi Kato

Institute for Molecular Science (IMS), National Institutes of Natural Sciences, Japan

Scientific Session

ANSCSE23 will be held under the theme “Expanding your mind” and will cover topics in the following area:

CHE Computational Chemistry CSE High Performance Computing,

Computer Science, and Engineering

BIO Computational Biology, Bioinformatics,

Biochemistry, and Biophysics MST Mathematics and Statistics

PFD Computational Physics, Computational

Fluid Dynamics, and Solid Mechanics EMT Experiment Meets Theory

Special Talk

ST1 eHPC: Current Status of Thailand HPC Infrastructure

Dr. Piyawut Srichaikul

NSTDA Supercomputer Center, Thailand

ST2 Applications of Synchrotron-based X-ray Absorption Spectroscopy on Advanced

Functional Materials

Dr. Pinit Kidkhunthod

Synchrotron Light Research Institute (Public Organization), Thailand

S3 Combined Experimental Computational Multi-Scale Studies in Catalysis

Dr. Supareak Praserthdam

Chulalongkorn University, Thailand

Special Workshop

eHPC A Joint Workshop on e-Science and High Performance Computing: eHPC2019

Dr. Piyawut Srichaikul and Dr. Manaschai Kunaseth

National Electronics and Computer Technology Center (NECTEC), NSTDA, Thailand

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Overall Program

Wednesday, June 26, 2019

Expected all participants arrive in Chiang Mai and check-in at hotels

Thursday, June 27, 2019

08.00 – 09.00 Registration 1st floor of

CB1

09.00 – 09.30 Opening Ceremony

Room:

CB1220

Chairman: Supawadee Namuangruk

09.30 – 10.15 The XYG3 Type of Doubly Hybrid Density Functionals: From

Molecular Systems to Extended Solids

Plenary Lecture: Prof. Xin Xu from Fudan University, China

10.15 – 10.35 eHPC: Current Status of Thailand HPC Infrastructure

Special Talk: Dr. Piyawut Srichaikul from NECTEC, Thailand

10.35 – 11.00 Coffee Break

11.00 – 12.00

Six Parallel Sessions

Room:

CB1220

Room:

CB1310

Room:

CB1313

Room:

CB1210

Room:

CB1314

Room:

CB1320

CHE BIO PFD MST EMT eHPC

12.00 – 13.00 Lunch

13.00 – 15.30


Room:

CB1220

Room:

CB1310

Room:

CB1313

Room:

CB1210

Room:

CB1314

Room:

CB1320



15.50 – 17.15


Room:

CB1220

Room:

CB1310

Room:

CB1313

Room:

CB1210

Room:

CB1314

Room:

CB1320


17.15 – 18.30 Poster Session

18.45 Welcome Dinner

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Friday, June 28, 2019

Chairman: Thanyada Rungrotmongkol and Siriporn Jungsuttiwong

Room:

CB1220

09.00 – 09.45

Experimental and Computational Approaches for Elucidating

Glycofunctional Mechanisms

Plenary Lecture: Prof. Koichi Kato from Institute of Molecular

Science, Japan

09.45 – 10.05

Applications of Synchrotron-based X-ray Absorption

Spectroscopy on Advanced Functional Materials

Special Talk: Dr. Pinit Kidkhunthod from Synchrotron Light

Research Institute, Thailand

10.05 – 10.25

Combined Experimental Computational Multi-Scale Studies in

Catalysis

Special Talk: Dr. Supareak Praserthdam from Catalyst Group,

Chulalongkorn University, Thailand


11.00 – 12.00


Room:

CB1220

Room:

CB1320

Room:

CB1313

Room:

CB1210

Room:

CB1314

Room:

CB1310

CHE BIO PFD MST EMT CSE

12.00 – 13.00 Lunch

13.00 – 15.00


Room:

CB1220

Room:

CB1320

Room:

CB1313

Room:

CB1210

Room:

CB1314

Room:

CB1310



15.20 – 16.35


Room:

CB1220

Room:

CB1320

Room:

CB1313

Room:

CB1210

Room:

CB1314

Room:

CB1310


16.35 – 17.00 Closing Conference with Poster and Oral Presentation Award Announcement

Saturday, June 29, 2019

07.15 – 16.00 Excursion: Mountain Tour (optional)

16.00 – 22.00 Saturday walking street market (optional)

Sunday, June 30, 2019

Depart from Chiang Mai Province

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Scientific Program

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Session: Computational Chemistry (CHE)


08.00 – 09.00 Registration


09.30 – 10.15 Plenary Lecture 1

10.15 – 10.35 Special Talk 1


Chairman: Supawadee Namuangruk and

Chompoonut Rungnim

11.00 – 11.30 CHE-I-01 Prof. Yong-Hyun Kim

11.30 – 12.00 CHE-I-02 Prof. Akihito Ishizaki

12.00 – 13.00 Lunch

Chairman: Akira Nakayama and

Suwit Suthirakun

13.00 – 13.30 CHE-I-03 Prof. Malgorzata Biczysko

13.30 – 14.00 CHE-I-04 Prof. Jianwen Liu

14.00 – 14.30 CHE-I-05 Dr. Chompoonut Rungnim

14.30 – 15.00 CHE-I-06 Dr. Supareak

Praserthdam

15.00 – 15.15 CHE-O-01 Mr. Tinnakorn Saelee

15.15 – 15.30 CHE-O-02 Mr. Yuki Oba


Chairman: Tetsuya Taketsugu and

Jun-Ya Hasegawa

15.50 – 16.20 CHE-I -07 Prof. Woo-Youn Kim

16.20 – 16.35 CHE-O-03 Mr. Panyakorn Taweechat

16.35 – 16.50 CHE-O-04 Mr. Tanabat Mudchimo





09.45 – 10.05 Special Talk 2

10.05 – 10.25 Special Talk 3


Chairman: Jianwen Liu and Supawadee Namuangruk

11.00 – 11.30 CHE-I-08 Prof. Jun-Ya Hasegawa

11.30 – 12.00 CHE-I-09 Dr. Suwit Suthirakun

12.00 – 13.00 Lunch

Chairman: Tim Kowalczyk and Nawee Kungwan

13.00 – 13.30 CHE-I-10 Prof. Tetsuya Taketsugu

13.30 – 14.00 CHE-I-11 Dr. Karan Bobuatong

14.00 – 14.30 CHE-I-12 Prof. Akira Nakayama

14.30 – 14.45 CHE-O-05 Ms. Kiriko Ishii

14.45 – 15.00 CHE-O-06 Mr. Nuttapon Yodsin


Chairman: Malgorzata Biczysko and

Supareak Praserthdam

15.20 – 15.50 CHE-I-13 Dr. Takako Mashiko

15.50 – 16.20 CHE-I-14 Prof. Tim Kowalczyk

16.35 – 17.00 Closing Conference

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Session: Computational Biology, Bioinformatics, Biochemistry, and Biophysics (BIO)





10.15 – 10.35 Special Talk 1


Chairman: Thanyada Rungrotmongkol

11.00 – 11.30 BIO-I-01 Prof. Lee-Wei Yang

11.30 – 12.00 BIO-I-02 Prof. Hisashi Okumura

12.00 – 13.00 Lunch

Chairman: Seiji Mori and Jitrayut Jitonnom

13.00 – 13.30 BIO-I-03 Prof. Deva Priyakumar

13.30 – 14.00 BIO-I-04 Prof. Jen-Shiang K. Yu

14.00 – 14.30 BIO-I-05 Prof. Norio Yoshida

14.30 – 15.00 BIO-I-06 Prof. Min-Yeh Tsai

15.00 – 15.15 BIO-O-01 Mr. Nikorn Shinsuphan

15.15 – 15.30 BIO-O-02 Mr. Thodsaphon

Lunnoo


Chairman: Jen-Shiang K.Yu

15.50 – 16.20 BIO-I-07 Prof. Yasuteru Shigeta

16.20 – 16.35 BIO-O-03 Mr. Pikkanet Suttirat

16.35 – 16.50 BIO-O-04 Mr. Pongsakorn

Kanjanatanin





09.45 – 10.05 Special Talk 2

10.05 – 10.25 Special Talk 3


Chairman: Panida Surawatanawong

11.00 – 11.30 BIO-I-08 Prof. Christian Schröder

11.30 – 12.00 BIO-I-09 Prof. Jhih-Wei Chu

12.00 – 13.00 Lunch

Chairman: Yasuteru Shigeta and Nuttee Suree

13.00 – 13.30 BIO-I-10 Prof. Seiji Mori

13.30 – 14.00 BIO-I-11 Prof. Satoru Itoh

14.00 – 14.30 BIO-I-12 Prof. Supa Hannongbua

14.30 – 14.45 BIO-O-05 Mr. Tanawat Horsirimanon

14.45 – 15.00 BIO-O-06 Ms. Natchayatorn Keawkla


Chairman: Deva Priyakumar

15.20 – 15.50 BIO-I-13 Prof. Jitrayut Jitonnom

15.50 – 16.20 BIO-I-14 Prof. Nuttee Suree


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Session: Computational Physics, Computational Fluid Dynamics

and Solid Mechanics (PFD)





10.15 – 10.35 Special Talk 1


Chairman: Yongyut Laosiritaworn and

Suraphong Yuma

11.00 – 11.30 PFD-I-01 Prof. Samantha

Jenkins

11.30 – 12.00 PFD-I-02 Dr. Tirawut

Wooakitpoonpon

12.00 – 13.00 Lunch

Chairman: Anucha Yangthaisong and

Worasak Sukkabot

13.00 – 13.30 PFD-I-03 Prof. Lam K.Kuynh

13.30 – 14.00 PFD-I-04 Prof. Steven R.Kirk

14.00 – 14.30 PFD-I-05 Prof. Ras B.Pandey

14.30 – 15.00 PFD-I-06 Dr. Suraphong Yuma

15.00 – 15.30 PFD-I-07 Dr. Osamu Kobayashi


Chairman: Udomsilp Pinsook and

Worasak Sukkabot

15.50 – 16.20 PFD-I-08 Prof. Masanori

Tachikawa

16.20 – 16.50 PFD-I-09 Prof. Theerapong

Puangmali

16.50 – 17.05 PFD-O-01 Ms. Saowalak Somjid





09.45 – 10.05 Special Talk 2

10.05 – 10.25 Special Talk 3


Chairman: Theerapong Puangmali and

Tirawut Wooakitpoonpon

11.00 – 11.30 PFD-I-10 Prof. Jer-Lai Kuo

11.30 – 12.00 PFD-I-11 Prof. Udomsilp Pinsok

12.00 – 13.00 Lunch


Worasak Sukkabot

13.00 – 13.30 PFD-I-12 Prof. Taro Udagawa

13.30 – 14.00 PFD-I-13 Prof. Malliga

Suewattana

14.00 – 14.30 PFD-I-14 Dr. Tsutomu Kawatsu

14.30 – 14.45 PFD-O-02 Dr. Kanokkorn

Pimcharoen



Worasak Sukkabot

15.20 – 15.50 PFD-I-15 Dr. Nirand Pisutha-

Arnond

15.50 – 16.20 PFD-I-16 Prof. Ryo Maezono

16.20 – 16.35 PFD-I-17 Prof. Kenta Hongo


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Session: Mathematics and Statistics (MST)





10.15 – 10.35 Special Talk 1

10.35– 11.00 Coffee Break

Chairman: Suthep Suantai and

Thanasak Mouktonglang

11.00 – 11.30 MST-I-01 Prof. Kazuyuki

Koizumi

11.30 – 12.00 MST-I-02 Prof. Thorsten

Dickhaus

12.00 – 13.00 Lunch

Chairman: Suthep Suantai and

Thanasak Mouktonglang

13.00 – 13.30 MST-I-03 Prof. Chalump

Oonariya

13.30 – 14.00 MST-I-04 Prof. Pakinee

Aimmanee

14.00 – 14.30 MST-I-05 Prof. Chidchanok

Lursinsap

14.30 – 14.45 MST-O-01 Ms. Wipawinee

Chaiwino

14.45 – 15.00 MST-O-02 Mr. Panasun Manorost

15.00 – 15.15 MST-O-03 Mr. Tawatchai

Petaratip

15.15 – 15.30 MST-O-04 Ms. Monthiya

Ruangnai





No session

17


Session: Experiment Meets Theory (EMT)





10.15 – 10.35 Special Talk 1


Chairman: Siriporn Jungsuttiwong

11.00 – 11.30 EMT-I-01 Prof. Hiroshi M.

Yamamoto

11.30 – 12.00 EMT-I-02 Prof. Vinich Promarak

12.00 – 13.00 Lunch

Chairman: Hiroshi M. Yamamoto and

Vuthichai Ervithayasuporn

13.00 – 13.30 EMT-I-03 Prof. Nantanit

Wanichacheva

13.30 – 14.00 EMT-I-04 Dr. Duangporn Polpanich

14.00 – 14.30 EMT-I-05 Dr. Deanpen Japrung

14.30 – 15.00 EMT-I-06 Prof. Shinji Nonose

15.00 – 15.30 EMT-I-07 Prof. Richard M. Laine


Chairman: Nantanit Wanichacheva

15.50 – 16.20 EMT-I-08 Prof. Hidehiro Sakurai

16.20 – 16.50 EMT-I-09 Dr. Kajornsak

Faungnawakij





09.45 – 10.05 Special Talk 2

10.05 – 10.25 Special Talk 3


Chairman: Phornphimon Maitarad

11.00 – 11.30 EMT-I-10 Prof. Shuai Yuan

11.30 – 12.00 EMT-I-11 Dr. Pinit Kidkhunthod

12.00 – 13.00 Lunch

Chairman: Kajornsak Faungnawakij and

Pinit Kidkhunthod

13.00 – 13.30 EMT-I-12 Prof. Vuthichai

Ervithayasuporn

13.30 – 14.00 EMT-I-13 Prof. Takuji Ohigashi

14.00 – 14.30 EMT-I-14 Prof. Lei Huang

14.30 – 15.00 EMT-I-15 Prof. Theeranun Siritanon


Chairman: Lei Huang

15.20 – 15.50 EMT-I-16 Prof. Kittipong Chainok

15.50 – 16.20 EMT-I-17 Prof.Burapat Inceesungvorn


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Session: High Performance Computing, Computer Science and Engineering (CSE)


No session

.



09.45 – 10.05 Special Talk 2

10.05 – 10.25 Special Talk 3


Chairman: Manaschai Kunaseth

11.00 – 11.30 CSE-I-01 Dr. Putt Sakdhnagool

11.30 – 11.45 CSE-O-01 Dr. Arpiruk Hokpunna

12.00 – 13.00 Lunch

Chairman: Putt Sakdhnagool

13.00 – 13.15 CSE-O-02 Dr. Chidchanok

Choksuchat

13.15 – 13.30 CSE-O-03 Ms. Wassamon

Phusakulkajorn



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Special Workshop

20


Workshop on e-Science and High Performance Computing: eHPC2019

“A Drive Toward National Computing Platform”

09.00 – 17.00, June 27, 2019, Room: CB1320, Chiang Mai University

Time Invited Speakers / Topic



09.30 – 10.15 The XYG3 Type of Doubly Hybrid Density Functionals: From


Plenary Lecture: Prof. Xin Xu from Fudan University, China

10.15 – 10.35 eHPC: Current Status of Thailand HPC Infrastructure

Special Talk: Dr. Piyawut Srichaikul from NECTEC, Thailand


11.00 – 11.25 Dawn of TARA: An Early Experience and Lesson Learned from

Developing Large-Scale Generalized HPC Service

Dr. Manaschai Kunaseth

11.25 – 11.50 HPC and AI in KU

Prof. Putchong Uthayopas

12.00 – 13.30 Lunch

13.30 – 13.55 Big data & AI in academic

Prof. Sarana Nutanong

13.55 – 14.20 Early experience of Taiwan Computing Cloud

Chun-Yu LIN, Associate Researcher

14.20 – 14.45 TBA

Dr. Utane Swangwit

14.45 – 15.10 Usability of Four-Factor Authentication in Information Security

Dr. Chalee Vorakulpipat


15.45 – 17.00 Panel discussion: การพฒนา National Computing Platform ใหเหมาะสมกบการใชงานในประเทศไทย (Thai language)

17.15 – 18.30 Poster Session (All Poster Presentation)


21


General Information

1. The ANSCSE23 registration counter will be located in front of the Multifunctional Room (CB1112), 1st floor of Chemistry Building 1 (CB1), Department of Chemistry, Faculty of Science, Chiang Mai University. Opening times are as follows:

Thursday, June 27 at 8:00 a.m. – 9:00 a.m. Friday, June 28 at 8:00 a.m. – 9:00 a.m.

2. Registration will be in alphabet order according to A-Z (List of participants). Please memorize your status.

3. Upon registration at the counter, you will receive your badge, receipt and conference materials. To facilitate the process, please bring with you your registration confirmation. You are kindly requested to wear your name badge during all events of the conference.

4. In case of issuing the new receipt (misspelling name/address or any incorrect information), the fee of 300 Baht for Thai participants or 10 USD for foreigner participants will be charged per receipt.

Poster presentation

1. The content of the poster should cover title, objectives, methodology, results, discussion, and conclusion.

2. The poster size must not exceed 80 cm width x 120 cm height.

Oral presentation

1. Oral presentations are required to be made by PowerPoint 2003 or higher.

2. Standard fonts, such as Arial, Times New Roman or Cordia New are preferable for the PowerPoint presentation.

3. All speakers are required to load and check the files in slide loading room at least 2 hours before the presentation.

4. The presentation time for general oral presentation is 15 minutes (12 minutes for presentation + 3 minutes for Q&A).

5. The time for invited presentation is 30 minutes (25 minutes for presentation + 5 minutes for Q&A).

6. A PC computer and an LCD projector will be provided.

22


Plenary Lecture

23


The XYG3 Type of Doubly Hybrid Density Functionals: From


X. Xu*

Collaborative Innovation Center of Chemistry for Energy Materials, Shanghai Key Laboratory of Molecular

Catalysis and Innovative Materials, MOE Key Laboratory of Computational Physical Sciences, Department of

Chemistry, Fudan University, Shanghai 200433, China * E-mail: [email protected]; Fax: +86 21 6564 3029; Tel. +86 21 3124 3529

ABSTRACT

Doubly hybrid (DH) functionals have emerged as a new class of density functional approximations (DFAs), which not only have a non-local orbital-dependent component in the exchange part, but also incorporate the information of unoccupied orbitals in the correlation part, being at the top rung of Perdew’s view of Jacob’s ladder in DFAs. In particular, the XYG3 1 type of doubly hybrid (xDH) functionals use a low rung functional (e.g. B3LYP) to perform the self-consistent-field calculation to generate orbitals and densities, with which a top rung DH functional is used for final energy evaluation. The xDH functionals have been shown to have remarkable accuracy for molecular systems. This talk presents the results that the xDH functionals are extended from molecular systems to extended solids. This is achieved by combining the xDH functionals with the XO (i.e., eXtended ONIOM 2) method that allows for the overlapping fragmentation. Here the high level is described with the cluster model at the xDH level, while the low level for the whole system is now described with the periodic boundary condition (PBC) at the PBE level. The method, thus coined as XO-PBC@XYG3 3, is applied to the cohesive energy predictions for molecular crystals, which shows promise in discriminating the multiple crystal packing motifs that have important implications for pharmaceuticals, organic semiconductors, and many other chemical applications. Keywords: XYG3, XO, molecular crystal, density functional theory, ONIOM REFERENCES

1. Zhang, Y., Xu, X., Goddard, W. A. III Proc. Nat. Acad. Sci, USA, 2009, 106, 4963-8. 2. Guo, W., Wu, A., Xu, X. Chem. Phys. Lett., 2010, 498, 203-8. 3. Chen, B., Xu, X. To be submitted.

Ph.D. at Xiamen University (XM), China, in 1991.

Associate professor (1993-1995) and Professor (1995-2010) at Department of Chemistry, XM, Lu-Jia-Xi chair professor at Department of Chemistry, XM, (2006-2010).

Distinguished professor at Department of Chemistry, Fudan University (2010-), Changjiang chair professor (2012-).

24


Experimental and Computational Approaches for Elucidating

Glycofunctional Mechanisms

Koichi Kato1, 2, 3

1 Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences,

Okazaki, Japan

2 Institute for Molecular Science (IMS), National Institutes of Natural Sciences, Okazaki, Japan 3 Graduate School of Pharmaceutical Sciences, Nagoya City University

* E-mail: [email protected]; Fax: +81 564 59 5225; Tel. +81 564 59 5224

ABSTRACT

Since biomolecules exert versatile functions through interacting with their binding partners, detail structural characterization of their interaction modes is of importance not only for deeper understanding the functional mechanisms but also for controlling and improving their functionality. Accumulating crystallographic data of biomolecular complexes have provided atomic descriptions of their interactions, offering the structural basis necessary for rational biomolecular engineering and drug design. However, it should be noted that biomolecules generally possess motional freedoms under physiologically conditions. Oligosaccharides represent one of the most extreme classes of biomolecules that are characterized by conformational flexibility.

We have developed a method for elucidating dynamic conformations of oligosaccharides in solution by employing MD simulation in conjunction with our developed NMR technique [1]. This has enabled exploration of conformational spaces of complicated, branched oligosaccharides. We herein apply this method to design of unnatural oligosaccharides having higher affinities for a specific target protein. Furthermore, we combined our structural biology approach with MD simulation for characterizing conformational dynamics of the Fc portion of immunoglobulin G, thereby providing a mechanistic view of improved functional efficacy of therapeutic antibodies depending on their glycosylation [2]. Keywords: NMR spectroscopy, MD simulation, Oligosaccharide, glycoprotein, therapeutic antibody REFERENCES

1. Kato, K., Yagi, H., and Yamaguchi, T. Modern Magnetic Resonance, 2nd Edition (G.A.Webb ed.), Springer International Publishing, 737-754 (2018)

2. Yagi, H., Yanaka, S., and Kato, K. Glycobiophysics (Y.Yamaguchi and K.Kato ed.), Springer Nature Singapore, 219-235 (2018)

Ph.D. at the Tokyo University (TU) in 1991, Assistant professor (1991) and Lecturer (1997) at Graduate School of Pharmaceutical Sciences, TU, Professor at Graduate School of Pharmaceutical Sciences, Nagoya City University (2000-), Professor at Okazaki Institute for Integrative Bioscience (Institute for Molecular Science 2008-) and Director and Professor at Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences (2018-).

25


Special Talk

26


eHPC: Current Status of Thailand Hpc Infrastructure

P.Srichaikul

NSTDA Supercomputer Center, Thailand

* E-mail: [email protected] Tel. +66-2564-6900

ABSTRACT

HPC infrastructure is a critical component for Computational science and Engineering research advancement. It has been nearly three decades of Thailand HPC journey. While it was a bumpy road, progress were made, even if far from the dream for fields of gold.

This presentation gives a brief update on Thailand HPC infrastructure and its related activities. Keywords: High Performance Computing, Computing Infrastructure, Supercomputing

Ph.D. (Solid State Physics) Auburn University, USA. (1995), NSTDA Supercomputer Center (ThaiSC), NECTEC, NSTDA (2019- )

A nice looking Bangkok native middle age Asian male with dark sense of humor who had scientific training background knowledge in Solid State Physics. His work experience over 20 years at National Electronics and Computer Technology Center, Thailand put him in multi-disciplinary roles of coordination and management such as Computational Science, High Performance Computing, Geo-informatics, Assistive Technology, Organization Management, Research Innovation, Data Analytics, and most recent, Computing Infrastructure Service. His latest post is senior researcher supervising Computing and Cyber-Physical Infrastructure of NSTDA.

mailto:[email protected]

27


Applications of Synchrotron-Based X-Ray Absorption

Spectroscopy on Advanced Functional Materials

Pinit Kidkhunthod*

Synchrotron Light Research Institute (Public Organization), 111 University Avenue, Muang, Nakhon

Ratchasima, 30000, Thailand * E-mail: [email protected]; Fax: +66 44 217 047; Tel. +66 44 217 040

ABSTRACT

The investigation of the local geometric and electronic structure of probing element in bulk samples is the most extensive field of application in X-ray Absorption Spectroscopy (XAS). XAS consists of two main regions which are X-ray Absorption Near Edge Structure (XANES) and Extended X-ray Absorption Fine Structure (EXAFS). The former region is used to explain the local geometry and oxidation states of selected element in a sample whilst the latter one is used to address the local structure around probing element in samples. Owing to the high brightness of synchrotron radiation, synchrotron based infrared microspectroscopy provides high spatial resolution, better signal to noise ratio and shorter data acquisition time than the conventional source. In my talk, the XAS beamlines and their applications on advanced functional materials will be introduced in order to obtain the accuracy of their locally structural information which cause that such properties in these materials.

Keywords: Advanced functional materials, Local structure, X-ray absorption spectroscopy, XANES, EXAFS REFERENCES

[1] P. Kidkhunthod, Structural studies of advanced functional materials by synchrotron-based x-ray absorption spectroscopy: BL5. 2 at SLRI, Thailand, Advances in Natural Sciences: Nanoscience and Nanotechnology 8, 035007

Dr. Pinit Kidkhunthod is a beamline manager at the SUT-NANOTEC-SLRI XAS beamline (BL5.2), Synchrotron Light Research Institute (Public Organization), Nakhon Ratchasima, Thailand. His research of interest is in the fields of structural studies of advanced functional materials such as energy materials, carbon-based ferrite composite materials and amorphous materials and novel glasses using an X-ray absorption spectroscopy (XAS) technique. Dr. Pinit Kidkhunthod received his B.Sc. (Physics), first class honors 3.99 from Khon Kaen University, Thailand in 2008, and Ph.D. (Physics) from Bristol University, U.K in 2012. He was one of two Thai students representative for DESY summer program, Germany, in 2007. Recently, Dr. Kidkhunthod has received research grants for young scientist from Thailand Research Fund (TRF2013), Ministry of Science and Technology (2014) and SUT-Center of Excellent on advanced functional materials (SUT-COE-AFM) from 2015-present. Moreover, he has been awarded a visiting professor position from SAIT, China during 2018-2020. He is the author and co-author of over 100 papers in ISI journals for structural studies of advanced functional materials using XAS technique.

28


Combined Experimental-Computational Multi-Scale Studies

in Catalysis

Supareak Praserthdam

Center of Excellence on Catalysis and Catalytic Reaction Engineering (CECC), Department of Chemical

Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand * E-mail: [email protected]; Fax: +66 2 2186761; Tel. +66 8 6101 2244

ABSTRACT

The Center of Excellence on Catalysis and Catalytic Reaction Engineering aims towards the goal to help to mitigate the global warming issues based on the know-how in catalysis. Therefore, our first research theme dealing with catalysis in energy comprises (1.1) renewable energy production via catalytic processes, (1.2) fuel cell technology, and (1.3) Biorefinery. Catalysis for environment, the second theme consists of (2.1) CO2 conversion to high value-added chemicals and (2.2) the reduction of NOx using SCR catalysts. Finally, the last theme is designated to be the support which is the experimental-computational catalysts screening via a combined high-throughput, density functional theory (DFT), and machine learning. Categorized by areas in catalysis, the center focuses on (1) photocatalysis, (2) electrocatalysis, (3) Ziegler Natta and metallocene catalysts for polymerization, (4) SCR catalysts, (5) computational catalyst screening and design, and (6) process simulation for catalytic processes.

Center of Excellence on Catalysis and Catalytic Reaction Engineering (CECC), Department of Chemical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand was established in 1979 by Prof. Dr. Piyasan Praserthdam, where in January 2 0 0 1 , the center has received its international recognition from hosting the Bangkok International Conference on Heterogeneous Catalyst, the first international conference in catalysis in Thailand.

27

29


e-Proceeding

28

30


List of e-Proceedings

Code Author Title Page

BIO-01

Channarong Khruto

Department of Chemistry, Faculty

of Science, Chulalongkorn

University, Thailand

Molecular Dynamics Simulations of M2 Channel in Phospholipid Bilayers with Different Thickness

31

BIO-02

Pikkanet Suttirat

Department of Physics, Faculty of

Science, Mahidol University,

Thailand

Finite Element Modeling of Vaccine Delivery Using Microneedles: Roles of Microneedle Shape and Antigen Diffusion Rate

39

BIO-03

Auwal Muhammad

Department of Physics, King

Mongkut’s University and Technology, Thonburi, Thailand

Characterization and Identifying the Positional Binding and Ligand Interaction of Wildtype Gh10 Xylanase via Computational Techniques

47

BIO-04

Tanawat Horsirimanon

Department of Electrical and

Computer Engineering, Faculty of

Engineering, King Mongkut's

University of Technology North

Bangkok, Bangkok, Thailand

Classification and Variable Selection in Large p Small n with Imbalanced Data Problems Using Regularized AUC

58

CSE-01

Chidchanok Choksuchat

Information and Communication

Technology Programme, Prince of

Songkla University, Thailand

Implementation IoT Rehabilitation Tracking System for Trigger Finger Patients

65

CSE-02

Wassamon Phusakulkajorn

National Metal and Materials

Technology Center (MTEC),

National Science and Technology

Development Agency (NSTDA),

Thailand

Corrosion Depth Prediction of an Onshore Gas Pipeline by Using Artificial Neural Network

76

31


BIO-01 Molecular Dynamics Simulations of M2 Channel in

Phospholipid Bilayers with Different Thickness

Channarong Khruto and Pornthep Sompornpisut*

Department of Chemistry, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand

*E-mail: [email protected]; Tel: 662-2187604; Fax: 662-2187598

ABSTRACT

M2 channel is an integral membrane protein ion channel which has an important role in transferring proton across the membrane. M2 channel is an important protein in replication of influenza A virus and one of anti-flu drug target. Structure of M2 channel is a homo-tetramer with a 4-fold symmetric arrangement. Each monomer consists of 97 residues. The residues 24-46 form an alpha helix embedded in the membrane. Three-dimension structure of the M2 channel from X-rays crystallography and nuclear magnetic resonance technique provides crucial information that could lead to an understanding of the molecular mechanism of the proton transport. The information can be useful for drug design and discovery. Structural information from site-directed spin labeling and electron spin resonance spectroscopy (SDSL-EPR) shows that the M2 channel undergoes conformational changes when it has been reconstituted in different types of phosphatidylcholine lipids. However, details at the molecular level from SDSL-EPR data are limited. In this study, we performed molecular dynamic simulation of the viral M2 channel in different phospholipid bilayer to examine the stability of the closed-state conformation of the channel. We found that a variation of the thickness of phosphatidylcholine has resulted in conformational changes of M2 channel. It was found that symmetry breaking of the tetramer was observed.

Keywords: Influenza A Virus, M2 Channel, Membrane Thickness, MD simulation, 1. INTRODUCTION

Flu is caused by influenza virus. There are three different types of influenza virus, A, B and C. Different subtypes of influenza A virus such as H3N2, H5N1 and H1N1 are classified according to hemagglutinin [1] and neuraminidase [2] genes.

Figure 1. Influenza A Virus

mailto:[email protected]

32


In the influenza viral membrane, it contains important protein matrix protein 2 called M2. The integral membrane protein M2 is a proton-selective ion channel [1]. In virus replication, the virus enters into the host cell through the binding of hemagglutinin to target cell. Upon entering host-cells, the M2 channel is activated at the low pH of the endosome. The channel undergoes conformational changes from the closed state to the open state, allowing proton flux to acidify the viral interior, [2]. This acidification facilitates to release of the viral genetic materials to the cytoplasm. The viral genome is subsequently imported into the nucleus where the viral RNA transcription and replication occur [6]. Thus, the function of M2 is important in the viral replication process. [4] The M2 channel has been a promising target for anti-influenza drug development. Amantadine or rimantadine are the two main drugs which inhibit the proton conductance of M2 channel. [5]

Figure 2. The replication of influenza A Virus

Figure 3. Amantadine and rimantadine

33


M2 is a homotetrameric protein. Each subunit consists of 97 residues with its N-terminal periplasmic domain, a transmembrane (TM) domain, and a C-terminal cytoplasmic domain. Four transmembrane segments assemble to form the channel pore in a four-fold symmetric arrangement (Figure 4) [1]. His37 is a pH sensor [6] Trp41 acts as a gate. The channel is closed at near neutral pH. At acidic pH, the protonation occurs at the sidechain of His37 tetrad, which subsequently opens the Trp41 gate. This increases the hydration and leads to activation of the M2 channel. [7]

Structural information from site-directed spin labeling and electron paramagnetic resonance spectroscopy (SDSL-EPR) showed a change in the spin-spin coupling of the nitroxide-labeled. M2 channel upon a variation of fatty acid length and saturation of phosphatidylcholine lipids. They suggested that the M2 conformation was sensitive to hydrophobic region of phospholipid bilayers. Especially the four TM segments were rearranged in response to changes in different types of phosphatidylcholine (PC) lipids. [8], [9] However, details at the molecular level from SDSL-EPR data were limited due to the low structural resolution and the number of degrees of freedom of the nitroxide side chain. In addition, the relationship between structure and dynamics of protein backbone and those of the spin-label side chain were not yet well-characterized. In this study, molecular dynamic (MD) simulations of the M2 channel embedded in PC-type lipid bilayers had been conducted to examine the stability of M2 channel in the closed-state conformation. Phosphatidylcholine is the most abundant lipid in cell membranes. Here, three types of PC lipids, 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC), 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) and 1,2-dilauroyl-sn-glycero-3-phosphocholine (DLPC), were used to construct the lipid bilayer systems. The three PC lipids are different in terms of the number of carbons and saturation of fatty acids. POPC (C16:0, C18:1PC) contains 16 and 18 carbons with an unsaturated fatty acid. DLPC (diC12:0PC)) and DMPC (diC14:0PC) contain two saturated acyl chains of C12 and C14, respectively. Hence, such differences influenced a hydrophobic thickness of the bilayer. In order to extract structural information from MD trajectories, the C-alpha root mean square deviation (RMSD) of the M2 channel, the C-alpha root mean square fluctuation (RMSF) of the individual amino acid residue and TM-TM packing were analyzed to validate the stability of M2 in the closed-state conformation

2. COMPUTATIONAL DETAILS

The initial structure of M2 tetramer was taken from the crystal structure of influenza A M2 wild type TM domain (strain A/Udorn/307/1972 H3N2) with PDB code 4QKL (Figure 4). The amino acid sequence of TM segment is shown in Figure 4c. This x-ray structure was obtained at high pH, corresponding to the closed-state channel. Protonation states of ionizable residues were assigned at neutral pH values using PROPKA. [10] The M2 channel was embedded in phospholipid bilayers solvated with TIP3P water models. Three protein-lipid systems were constructed in a periodic box of 85×85×80 Å3 for DLPC (1,2-dilauroyl-sn-glycero-3-phosphocholine), 91×91×88 Å3 for DMPC (1,2-dimyristoyl-sn-glycero-3-phosphocholine) and 92×92×95 Å3 for POPC (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine). A total number of atoms in each system are 47559 for DLPC, 57569 for DMPC, and 61650 for POPC.

34


Figure 4. Tetramer structure of the transmembrane domain of M2 (PDB 4QKL). (a) Transmembrane seen from extracellular side perpendicular to the four-fold axis. Each chain is labeled as TMA, TMB, TMC, and TMD, (b) Side view of TMs. (c) Amino acid sequence of M2 TM

The systems were neutralized by 100 mM NaCl using VMD’s Autoionize plugin. CHARMM36 [11] force field was applied for protein and lipid molecule. System were performed at 300 K and 1 atm. Langevin temperature and Langevin piston pressure coupling schemes were used. [12, 13] Short-range interactions were calculated using a cutoff distance of 12 Å, and particle mesh Ewald method was used for long-range electrostatic interactions [14]. Energy minimization was performed to remove bad contacts between atoms. Restrained MD simulations were employed to relax structural strains of the model systems. In the first stage, the restrained MD was conducted with protein and lipid head group atoms kept fixed to their initial positions. A subsequent run then allowed the whole system (waters, lipids and counterions) except for the protein to be relaxed. Finally, the equilibration and production runs were performed without any positional restraints. All simulation systems were run with a time step of 2 fs. Five independent MD simulations were run for 100 ns to produce large ensembles for obtaining results with statistical significance. The simulated systems were constructed using TCL scripts in VMD. MD simulations were performed with NAMD software. [15]

Analyses of MD trajectories, including root-mean square deviation (RMSD), root-mean square fluctuation (RMSF) and TM-TM distances between the center of mass of selected residues were carried out using TCL scripts in VMD. Unless otherwise specified, analysis of MD trajectories was carried out during the simulation periods of 80-100 ns. The RMSDs relative to the initial structure were calculated only on the Cα atoms. The Cα RMSFs were computed with respect to the average structure of the last 20 ns of MD trajectory

viron surface

-- Intraviron side C. viron surface--

Intraviron side

22 46

35


3. RESULTS AND DISCUSSION

3.1 Equilibrium of systems

An equilibrium of systems was validated by the Cα RMSD with respect to the initial structure. The Cα RMSD profiles of the M2 TMs averaged over the 5 independent runs are shown in Figure 5. For all the three systems, the averaged Cα RMSD profiles of the M2 TMs rose dramatically during the first 10 ns and remained stable with small fluctuation after 20-30 ns of the simulation time of 100 ns (Figure 5). This demonstrated a well-behaved MD simulated system for all three systems. The averaged Cα RMSD values obtained during the last 20 ns simulation time were 2.8 0.2, 2.2 0.2 and 2.5 0.2 Å for M2-DLPC, M2-DMPC and M2-POPC, respectively.

Figure 5. Average RMSD of the four TMs of M2-DLPC, M2-DMPC and M2-POPC

3.2 Fluctuation of amino acid residue

To illustrate the magnitude of TM flexibility, RMSF per TM segment were shown in Figure 6. The results showed that RMSFs of the four TMs in M2-POPC were in the same range of fluctuation (Figure 6c). However, there appeared more difference of RMSFs among the four chains for M2-DLPC and M2-DMPC. In M2-DLPC, a range of RMSF values were slightly different within the four TMs (Figure 6a). However, the average values of TMB and TMD are higher than those of TMA and TMC. This suggested that the flexibility of TMB and TMD are greater than that of TMA and TMC. For M2-DMPC, the flexibility of TMA, TMC and TMD were greater than that of TMB. Therefore, the difference of RMSFs suggested that the four TM helices do not undergo concerted motion when M2 is surrounded by lipid bilayers with shorter fatty acid chain-length. As compared to POPC, DMPC may induce conformational changes of M2 TMs. In addition, the motion of the four TMs in M2-DMPC are difference in magnitudes. This implied asymmetric structural changes of the tetramer in M2-DMPC.

Figure 6. Box plot of RMSF per TM segment of M2-DLPC, M2-DMPC and M2-POPC

36


3.3 Conformational changes of transmembrane helices

Our MD data had revealed a change in M2 TMs motion in response to a reduction of hydrophobic thickness of the bilayer. To proof M2-DMPC undergoing asymmetric motion, we had measured a set of distance between diagonal subunits, i.e. TMA-TMB and TMC-TMD. We used the two centers of mass belonging to the pore-lining residues in diagonal subunits. This included V27, S31, S34, H37 and W41 (Figure 7a). W41 is located near the intracellular side. The diagonal subunit distances were averaged over the last 20ns of MD trajectories. The results are shown in Figure 7b. and Table 1. In M2-POPC, TMA-TMB distances were almost the same as TMC-TMD distances. In contrast to M2-DMPC, TMA-TMB distances were significantly different from TMC-TMD distances. This suggested asymmetric motions of TMs in M2-DMPC has led to a symmetric breaking in the tetramer structure. From Table 1 and Figure 4, the distances between each of S34, H37, and W41 in TMA and its corresponding one in TMB for M2-DLPC are longer than those for M2-POPC. This indicated that the four TMs near the intracellular end move closer in a shorter lipid compared to POPC lipid.

Figure 7. Diagonal subunit distance between the center of mass of the pore-lining residues (a) For clarity, two diagonal subunits were shown. (b) TMA-TMB distance and TMC-TMD distances obtained from M2-DLPC, M2-DMPC and M2-POPC.

Intracellular side

(b)

37


Table 1. Diagonal subunit distance between the center of mass of residues

Residue TMA-TMB Distance (Å) TMC-TMD Distance (Å)

DLPC DMPC POPC DLPC DMPC POPC

V27 9.90.2 10.20.2 10.00.3 9.90.2 9.10.2 10.00.2

S31 11.40.2 12.10.2 11.30.2 11.40.2 10.80.2 11.50.2

S34 10.60.2 11.10.2 10.30.2 10.60.2 9.80.2 10.50.2

H37 10.90.4 10.20.3 9.70.4 10.50.4 9.00.3 9.70.4

W41 12.20.3 12.20.3 12.10.4 12.80.4 12.10.3 12.00.3

4. CONCLUSION

The conformation of M2 channel was investigated using all-atomistic MD simulations. We had shown that different thickness of phospholipid bilayer can induce conformational changes in the transmembrane domain of the channel. In other words, the length of fatty acid can influence the stability of M2 structure in the closed-state by breaking the symmetry arrangement of the channel.

REFERENCES

[1] Skehel. J. J., Wiley. D. C., Annu Rev Biochem, 2000, 69, 531-69. [2] Colman P.M., Textbook of Influenza, Nicholson KG, Webster RG, Hay AJ (eds). Blackwell

Science, London, 1998, 65–73. [3] Pinto. L. H., Holsinger. L. J., Lamb. R. A., 1992, Cell, 69, 517-28. [4] Pinto. L. H., Lamb. R. A., 2006, J Biol Chem., 281, 8997-9000. [5] Hong. M., DeGrado. W. F., 2012, Protein Sci., 21, 1620–1633. [6] Fanghao. H., Wenbin. L., Mei H., 2010, Science, 330, 505-508 [7] Schnell J. R., Chou J. J., 2008, Nature., 451, 591-5 [8] Krisna. C. D., Vikas. N., William. F. D., Kathleen. P. H., 2005, Protein Science, 14, 856-861. [9] Kei. S., Krisna. C. D., Kathleen. P. H., 2015, Biopolymers, 104, 405-411. [10] Perozo. E., Cortes. D. M., Sompornpisut. P., Kloda. A., Martinac. B., 2002, Naturel, 418,

942−948 [11] Jing H., Alexander D. MacKerell J., 2013, J. Comput. Chem. 34, 2135–2145 [12] Feller. S. E., Zhang. Y. H., Pastor. R. W., Brooks. B. R., 1995, J. Chem. Phys., 103, 4613−4621. [13] Martyna. G. J., Tobias. D. J., Klein. M. L., 1994, J. Chem. Phys., 101, 4177−4189 [14] Darden. T., York. D., Pedersen. L., 1993, J. Chem. Phys., 98, 10089−10092 [15] Phillips. J. C., Braun. R., Wang. W., Gumbart. J., Tajkhorshid. E., Villa. E., Chipot. C., Skeel. R.

D., Kale. L., Schulten. K., 2005, J. Comput. Chem. 26, 1781−1802.

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ACKNOWLEDGMENTS

This study was financially support from The Scholarship from the Graduate School, Chulalongkorn University to commemorate 72nd Anniversary of his Majesty King Bhumibol Adulyadej is gratefully acknowledged and the 90th Anniversary Chulalongkorn University Fund (Ratchadaphiseksomphot Endowment Fund.) The simulation and analysis were run on computational resource from Computational Chemistry Center of Excellence (CCCE), department of chemistry, faculty of science, Chulalongkorn university. Thank my advisor, prof. Pornthep Sompornpisut.

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BIO-02

Finite Element Modeling of Vaccine Delivery Using

Microneedles: Roles of Microneedle Shape and

Antigen Diffusion Rate

Pikkanet Suttirat1, Jeerapond Leelawattanachai2, Chaiwoot Boonyasiriwat1, and Charin

Modchang 1,*

1Department of Physics, Faculty of Science, Mahidol University, Bangkok 10400, Thailand 2Nano-Molecular Target Discovery Laboratory, National Nanotechnology Center, National Science

and Technology Development Agency, Pathum Thani 12120, Thailand

* E-mail: [email protected]; Fax: +66 2354 7159; Tel. +66 2201 5770;

ABSTRACT

Microneedle arrays have been developed to deliver various types of biomolecules, including vaccine, into the skin. It has been reported that the skin is a potential target of vaccines due to existence of immune cells with high density. Microneedle arrays are capable of delivering those molecules without stimulating pain receptors and reaching or damaging blood vessels that lie beneath. These microneedles are usually designed with a wide range of geometrical shapes. Recently, a group of researchers had developed a three-dimensional finite element model describing microneedle‐mediated vaccine delivery. The model describes the diffusion and the kinetics of delivered antigens via microneedle array. However, some important aspects of microneedle, e.g. shape of microneedles, have not yet been investigated. In this work, we adapted the established finite element model to investigate influences of microneedle shape on microneedle‐mediated vaccine delivery. The immune response is assumed to depend on the number of activated immune cells after antigens are internalized into immune cells. Moreover, the roles of varying antigen diffusion coefficient within the skin were also investigated. We found that both the microneedle shape and the antigen diffusion rate affects to the efficiency of immune cell activation. The model also shows the important of these parameters in enhancing the immune response of microneedle‐mediated vaccine delivery into skin.

Keywords: Finite element model, Microneedle, Vaccine delivery

1. INTRODUCTION

Infectious diseases have been one of the greatest threats of mankind since ancient time. Impacts of infectious diseases are not only limited to health of individuals but also on economic systems [1]. In 1796, the impacts of infectious diseases on human society started to decline since the first vaccine was introduced by Edward Jenne [2, 3]. The first vaccine was employed to prevent smallpox, a disease caused by Variola virus [2]. After the global campaign of surveillance and vaccination by the World Health Assembly (WHA), smallpox was declared eradicated in 1980 [4]. The eradication of smallpox is an important evidence indicating the benefits of vaccines.

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Vaccines are traditionally delivered into muscle layers that lie underneath skin using a hypodermic needle. Although the hypodermic needles have been widely used, they are associated with needle-based injuries, spread of blood-borne diseases, pain, and phobias. In addition, the hypodermic needles are not appropriate for self-administration without enough training on administration technique and disposal procedure [5]. In addition, there have been evidences showing that the skin may be a more potential target of the vaccines. This is because a lot of immune cells, called the Antigen Presenting Cells (APCs), which response to the antigens in the vaccines are presented in the skin [6]. These APCs in the skin layers act as the helpers to create specific immune to the antigens. These evidences suggest that delivering some vaccines into the skin might be better than delivering it into the muscle layers. However, it is very difficult to deliver vaccines into the skin using the traditional hypodermic needles.

Recently, an alternative method of delivering a vaccine into the skin using microneedles was introduced [7, 8]. Microneedles are tools developed to bypass the outermost layer of the skin, which is the main barrier in the dermal vaccination and the transdermal drug delivery [9, 10]. By using micron-sized needles to pierce through the outermost layer, microneedles allow some molecules to transport directly into the inner skin layer without stimulating the pain receptors and the blood vessels. The sizes of the microneedles are much smaller than the size of hypodermic needles. Consequently, they also cause almost painless and less damage at the applied skin. Microneedle arrays are also appropriate for self-administration due to the fact that it is easy to be applied and disposed.

Computational modelling is one of the methods to assist microneedle designs. Recently, Römgens et al. developed a finite element model describing the dermal vaccination and predicted the optimal geometry of the microneedles [11]. The model described the diffusion and the kinetics of antigens delivered via microneedles. Although many aspects of the microneedle design had been explored, some important aspects such as shape of microneedles and antigen diffusion rate have not yet been investigated. In this work, we therefore modify their model to investigate roles of microneedles shape and antigen diffusion rate in the microneedle-assisted vaccine delivery.

The arrays of micron-size needles (microneedles) are designed to penetrate the outermost layer of the skin named “stratum corneum”. This outermost layer is the main barrier to prevent the molecules having the molecular weight higher than 500 Da (g/mol) to penetrate into the skin [10]. Application of microneedles to skin surface can mechanically perforate the stratum corneum and create pathway to deliver molecules into the inner layers of the skin. Microneedles research started when Henry et al. proposed the idea of using microneedle to enhance drug delivery across skin in 1998 [7]. Since then, there have been many researches investigating microneedles and their applications.

In this work, we focus on a coated type of microneedles. Coated microneedles contain drug molecules inside the thin coated layer around the microneedles. The contained drug molecules are released subdermally following application of the microneedle array. The benefits of microneedles array over hypodermic needles are that they cause significant less pain after penetration. A study showed that a microneedles pain score is 5% - 40% of the hypodermic needle. The microneedle length is the strongest factor contributing to pain while the microneedle size is not significantly influence pain [12].

Vaccine delivery into skin via microneedles is an alternative way of vaccination. The skin is the largest immune organ of human body. The skin consists of 3 main layers: the stratum corneum, the epidermis and the dermis. In addition to the fact that the skin is the barrier for large molecules, the immune cells called “Antigen Presenting Cells (APCs)” can also be found in both the epidermis and dermis. APCs are a group of cells that assists the immune system by inducing specific immunity against invading antigens while maintaining tolerance to self-antigens (antigen on our cells) [13]. These cells are the main recipients of intradermal vaccines and also are the key cells in antigen-specific immune response. Consequently, the potential of using the skin as a vaccine target instead of subcutaneous tissues and muscles lied underneath is viable [14, 15].

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Computational models have been increasingly used to investigate the drug delivery via microneedles [16-19]. Nevertheless, there has been only one model developed for vaccine delivery. Römgens et al developed a model to predict the geometry of microneedle and their array for microneedle-mediated vaccine delivery [11]. The model is a finite element model consisting of partial differential equations. The model presented the efficiency of the design by the number of activated APCs. The authors assumed a quarter of a coated microneedle within the skin to represent the whole microneedle array. Their model showed that there exists an optimal distance between microneedles and an optimal microneedle length that could maximize the number of activated APCs in both the epidermis and dermis layer. On the other hand, the microneedle base radius and the release rate contribute only minimal effect on the cell activation. 2. COMPUTATIONAL DETAILS

We consider coated microneedles of length l, base radius r ,and coated thickness tc. The coated thinkness tc is measured along the direction of the base radius. The microneedle arrays are described by the distance between microneedles S. The skin consists of 2 layers, epidermis and dermis, with thickness te and td, respectively (Figure 1a.). Due to symmetry of the microneedles and their arrays, a quarter of a microneedle within a rectangular skin is assumed to represent the entire microneedle arrays. In this work, we consider the microneedle shape as: (1) conical with a radius r or (2) squared pyramid with a side length 2r.

Figure 1. Model geometry and their corresponding mesh created using fine mesh around the microneedle and the epidermis-dermis boundary (a) Overall system (b) Close up of the mesh around the conical microneedle (c) Close up of the mesh around the square pyramid microneedle.

(c)

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The antigens are passively released from the coated layer by diffusion. The antigens can bind to the cell receptors, internalize into the cells, and are taken up into the circulation system in the dermis. The model is governed by the following set of Reaction-Diffusion equations:

( )MNMN MN

CD C

t

=

(1)

( ) ( ),, , ,

ECM e

e ECM e a tot e rec cells ECM e d rec

CD C k R C C C k C

t

= − − − +

(2)

( ) ( ),, , , , ECM d

d ECM d a tot d rec cells ECM e d rec c ECM d

CD C k R C C C k C k C

t

= − − − + −

. (3)

( ), , ( ) reca tot e rec cells ECM i d int rec

Ck R C C C k k C

t

= − − − +

(4)

, circ ECM d

Ck C

t

=

(5)

cellsint rec

Ck C

t

=

(6)

Each equation describes the rate of change of concentration over time at various location. Equations (1) – (3) describe the unbounded antigen concentration within microneedle, epidermis, and dermis, respectively. Equation (4) describes concentration of antigen bounded with receptors. Equation (5) describes the antigen concentration taken up by the microcirculation system and equation (6) describes the antigen concentration taken up by cells. The migration of antigens is described by the diffusion parts of the partial differential equations and the antigen interactions within the skin are described by the reaction parts. The model assumes that the receptors could not be recycled, and each receptor can facilitate the uptake of a single antigen into the cell. A more detailed description of the antigen-receptor binding dynamics and the model variables is described in [11]. Table 1 shows the default values of the parameters used in the model.

The model was implemented in COMSOL Multiphysics, a general-propose finite element software. The model geometry was first constructed, then the mesh of tetrahedral elements was created with fine mesh around the microneedle and the epidermis-dermis boundary (Figure 1b). We use COMSOL Multiphysics to solve the model equations (1) – (6). and then export the solution for calculating the efficiency of the immune response in MATLAB. The efficiency of the immune response is determined by the number of activated cells inside the skin. Following [11], the activation of cells is assumed to depend on a level of saturation, L, which is defined as the ratio of the antigen concentration taken up by cells Ccells and the total receptors concentration, Rtot. In this study, we assumed that if the level of saturation within a certain volume exceeds 0.9, every cell within the volume is considered as the activated cells. The number of the activated APCs is assumed to be 10% of the activated cells population (the cell density is 6.2 × 104 for the dermis and 1.3 × 106 for the epidermis) [11]. The equation for calculating the number of the activated APCs is shown in Figure 2.

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Table 1. The default values of the parameters used in the model [11]. (Left) Skin kinetics parameters. (Right) Physical parameters.

Initially, antigens exist only in the

coated layer of microneedle while the other concentrations are zero. The initial antigen concentration is calculated in dose per microneedle dMN . During simulation, the antigen concentration at the bottom plane of the rectangular skin is kept at zero (sink boundary condition). Side and top planes of the rectangular skin are impermeable for antigen (zero-flux boundary condition). The model runs until the steady state is reached, when the change of unbound antigen concentration is lower than 1 × 10-15

μmol/s. The important steps of the computer simulations are summarized in Figure 2.

Figure 2. The flow chart showing the important modelling steps.

Parameters Values

Dose per microneedle (dMN) 5.2 × 10-8

μmol

Initial receptors concentration within epidermis (Rtot,e)

2.2 × 10-7 μmol mm-3

Initial receptors concentration within dermis (Rtot,d)

1.0 × 10-8 μmol mm-3

Association rate of binding (ka) 1 × 105

M-1s-1

Dissociation rate of binding (kd) 1 × 10-3 s-1

Rate of internalization (kint) 1 × 10-3 s-1

Rate of uptake into the circulation (kc)

1 × 10-5 s-1

Diffusion coefficient within epidermis (De)

8 × 10-6 mm2 s-1

Diffusion coefficient within dermis (Dd)

21 × 10-6 mm2 s-1

Diffusion coefficient within microneedle (DMN)

8 × 10-6 mm2 s-1

Parameters Values

Microneedle spacing (S) 1 mm

Microneedle length (l) 0.3 mm

Base radius (r) 0.0875 mm

Epidermis thickness(te) 0.2 mm

Dermis thickness (td) 1.8 mm

Coating thickness (tc) 0.02 mm

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We run the model using the two different shapes of microneedle and compared the efficiency of the microneedle in activating the immune response. The surface areas of default conical and square pyramid microneedle are 0.036 mm2 and 0.042 mm2, respectively. The microneedle length was varied from 0.075 – 1.5 mm. Figure 3a shows that although the conical microneedle has less surface area, it consistently provides higher number of activated APCs. We also found that when the microneedle length of both the conical and square pyramid microneedles increases, the number of the activated APCs in the epidermis decreases while that in the dermis increases. Since there are more available APCs in the epidermis (see Table 1), this results in the decrease in the total number of activated APCs. These results (for the conical microneedle) also agree well with the previous finding [11]. However, we found that the square pyramid microneedle with length 0.075 mm activates lower number of APCs as compare to that of length 0.3 mm, while the conical microneedle always activates lower numbers of APCs when its length increases.

Figure 3. Number of the activated APCs as a function of microneedle length lMN for (a) the whole skin, (b) the epidermis, and (c) the dermis. The two lines represent two different shapes of microneedles: conical (blue) and square pyramid (orange).

We then investigated the influence of the antigen diffusion rate on the efficiency of APCs

activation. The antigen diffusion rate was varied from 0.01 to 10 times of the default values. The diffusion rates in all regions of the model were scaled equally while the values of all other parameters remain unchanged.

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Figure 4. Number of the activated APCs as a function of the antigen diffusion rate Di, where i = MN, e, and d for (a) the whole skin, (b) the epidermis, and (c) the dermis. All regional diffusion rates are scaled equally. The saturation threshold was fixed at 0.9.

The result in Figure 4 shows that the antigen diffusion rates can largely affect to the number of activated APCs. Antigens that are either too small or too large in sizes (having too low or too high diffusion rates) are not ideal for activating APCs inside the skin layers. Too small antigens move too fast and are absorbed more by the larger lymph and blood vessels underneath the skin while too large antigens move too slow and are more taken up by the microcirculation (small-size blood vessels) in the dermis. The result shown in Figure 5 confirms that the antigens with lower diffusion rates are significantly more taken up by the microcirculation. These results suggest that the diffusion rates of antigens should be considered as an important factor when designing antigens and implementing microneedles.

Figure 5. The total number of antigens taken up by the microcirculation as a function of the antigen diffusion rates Di in the unit of times of the default values.

46


4. CONCLUSION

The existing finite element model of microneedles was adapted to investigate the influences of the microneedle shape and the antigen diffusion rates on the efficiency of APCs activation. We found that the efficiency of APCs activation is not much sensitive to the change in the microneedle shape. In contrast, the antigen diffusion rates can largely affect to the efficiency of APCs activation. Antigens having either too low or too high diffusion rates are not ideal for the microneedle-mediated skin vaccination. REFERENCES

[1] Fonkwo, P.N., EMBO reports, 2008, 9 (1S), S13-S7. [2] Riedel, S., Baylor University Medical Center Proceedings, 2005, 18 (1), 21-5. [3] Delany, I., Rappuoli, R., De Gregorio, E., EMBO Molecular Medicine, 2014, 6 (6), 708-20. [4] Mondiale de la Santé, O., Organization, W.H., Weekly Epidemiological Record= Relevé

épidémiologique hebdomadaire, 2016, 91 (20), 257-64. [5] Sahm, L.J., Moore, A.C., Human Vaccines & Immunotherapeutics, 2016, 12 (11), 2975-83. [6] Romani, N., Thurnher, M., Idoyaga, J., Steinman, R.M., Flacher, V., Immunology and cell

biology, 2010, 88 (4), 424-30. [7] Henry, S., McAllister, D.V., Allen, M.G., Prausnitz, M.R., Journal of Pharmaceutical

Sciences,1998, 87 (8), 922-5. [8] Marshall, S., Sahm, L.J., Moore, A.C., Human Vaccines & Immunotherapeutics, 2016, 12 (11),

2975-83. [9] Holbrook, K.A., Odland, G.F., Journal of Investigative Dermatology, 1974, 62 (4), 415-22. [10] Lim, D.-J., Vines, J.B., Park, H., Lee, S.-H., International Journal of Biological Macromolecules,

2018, 110 30-8. [11] Römgens, A.M., Bader, D.L., Bouwstra, J.A., Oomens, C.W.J., Computer Methods in

Biomechanics and Biomedical Engineering, 2016, 19 (15), 1599-609. [12] Gill, H.S., Denson, D.D., Burris, B.A., Prausnitz, M.R., The Clinical journal of pain, 2008, 24

(7), 585. [13] Al-Zahrani, S., Zaric, M., McCrudden, C., Scott, C., Kissenpfennig, A., Donnelly, R.F., Expert

Opinion on Drug Delivery, 2012, 9 (5), 541-50. [14] Lambert, P.H., Laurent, P.E., Vaccine, 2008, 26 (26), 3197-208. [15] Hickling, J.K., Jones, K.R., Friede, M., Zehrung, D., Chen, D., Kristensen, D., Bulletin of the

World Health Organization, 2011, 89 (3), 221-6. [16] Lv, Y., Liu, J., Gao, Y., Xu, B., Journal of Micromechanics and Microengineering, 2006, 16

(11), 2492. [17] Al-Qallaf, B., Mori, D., Olatunji, L., Das, D.B., Cui, Z., International Journal of Chemical

Reactor Engineering, 2009, 7 (1). [18] Al‐Qallaf, B., Das, D.B., Davidson, A., Asia‐Pacific Journal of Chemical Engineering, 2009, 4

(6), 845-57. [19] Kim, K.S., Ita, K., Simon, L., European Journal of Pharmaceutical Sciences, 2015, 68 137-43. ACKNOWLEDGMENTS

This work is supported by the Development and Promotion of Science and Technology Talents Project (DPST), the Thailand Research Fund (TRF), and the National Nanotechnology Center (NANOTEC), Thailand.

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BIO-03

Characterization and Identifying The Positional

Binding and Ligand Interaction of Wildtype Gh10

Xylanase via Computational Techniques

Auwal Muhammad1,2, and Thana Sutthibutpong1,* 1 Theoretical and Computational Physics Group, Department of Physics, King Mongkut’s University and

Technology, Thonburi, (KMUTT) Thailand 2Department of Physics, Faculty of Science, Kano University of Science and Technology (KUST), Wudil,

Kano, Nigeria

*E-mail: [email protected]; Tel. +66 967434712

ABSTRACT

Glycoside Hydrolase (GH) family 10 enzymes are among the most common hydrolytic enzymes used to catalyze the cellulose and hemicellulose into monomeric sugars. However, lignin compounds inhibited the enzymatic reactions. In this study, interactions between a GH10 xylanase and each of three type of lignin monomers were investigated by molecular docking and molecular dynamics simulations aiming to understand the substrate (lignin monomers) positional binding and interaction networks at binding site of a GH10 xylanase. From the docking results, coniferyl (G) and sinapyl (S) alcohols showed better binding affinity of −6.2 kcal/mol and −6.0 kcal/mol respectively compared to p-coumaryl alcohol (H) with value of −5.9 kcal/mol. Important binding site residues of the GH10 xylanase starting structure were then identified. The results from the MM/PBSA analysis of the MD trajectories showed that the binding of lignin monomers was majorly contributed by the hydrophobic interactions, suggesting that hydrophobicity was the main cause for enzyme inhibition. Additional hydrogen bonding networks formed at HIS218, TYR252, TYR254, TYR256, ASN310, and LYS312 made the coniferyl alcohol the strongest inhibitor for GH10.

Keywords: Glycoside Hydrolase, Lignin monomers, Enzymes, Computational techniques, Binding.

1. INTRODUCTION

Xylanases are glycoside hydrolases (GHs) that catalyze the random endo hydrolysis of the 1,4-β-D-xylosidic linkage in xylan, a major component of plant hemicelluloses. Over the last several decades, there has been a growing interest in xylanases due to their potential application in various industrial processes, such as manufacture of animal feed, the pulp and paper industry and biofuel production [1]. Xylanases are produced by many organisms, including bacteria, actinomycetes, protozoa and fungi. These enzymes vary in primary sequences, structure folds, substrate specificities and catalytic mechanism. They have been mainly classified into glycoside hydrolase families [2]. It was reported that billions of tons of organic matter are naturally decomposed by GHs which result in the production of commercially important chemicals from lignocellulosic plant biomass [3]. One of the most abundant sources of organic materials on earth is plant biomass and considered to be a promising alternative for sustainable energy production [4]. Lignocellulosic biomass consists of mainly three natural polymers: cellulose, hemicellulose and lignin. These polymers form a recalcitrant structure that halt the enzymatic hydrolysis of the lignocellulose. This necessitate the need for an effective biomass disrupting pretreatment with a view to increase the efficiency of enzymatic hydrolysis [5]. Currently, numerous pretreatment strategies have been adopted, but most of the pretreatments were performed under extreme conditions leading to the formation of various by- products, including furan derivatives, and phenolic compounds. The phenolic compounds forms part of lignin, a component of plant cell wall, which strongly inhibit the enzymes activities and consequently affect microbial fermentation capacity [6]. Biological pretreatment is an effective method to modify and degrade the complex biomass, and

48


significantly decrease the number of enzymes needed to transform biomass into sugar. However, the inhibition of enzymatic activity in the pretreated biomass by lignin severely limits the efficiency of the whole process. In this study, computational detail of xylanase/lignin monomers at the atomic level, positional binding of the ligands and energy contribution per residues to binding has been investigated, which is of paramount importance for protein engineering.

2. THEORY AND RELATED WORKS

Family 10 Xylanase are high molecular weight xylanases that are typically composed of a catalytic domain and carbohydrate binding domain having endo-1, 4-E-xylanases (EC 3.3.1.8) as the major enzymes in this family [7]. Glycoside hydrolase family 10 (GH 10) are more conserved in protein structure and sequence, and have a higher percentage of identical and spatially equivalent residues. The family is categorized as part of clan GH-A, which is main clan of glycoside hydrolase. Enzymes belonging to clan GH-A display common (β/α)8 TIM-barrel fold structure, with extended loops creating a catalytic cleft that contains at least four to seven xylo-binding subsites [1]. The reaction mechanism of this enzymes are retaining due to double displacement mechanism, catalyzed by two glutamic acid residues acting as an acid/base pair and a nucleophile [8]. GH10 (CbXyn10C) xylanase sequence used in this study was obtained from a bacterium Caldicellulosiruptor bescii, and characterized with two glutamic acid E140/E248 [9]. Studies of xylanase enzymes from GH10 (CbXyn10C) refers to the three-dimensional structure (Figure 1).

Figure 1. Cartoon representation of three-dimensional crystal structure of GH10 (CbXyn10C)

3. COMPUTATIONAL DETAILS

All the computational work was carried out on an Intel(R) Core TM i7-6700 CPU @ 3.4 GHz × 8 processor with a memory of 8.00 GB RAM running on a Linux 64 operating system. Visual Molecular Dynamics (VMD) software [10] was used for the visualizing both protein and ligand molecules.

3.1 Ligand preparation

All the ligands were retrieved from PubChem database [11] and saved into .sdf format. These ligands were then converted into three-dimensional (3D) .pdb format with the help of Avogadro software [12].

49


3.2 Protein preparation

Glycoside Hydrolase family 10 (PDB ID: 5OFJ) [9] was retrieved from the Protein Data Bank website [13]. Crystallographic water molecules were removed from the protein prior to docking.

3.3 Molecular docking

The PDB files of receptor and the ligand were prepared using AutoDock Tools (ADT). The ligand optimization was performed using AutoDock 4.2 [14], Geister charges were added, non-polar hydrogens were merged, and saved as PDBQT file. Atom charges, solvation parameters and polar hydrogen were added to the protein receptor for docking simulation before converting to PDBQT file. For AutoDock Vina to run, the structure for both ligand and receptor has to be available in the PDBQT file. Vina also requires pre-calculated grid maps, and the grid must surround the active site of the receptor. The grid box size was set to 100, 80, and 100 Å, for x, y, and z respectively, and the grid spacing of 0.303 Å. Then, a standard configuration file for AutoDock Vina, containing the information about ligand, receptor, search area, and output information was made. The docking was then started using the configuration file. The output of AutoDock Vina consists of the theoretical binding affinity as well as two measures of how close the results are to each other (two different RMSD measures). The affinity gives some information about how strong the binding is, the smaller the number the better. The best ligand conformations with the lowest affinity value was further selected as the initial conformation for MD simulation. All the docked results were viewed with a visualization program like AutoDock Tools and VMD.

3.4 Molecular dynamics (MD) simulations

Molecular Dynamics Simulations were performed using Gromacs 5.1.2 with GROMOS 53A6 forcefields [15]. The starting conformations of each CbXyn10C/lignin monomers complex were taken from the molecular docking result of AutoDock Vina [16]. The topology file for each xylohexaose and lignin monomers (ligand) was generated using Automated Topology Builder (ATB) webserver [17] with GROMOS 53a6 forcefield and partial charges derived from semi-empirical QM calculations by MOPAC [18]. The simple point charge (SPC) model [19] was used for water molecules and Na+ or Cl−counter ion was added to neutralized the system. The box dimension created were 8 nm × 8 nm × 8 nm. Particle Mesh Ewald (PME) method with 10 Å cutoff distance was used to deal with short range interactions. P-LINC algorithm was used to deal with geometrical constraints for covalent bonding associated with hydrogen atoms so that the 2 fs timestep was used [20]. Energy minimization with the steepest descent algorithm, and 1 ns equilibration run were performed for each simulation. During an equilibration, each structure was heated from 100 K to 300 K. Afterward, 40 ns production MD run in NPT ensemble with constant temperature of 300 K and 1atm pressure were performed.

Having completed the simulation, all the trajectory snapshots were fitted to their starting structures by using least square fitting methods from GROMACS module ‘trjconv’, so that translational and rotational motion would be removed and only internal changes could be observed in the fitted trajectory.

3.5 Molecular mechanics poisson boltzmann surface area (MM/PBSA)

Relative binding energies of ligands were calculated using molecular mechanics poisson Boltzmann surface area (MM/PBSA) with solvent accessibility techniques implemented in g_mmpbsa package [21]. Ligand binding free energies (∆Gbind) are calculated as the sum of molecular mechanics and solvation energies given by the equation. ∆Gbind = ∆H − T∆S ≈ ∆EMM + ∆Gsolv − T∆S (1)

Where ∆EMM is the vacuum phase molecular mechanics energy, ∆Gsolv is the desolvation free energy, ∆S represent entropy, and T is the temperature in kelvin. In this work, entropy calculation was ignored due to expensive computational cost, algorithms are computationally very demanding and low prediction accuracy[22][23]. Implicit solvation energies were computed using Adaptive Poisson

50


Boltzmann Solver (APBS) implemented in g_mmpbsa. The dielectric constant of protein and solvent were set to 4 and 80 respectively, and ion concentration was set to 0.0032 equivalent to 1 Cl− counterion. Non-polar solvation energy was calculated using Solvent Accessible Surface Area (SASA). Surface tension of solvent and SASA energy constant were set to 0.0226778 kJ/(mol Å2) and 3.84982 kJ/mol respectively. MM/PBSA calculations were performed for snapshots taken every 0.1 ns from the last 20 ns of CbXyn10C - ligands complex MD trajectories. Energy decomposition per residue was also computed to see the contribution of each amino acid residue to total binding free energy.


4.1 Molecular docking results

Docking of xylohexaose and lignin monomers were performed to estimate binding affinity and position of ligand within binding pocket. Docking energy of known substrate (xylohexaose) was higher than that of lignin monomers (coniferyl, sinapyl and p-coumaryl alcohols). However, with respect to lignin monomers, binding energy of coniferyl and sinapyl alcohols were higher than p-coumaryl alcohol as shown in Table 1. The results showed that, in the binding cleft of CbXyn10C, hydrogen bond and hydrophobic interactions with xylohexaose are observed as shown in Figure 2 (a). Hydrophobic residues participated in the interactions are PRO23, ALA24, GLU46, TRP91, GLU140, TYR185, HIS218, TRP305, and PHE309. Seven residues LYS25, ASN47, GLU48, HIS92, SER93, GLN94, and GLN216 formed hydrogen bonding with xylohexaose respectively. Considering lignin monomers, coniferyl alcohol interacted with hydrophobic residues of HIS218, ILE219, TRP223, SER252, TYR254, TYR256, GLY257 and ASN310. Four hydrogen bonds were observed with ASN220, ASN255, SER258 and LYS312 residues (Figure 2b).

Table 1. Binding free energy obtained using docking and 2D structure of ligands used

Ligand types

2D Structure of the ligand

Binding

energy

(kcal/mol)

Xylohexaose

−9.5

Coniferyl alcohol

−6.2

Sinapyl alcohol

−6.0

Pcoumaryl alcohol

−5.9

Docking of sinapyl alcohol showed three hydrogen bonds with SER93, HIS218 and TRP297. Also, the hydrophobic residues of GLU46, ASN47, LYS50, GLN94, GLN216, GLU248, and TRP305 interacted with the ligand as given in the Figure 2(c).

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Figure 2. Schematic diagram produced by LIGPOLT 2.1 [24] showing interactions between the starting structure of CbXyn10C and (a) xylohexaose (b) coniferyl alcohol (c) sinapyl alcohol (d) p-coumaryl alcohol

CbXyn10C – p-coumaryl complex formed hydrogen bond with GLU46, ASN47, and HIS218, while hydrophobic residues observed are GLN216, GLU248, TRP297, and TRP305. The 2D structure of the interactions is presented in Figure 2(d).

4.2 Molecular dynamics (MD) results

The starting conformation of each protein – ligand complex was taken from molecular docking result of AutoDock Vina, to perform molecular dynamics simulations. The purpose is to establish a reliable mechanism for illustrating protein – ligand interactions. The dynamics properties of complexes have been compared in terms of binding energy, energy decomposition per residue, and hydrogen bond interactions within the binding pocket. Figure 3 gave representation of MD results and residues participated in the binding. It is clearly observed that some amino acid residues participated in binding for xylohexaose and coniferyl alcohol are similar to that obtained in molecular docking results. GLU140, TYR185, HIS218, GLN216 and PHE309 have appeared in both molecular docking and MD results of xylohexaose. Additional amino acid residues were observed in MD results which are PRO96, GLU188, ILE219, TRP223, PRO224, GLU248, ASP250, and TRP256, this might be due to dynamic mechanism of the CbXyn10C-xylohexaose complex. For coniferyl alcohol, TRP223, TYR254, TYR256, HIS218, LEU306, and LYS312 are similar residues contributed to both molecular docking and MD results. However, additional amino acid residues of GLU188, ILE219, ASN220, ASP250, TRP305, PHE309 were seen in the MD results.

52


(a) CbXyn10C-xylohexaose complex

(b) CbXyn10C-coniferyl alcohol complex

(c) CbXyn10C-sinapyl alcohol complex

(c) CbXyn10C-p-coumaryl alcohol complex

Figure 3. Crystal structures of CbXyn10C with four different types of ligand complexes obtained from MD results using VMD

53


Examining CbXyn10C-sinapyl alcohol and CbXyn10C-p-coumaryl alcohol complexes, there are no any similar amino acid residues observed as compared to molecular docking results. This is because the ligand flops out at the initial stage, and move in within the active site with the positional change of the ligands. In CbXyn10C-sinapyl alcohol, the amino acid residues contributed in the binding were ASP302, ARG307, SER308, ASN313, and GLU321 respectively, while in the case of CbXyn10C-p-coumaryl alcohol, the amino acid residues interacted with the ligand were LYS25, CYS26, ASP32, LYS300 and TYR303 respectively.

Calculations of MM/PBSA were performed for snapshots taken every 0.1 ns from the last 20 ns of CbXyn10C - xylohexaose and all three CbXyn10C - lignin monomers complex MD trajectories. Binding energy values of individual components from van der Waals interactions, electrostatics interactions, polar solvation energy, SASA energy, and total binding free energy in kJ/mol are listed in Table 2. The results showed that xylohexaose with binding free energy values of -208.406 kcal/mol have best binding free energy compared to others. However, lignin monomers such as coniferyl possessed highest negative binding free energy values of -101.424 kcal/mol followed by sinapyl with value of -76.377 kcal/mol and p-coumaryl displayed the value of -44.914 kcal/mol respectively.

Table 2. Binding energy values and individual energy component calculated with MM/PBSA

Ligand types

Van der

Waals

Energy

(kcal/mol)

Electrostatics

Energy

(kcal/mol)

Polar

Solvation

Energy

(kcal/mol)

SASA

Energy

(kcal/mol)

Total

Binding

Energy

(kcal/mol)

Xylohexaose -266.738 -103.480 -103.480 -103.480 -103.480

Coniferyl -117.103 -52.796 -52.796 -52.796 -52.796

Sinapyl -74.016 -53.398 -53.398 -53.398 -53.398

Pcoumaryl -43.597 -70.724 -70.724 -70.724 -70.724

Also, van der waals energy, electrostatic energy and SASA energy (non-polar solvation) were

negatively contributed to the total binding energy, while polar solvation energy positively contributes to the total free binding energy in all the complexes.

Examining the binding free energy of the individual components, we observed that, for CbXyn10C-xylohexaose, CbXyn10C-coniferyl and CbXyn10C-sinapyl complexes, calculated binding energy are dominated by van der waals energy, while for CbXyn10C-p-coumaryl complex, total binding energy are dominated by electrostatic energy. The nonpolar energy for all complexes contributes relatively less as compared to total binding free energy.

CbXyn10C - xylohexaose MM/PBSA calculations gave best binding free energy due to the higher contribution of amino acid residues participated in hydrogen bond formation. The highest negative values observed in van der waals energy is an indication of more contribution from hydrophobic residues.

In terms of lignin monomers, CbXyn10C/coniferyl MM/PBSA calculations shows highest negative values of van der waals energy which accounts for approximately 87% of the total binding free energy. Highest negative value of van der waals energy represent massive hydrophobic interactions within the binding cleft.

54


Per residue energy contribution of known substrate (xylohexaose) suggested that PRO96, GLU140, TYR185, GLU188, GLN216, HIS218, ILE219, TRP223, PRO224, GLU248, ASP250, TYR256, and PHE309 were the major energy contributors (Figure 4a). All Glutamic (GLU) acid showed positive energy values, an indication of non-favorable interactions. CbXyn10C-coniferyl indicated that GLU188, HIS218, ILE219, ASN220, TRP223, PRO224, ASP250, TYR254, TYR256, TRP305, LEU306, PHE309, and TYR256 were the major amino acid residues interacting in the binding pocket (Figure 4b). GLU188, ASP250 and LYS312 showed positive energy suggesting unfavorable interactions.

In sinapyl binding with CnXyn10C, amino acid residues ASP302, ARG307, SER308, and ASN313 showed favorable energy contributions while GLU321 displayed unfavorable interactions. In case of CnXyn10C – p-coumaryl, per residue energies decomposition were observed in LYS25, CYS26, ASP32, LYS300, and TYR303. However, one amino acid residue LYS25 showed positive energy value.

Figure 4. Per residues energy contribution of (a) Xylohexaose (b) Coniferyl alcohol (c) Sinapyl alcohol and (d) Pcoumaryl alcohol

Comparison of per energy contribution between xylohexaose and coniferyl alcohol, suggested that, the amino acid residues GLU188, HIS218, ILE219, TRP223, PRO224, ASP250, and PHE309 were actively participated in the binding cleft of both. However, ASN220, TYR254, TRP305, LEU316, and LYS312 contributed in the binding with coniferyl alcohol. Additionally, amino acid residue TYR256 contribute significantly with the lowest energy of -8.935 kcal/mol (Figure 5), resulting from hydrogen bonds formation with the coniferyl alcohol, which might be reason for the strongest inhibition compared to sinapyl and p-coumaryl alcohols respectively. One way to reduce the effect of coniferyl inhibition, is to engineer the enzyme by considering these residues revealed in this study.

55


Figure 5. Per residues decomposition free energies of xylohexaose(blue), coniferyl (red), sinapyl (yellow), and p-coumaryl (green) alcohols

5. CONCLUSION

Xylohexaose and lignin monomers were subjected to molecular docking and molecular dynamics simulation with GH10 (CbXyn10C) xylanase to identify positional binding of substrate and per residues energy contribution which revealed inhibition effects in term of binding energy. Among all, known substrate displayed highest negative binding energy. In lignin monomers, coniferyl alcohol showed best binding affinity as obtained in molecular docking. The starting conformations of each protein – ligand was taken from molecular docking results subjected to molecular dynamics simulations and MM/PBSA analysis. MM/PBSA results showed that, known substrate and coniferyl alcohol fit well into the active pocket with better binding energy. These results also revealed that, binding of these two ligands to CbXyn10C was ruled by hydrophobic interactions. Based of comparison made, some of the key residues participated in CbXyn10C – coniferyl interactions were identified which can serves as a future target for protein engineers to design lignin resistance xylanase.

REFERENCES

[1] Zhang, Y., Ann, J., Yang, G., Zhang, X., Xie, Y., Chen, L., and Feng, Y., “Short Communication Structure features of GH10 xylanase from Caldicellulosiruptor bescii : implication for its thermophilic adaption and substrate binding preference,” Acta Biochim Biophys Sin, 2016, 48

(10), pp. 948–957. [2] An, J., Xie, Y., Zhang, Y., Tian, D., Wang, S., Yang, G.,and Feng, Y., “Characterization of a

thermostable, specific GH10 xylanase from Caldicellulosiruptor bescii with high catalytic activity,” J. Mol. Catal. B Enzym., 2015, 117, 13 - 20.

[3] Wan, Q., Parks, JM., Hanson, BL., Fisher, SZ., Ostermann, A., Schrader, TE., Graham, DE., Coates, L., Langan, P., and Kovalevsky, A., “Direct determination of protonation states and visualization of hydrogen bonding in a glycoside hydrolase with neutron crystallography,” Proc.

Natl. Acad. Sci., 2015, 112(40), 12384 - 12389. [4] Davidi, L., Morais, S., Artzi, L., Knop, D., Hadar, Y., Arfi, Y., and Bayer, EA., “Toward

combined delignification and saccharification of wheat straw by a laccase-containing designer cellulosome,” Proc. Natl. Acad. Sci., 2016, 113 (39), 10854 - 10859.

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[5] Oliva-Taravilla, A., Tomás-Pejó, E., Demuez, M., González-Fernández, C., and Ballesteros, M., “Phenols and lignin: Key players in reducing enzymatic hydrolysis yields of steam-pretreated biomass in presence of laccase,” J. Biotechnol., 2016, 218, 94 - 101.

[6] González-Bautista, E., Carlos Santana-Morales, J., Javier Rios-Franquez, F., Mario Poggi-Varaldo, H., Ramos-Valdivia, AC., Cristiani-Urbina, E., and Ponce-Nayola, T., “Phenolic compounds inhibit cellulase and xylanase activities of Cellulomonas flavigena PR-22 during saccharification of sugarcane bagasse,” Fuel, 2017, 196, 32 - 35.

[7] S. Anbarasan, “Thermostable beta-glycosidases as processing enzymes in biorefineries.” Aalto University; Aalto-yliopisto, 2016, p. 107.

[8] Kaneko, S., Ichinose, H., Fujimoto, Z., Iwamatsu, S., Kuno, A., and Hasegawa, T., “Substrate Recognition of a Family 10 Xylanase from Streptomyces olivaceoviridis E-86: A Study by Site-directed Mutagenesis to Make an Hindrance around the Entrance toward the Substrate-binding Cleft,” J. Appl. Glycosci., 2011, 56 (3), 173 - 179.

[9] Chu, Y., Tu, T., Penttinen, L., Xue, X., Wang, X., Yi, Z.,Gong, L., Rouvinen, J., Luo, H., Hakulinen, N., Yao, B., Su, X., “Insights into the roles of non-catalytic residues in the active site of a GH10 xylanase with activity on cellulose,” J. Biol. Chem., 2017, 292 (47).

[10] Humphrey, W., Dalke, A., and Schulten, K., “VMD: Visual molecular dynamics,” J. Mol.

Graph., 1996, 14 (1), 33 - 38. [11] Kim, S., Thiessen, PA., Bolton, EE., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S.,

Shoemaker, BA., Wang, J., Yu, B., Zhang, J., and Bryant, SH., “PubChem substance and compound databases,” Nucleic Acids Res., 2016, 44 (D1), D1202 - 13.

[12] Hanwell, MD., Curtis, DE., Lonie, DC., Vandermeerschd, T., Zurek, E., and Hutchison, GR., “Avogadro: An advanced semantic chemical editor, visualization, and analysis platform,” J.

Cheminform., 2012, 4, 17. [13] Protein Data Bank, “RCSB PDB: Homepage,” RCSB PDB, 2019. [14] Huey, R., and Morris, G., “Using AutoDock 4 with AutoDockTools: A Tutorial,” Scripps Res.

Institute, USA, 2008. [15] Schmid, N., Eichenberger, AP., Choutko, A., Riniker, S., Winger, M., Mark, AE., and Van

Gunsteren, WF., “Definition and testing of the GROMOS force-field versions 54A7 and 54B7,” Eur. Biophys. J., 2011, 40 (7), 843 - 856.

[16] Trott, O., and Olson, AJ., “Software news and update AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading,” J.

Comput. Chem., 2010, 31 (2), 455 - 461. [17] Stroet, M., Caron, B., Visscher, KM., Geerke, DP., Malde, AK., and Mark, AE., “Automated

Topology Builder Version 3.0: Prediction of Solvation Free Enthalpies in Water and Hexane,” J.

Chem. Theory Comput., 2018, 14 (11), 5834 - 5845. [18] Dewar, MJS., Zoebisch, EG., Healy, EF., and Stewart, JJP., “Development and use of quantum

mechanical molecular models. 76. AM1: a new general purpose quantum mechanical molecular model,” J. Am. Chem. Soc., 1985, 107 (13), 3902 - 3909.

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[22] Martis, EAF., and Coutinho, EC., “Free Energy-Based Methods to Understand Drug Resistance Mutations,” in Structural Bioinformatics: Applications in Preclinical Drug Discovery Process, C. G. Mohan, Ed. Cham: Springer International Publishing, 2019, 1–24.

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[24] Laskowski, RA., and Swindells, MB., “LigPlot+: Multiple ligand-protein interaction diagrams for drug discovery,” J. Chem. Inf. Model., 2011, 51 (10), 2778 - 2786.

58


BIO-04

Classification and Variable Selection in Large p

Small n with Imbalanced Data Problems Using

Regularized AUC

Tanawat Horsirimanon, and Waranyu Wongseree*

Department of Electrical and Computer Engineering, Faculty of Engineering, King Mongkut's

University of Technology North Bangkok, Bangkok, Thailand

*E-mail: [email protected]; Fax: +66 2 585 7530; Tel.+66 25552000 8410;

ABSTRACT

Classification and variable selection when the number of variables is much larger than the number of samples has become increasingly frequent and important in bioinformatics. Regularization methods successfully cope with this challenge with limitation in imbalanced classification problems. The area under the receiver operating characteristic curve (AUC) is a measure to evaluate and compare the performance of classifiers. Maximizing AUC in the process of classifier construction can be more appropriate solution which is however an NP-hard problem due to neither continuous nor concave function of AUC. Many surrogate functions have been proposed to approximate AUC function. Nonetheless, there is no benchmark experiment to proof that the performance of AUC based classifier is better than accuracy based classifier for an application of high dimensional imbalanced class data. In this study, we propose a surrogate loss function as cross-entropy function based on pairwise comparisons between positive samples and negative samples in order to incorporate with lasso logistic regression. The training data is used to select optimal regularization parameter via cross-validation and independent testing data is used to compare performance of the proposed method with lasso logistic regression. The simulation results indicate that the performance of proposed method is much better than lasso logistic regression in case of skewed class distribution.

Keywords: AUC, Lasso, Imbalanced Data, Variable Selection 1. INTRODUCTION

Several classification problems in bioinformatics have involved dealing with a small sample size of high dimensional data [1-2]. Therefore, an efficient feature selection has been required to improve classifier performance and reduce computational intensiveness. Penalized variable selection and classification methods such as lasso [3-5] and elastic net [6] successfully cope with this challenge with limitation in imbalanced class scenarios [7].

Area under the receiver operating characteristic curve (AUC) is a criteria to evaluate and compare performance of classifiers especially in imbalance class problem. There are a statistical study to find the relationship between AUC and an accuracy. It found that high classification accuracy may not lead to high AUC in the imbalanced class scenario, because an accuracy of classifier depends on the number of correct classification results while AUC depends on not only the number of correctly classify but also correct rank of each sample. In addition, there are theoretical and empirical proofs that the AUC criteria have higher discrimanant ability than an accuracy in both balance and imbalanced problem. Furthermore, high AUC classifier tends to have high accuracy [8]. So, constructing a classifier based on a merit of AUC can be more appropriate solution for this content of the problem.

Replacement strategies of AUC maximization and classification error minimization in the process of classifier construction however deals with an NP-hard problem, since the AUC is mathematically interpreted as the concave cost function in optimization process, unlike traditional continuous function of classification error. Many surrogate functions have been proposed to approximate AUC function. Ma et al. [9] proposed logistic function to approximate indicator function of AUC but it still has numerical problems. Yu and Park [10] proposed linear regression to maximize

59


AUC over the multivariate normal distribution assumption. However, the assumption about multivariate normal distribution are rarely in bioinformatics data. Recently, Zhao et al. [11] proposed negative log likelihood and hinge loss function as a surrogate function and showed that the performance of the proposed method have higher AUC than the logistic regression.

There is no benchmark experiment to proof that the performance of AUC based classifier is better than accuracy based classifier when apply to high dimensional imbalanced class data. In this study, we have proposed cross-entropy function as a surrogate loss function based on pairwise comparisons between positive samples and negative samples in order to incorporate with lasso logistics regression.

2. METHOD

For constructing a linear classifier, maximizing the AUC is equivalent to maximize number of pairwise comparison between positive and negative samples that positive score T

i

+w x is higher than

negative score Tj

−w x .

T T

1 1

1AUC( ) ( )

m n

i ji jI

n n

+ −+ − = =

= w w x w x

where i

+x and j

−x are covariates of positive and negative samples, w is coefficients of a linear

classifier, n+ is number of positive sample, n

− is a number of negative sample and ( )I is an indicator function which returns 1 if the argument is true and 0 otherwise. The NP-hard problem of AUC maximization can be overcome by approximating an indicator function as a smooth function. We propose to use log transformation after applying logistic function to approximate the indicator function. Then, surrogate function of AUC is

1 1 ( )

1 1( ) log

1 expT

i j

m n

i jf

n n+ −+ − = = − −

=+

w x xw

Solution of maximizing ( )f w is equivalent to minimize cross entropy error of logistic regression

with i j

+ −−x x as classification features and applying the positive class for all samples. Then, the

proposed surrogate function can be incorporated with regularization in order to identify a subset of the most relevant variables. Both the generalization performance and feature selection depend on the value of regularization parameter. There are several efficient algorithms regarding a computational benefit to implement regularized logistic regression for high-dimensional data. In this paper, we use glmnet [12] to solve the problem. Pseudo code of the propose method show in figure 1.

60


Algorithm Pairwise Logistic Regression ( , )X y

// Input: X is an input matrix, y is an output vector. // Output w is a coefficient vector.

, 0n n+ −

for 1i to length( y ) do

if 1iy = then

[ ,:] [ ,:]n i+ + X X , 1n n

+ + +

else

[ ,:] [ ,:]n i− − X X , 1n n

− − +

0k

for 1i to n+ do

for 1j to n− do

[ ,:] [ ,:] [ ,:]k i j+ − −Z X X , [ ] 1k o , 1k k +

w lasso logistic regression ( ,Z o )

return w

Figure 1. Pseudo code of the propose method

3. Simulation Study

To compare the performance of the pairwise logistic regression to logistic regression under various setting, we simulate data following a setting similar to Zhao et al. [11]. Here are details of the four scenarios.

Scenario 1: multivariate normal distribution

1 1( , )i N + X and 2 2( , )j N − X

where i

+X and j

−X are p-dimensional random vectors. Scenario 2: mixture of multivariate normal distribution

1 1 3 30.8 ( , ) 0.2 ( , )i N N + + X and

2 2 3 30.8 ( , ) 0.2 ( , )j N N − + X

Scenario 3: multivariate log normal distribution

1 1log( ) ( , )i N + X and 2 2log( ) ( , )j N − X

Scenario 4: mixture of multivariate log normal distribution

1 1 3 3log( ) 0.8 ( , ) 0.2 ( , )i N N + + X and

2 2 3 3log( ) 0.8 ( , ) 0.2 ( , )j N N − + X

where T1 (1,0,0,...,0) = , T

2 (0,1,0,...,0) = , T3 (1,1,1,...,0) = ,

61


1 2

1 1/ 3 1/ 3

1/ 3 1 1/ 3

1/ 3 1/ 3 1

= =

, 3 5I =

For all 4 scenarios, each training data set contains 100 samples and 2,000 variables. Five levels of the class distribution ratio are shown as in table 1. Under each scenario, we simulate 50 sets of training and testing data set. The lasso logistic regression and the proposed method with tuning parameters were fitted to the training dataset through 10-fold cross-validation. The most parsimonious model within one standard error of the best model was chosen. For the chosen tuning parameters, the estimated coefficients based on the whole training dataset were then compared in terms of true positive rate and false negative rate. The prediction AUC of model was evaluated on the test dataset. The flow chart of experimental design in this study show in Figure 2. Table 1. Parameter setting of simulation scenario

Parameter Value

Number of variables 2,000 Number of training samples 100 Number of testing samples 100,000 Class distribution ratio 5:5, 4:6, 3:7, 2:6, 1:9

Figure 2. flow chart of experimental design in this study

62



We measured the prediction AUC by averaging the 50 test AUCs. Figure 3 shows boxplot of testing AUC over various choices of scenarios and class distribution ratios. The AUCs of propose method and lasso logistic regression were different in all simulations in skew class distribution.

Table 2 reports the average value of true positive rate and false positive rate over 50 simulations. The true positive rate (TPR) and the false positive rate (FPR) were used to evaluate performance of variable selection methods. These results show that the propose method outperformed the lasso logistic regression in term of TPR, especially when class distribution is highly skew. Nevertheless the proposed method provided slightly larger FPR than the lasso logistic regression. 5. CONCLUSION

This study proposed using pairwise logistic regression to identify significant variable in large p small n with imbalanced data problems. Although this problem is common in bioinformatics, existing methods are proven to have limited success. The advantages of the propose method were demonstrated through simulations. The proposed method outperformed the lasso logistic regression in terms of the significant variable detectability.

Although the pairwise logistic regression offers an alternative choice for identifying significant variable in imbalanced data problem, it still has practical limitations. This method assumes that each variable is independent. However, there is a complex interaction between variable in bioinformatics data [13]. An investigation into the use of complex model will be considered for future work. Table 2. True positive rate and false positive rate of variable selection methods

Scenario Ratio True Positive Rate False Positive Rate

AUC based

model

Accuracy

based model

AUC based

model

Accuracy

based model

1 5:5 99.00 99.00 0.65 0.45 4:6 98.00 97.00 0.63 0.36 3:7 97.00 98.00 0.55 0.41 2:8 95.00 89.00 0.67 0.38 1:9 58.00 48.00 0.38 0.16

2 5:5 76.00 63.00 0.50 0.26 4:6 79.00 64.00 0.45 0.22 3:7 69.00 53.00 0.51 0.24 2:8 55.00 40.00 0.43 0.12 1:9 17.00 10.00 0.21 0.07

3 5:5 100.00 98.00 0.83 0.40 4:6 99.00 97.00 0.86 0.47 3:7 96.00 90.00 0.74 0.45 2:8 91.00 70.00 0.67 0.34 1:9 78.00 37.00 0.44 0.14

4 5:5 68.00 40.00 0.53 0.21 4:6 80.00 58.00 0.92 0.39 3:7 76.00 40.00 0.77 0.28 2:8 54.00 16.00 0.50 0.12 1:9 38.00 8.00 0.34 0.04

63


Logistic Regression

Pairwise Logistic Regression

(a)

(b)

(c)

(d)

Figure 3. The boxplots of AUC in testing sets over 50 runs for simulation cases of (a) normal (b) normal mixture (c) log normal and (d) log normal mixture data with difference class distribution ratio.

64


REFERENCES

[1] Dudoit, S., Fridlyand, J., and Speed, T., Journal of the American Statistical Association, 2002, 97(457), 77-87.

[2] Zhu, J. and Hastie, T., Biostatistics, 2004, 5(3), 427–43. [3] Tibshirani, R., Journal of the Royal Statistical Society: Series B (Statistical Methodology), 1996,

58(1), 267–288. [4] Guo, Y., Hastie, T., and Tibshirani, R., Biostatistics, 2006, 8(1), 86–100. [5] Tibshirani, R. , Hastie, T. , Narasimhan, B. , et al. , Proceedings of the National Academy of

Sciences of the United States of America, 2002, 99(10), 6567–72. [6] Zou, H. and Hastie, T. , Journal of the Royal Statistical Society: Series B (Statistical

Methodology), 2005, 67(2), 301–20. [7] Ma, S., and Huang, J., Briefings in Bioinformatics, 2008, 9(5), 392-403. [8] Huang, J. , and Ling, X. , C. , IEEE Transactions on Knowledge and Data Engineering, 2005,

17(3), 299-310. [9] Ma, S., Xiao, S., and Huang, J., BMC Bioinformatics, 2006, 7(253). [10] Yu, W., and Park, T., BMC Genomics, 2014, 15(s10). [11] Zhao, X. G., Dai, W., Li, Y., and Tian, L., Bioinformatics, 2011, 27(21), 3050-5. [12] Friedman, J., Hastie, T. and Tibshirani, R., Journal of Statistical Software, 2008, 33(1), 1-22. [13] Park, M. Y. and Hastie, T., Biostatistics, 2008, 9, 30-50. ACKNOWLEDGMENTS

This work was partially supported by the Department of Electrical and Computer Engineering, Faculty of Engineering, KMUTNB.

65


CSE-01 Implementation IoT Rehabilitation Tracking

System for Trigger Finger Patients Numtip Trakulmaykee, Panupong Kreanual, Khunanya Suksawat, and Chidchanok Choksuchat*

Information and Communication Technology Programme, Prince of Songkla University,

Hat Yai, Songkhla, Thailand

*E-mail: [email protected]; Fax: +66 74 288 697; Tel. +66 74 288 674

ABSTRACT

Currently, many patients are suffered with Trigger Finger (TF) which is a symptom that causes stiffness, and a locking sensation when they bend and straighten their fingers. It really causes pain to patients. The early stage of trigger finger has not to do a surgery operation. Patient can heal by physical therapy method, slowly repeated pressing-releasing (paper-rock) manner of palm. The problems of patients and physicians who concerned the matter that cannot follow up the results of the patients exercise in the proper manner and appropriate time. Our team related to the Traditional Thai Physician who realized to this problem, hence the solution can be solved by ICT process. We developed the tracking system as a web application that is used for monitoring the patients. We focus on displaying the patient's data, which is sent from healthcare Internet-of-Things (IoT) physical therapy device. Making a convenience to Traditional Thai Medicine (TTMed) staff to track the test results and patient’s history. The main purpose of this web application that use to track the symptoms of patients especially under taking care by therapists to efficiency treatment. We will test the performance of web application with healthy user with parallel improvement and launch an application to the TTMed staff and TF patient in the further step.

Keywords: Tracking system, Trigger Finger, Internet of Things, Monitoring Web, Real-time database.

1. INTRODUCTION

Lifestyle of work that office men/ladies use at a mobile device or using mouse/keyboard equipment of computer for a long time and this activity can continue in daily routine causes many patients suffered with office syndrome. These cases might let some illness or disease occur on either muscles or bone, eyes and especially trigger finger (TF). The cause of TF is not only to work with hand in the same gestures for many hours per day, moreover, other activities e.g., housework, carry on the heavy things without changing their gesture or routine work effect a person as well. According to the risk of growing TF is 2% - 3% [1], the symptoms contain pain and loss of motion of the affected finger. Involved digits’ functional limitations can include imperfect grip strength and decreased physical ability to hold narrow handle objects. The treatments have a surgery method for the seriously ill patient by a physician and healing by hand rehabilitation on moderate illness and recovery after a surgery. Hence, remotely rehabilitation system for monitoring TF patient’s hand exercise is required for most cases.

As a result, our ICT team, obtained a rehabilitation tracking system for TF patient developed problem and retrieved data from IoT relief pain flex-sensor device from faculty of Traditional Thai Medicine (TTMed), Thai Massage department. They need to detect the symptoms and physical therapy for TF patients which has useful for doctor to diagnose the disease faster. The solution is created through a web application as tracking system that can display the patient information such as a patient's result of diagnosis and collect as history. The patient's hand force practice can send to the physicians and they can diagnose until monitor the symptoms of the patient continuously by dashboard on web application.

66


Internet of Things or IoT is the technology which automatically collects the input data by sensors and actuators connecting to internet by the embedded network device on microcontroller or separated/add-on connectivity chip. The data are transferred to the private/public storage like cloud provider or private server. End user can extract or retrieve and customize these data to analyze in specific domain. Many researchers did the IoT wearable device on enabled monitoring and smart healthcare management system. We study and implement these skills with Traditional Thai doctor on IoT rehabilitation tracking system for Trigger Finger patients.

Our objectives are 1) to implement an IoT rehabilitation tracking system for trigger finger patients. 2) in data layer, we manage the storage both real-time and local database to run smoothly. Finally, we use the software engineering combining the external web service with securing internal data together.

This paper structure is aligned as follows. Section 2 mentioned Theory and Related Work. Section 3 has addressed through software implementation methods. Section 4 presented the result of this research. The remains are conclusion, future work, acknowledgment, and reference.

2. THEORY AND RELATED WORKS

In this part, we describe and analyze the related work. This section is organized by studying the research of TTMed rehabilitation methods for TF, IoT devices development with the flex sensors, TF rehabilitation application, and local and real-time storages.

2.1 Traditional thai medicine method for monitoring TF rehabilitation

Normally, there are several protocols on physical therapy of TF [2]. They often based on a variety of factors to select an appropriate tool, including applicability to a given population, time, availability, psychometric soundness, and familiarity. For the assessment in the present western protocols study, they used the Functional Dexterity Test (FDT) and the Purdue Pegboard Test (PPT) [2] which have also been reported to be usually used by occupational therapists in hand clinics from Israel and the United States [3]. The FDT tool is suitable for the adult population over 20 years. Information of clients' ability to use their hands for functional tasks requiring an equipment are given [4]. The PPT is also use the equipment to test the quality and speed of hand performance as the person accomplishes the four subtests in the limited trial time since 1948 until now [2, 5]. While TTMed method has to support the poverty patients who live in the distant especially security concerned in the southern of Thailand, it provided the convenient assessment way by pressing and releasing hand, for instance, with a set of ten times and 10 seconds of each manner. These pressed-released a fist have also used to exercise in homed-based hand therapy.

Among these TF physical therapies, the protocols require a doctor to manually enter details, measure the degree of fingers, symptoms, and the recommended treatment. Regarding reducing medical staffs time and complement of specific learning on firmly against the degree of fingers, we bring IoT with flex sensors and TTMed method, for helping to observer a patient’s physiological signs in terms of reducing service time, increasing precision sign-evaluation, and reducing number of medical staffs.

2.2 IoT devices development with flex sensors

IoT device has an end node commonly combining actuator as output from the system and sensor as input to a system. We focused on two versions of flex sensors, 2.900 and 4.419 inches long respectively. Regarding Sparkfun’s [6] flex sensor 2.2- and 4.5-inch version datasheet [7-8], features of them are similar. These are angle displacement measurement, bends physically with motion device, possibly used for robotics, medical devices, and physical therapy. We categorized these two versions of flex sensors in Table 1 below. Detail includes version of sensor, part and active length, mechanical and electrical specifications, the way in which each sensor works, and basic circuit in schematic.

There are many researches regarding measurements of finger movements by flex sensors [9], measuring flexion movements [10], and using an advantage of sensor resistance change as the shape of the compartment deforms due to external force [10-12].

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About microcontroller, we use an Arduino family with small, complete, and based on the Tmega328P, that is Arduino Nano. It lacks only a DC power jack and works with a Mini-B USB cable instead of a standard one that can be used breadboard to improve and compliment.

Table 1. Comparison type of flex sensor 2.2- and 4.5-inches in Sparkfun.

Flex sensors Version: 2.2 inches (Rev A2) Version: 4.5 inches (v.2014 Rev A)

Part length 2.900 inches (73.66 mm) 4.419 inches (112.24 mm)

Active length 2.180 inches (55.37 mm) 3.750 inches (95.25 mm)

Mechanical Specifications

• Life Cycle: >1 million • Height: 0.43mm (0.017") • Temperature Range: -35°C to +80°C

Electrical Specifications

Flat Resistance 25K Ohms 10K Ohms ±30%

Bend Resistance 45K to 125K Ohms

(depending on bend radius)

minimum 2 times greater than

the flat resistance at 180° pinch bend

Power Rating 0.5 Watts continuous; 1Watt Peak

How it works

Schematics

(Basic circuit)

2.3 Trigger finger rehabilitation application

Due to we have studied various TF rehabilitation applications, each one has different characteristics. These apps have the similar models attaching with the tool. For example, Jamar Smart Lite [13] app is a connected app with tool measuring the force of hand named Hand Dynamometers and display the digital force value. Specific devices are used for applications. Hidden Bubble-Squeezing Game is a game that uses the speed on the squeezing it [14]. Oriori Ball is a ball that can measure the strength of the patient. The application is a game that can read the value of the patient but cannot be collected for analysis [15].

2.4 Local and real-time database

Local database is an internal storage of organization which can manage beneficially a data security channel for healthcare application. However, this kind of storage is not appropriate for writing the accelerated IoT input with multiple threads. Rautmare and Bhalerao had proved on MySQL and NoSQL database comparison for IoT application [16]. In addition, MySQL is stable for reading/selecting for higher number of records. Therefore, we use MySQL as a local storage.

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For IoT data layer, Google Firebase platform as a service, a NoSQL document data store has been selected [17]. Applications store data as JSON objects and interact with the local database using a JavaScript API. The data store is designed to scale with application call, thus there is no need to add servers, or perform other maintain a database back-end. Thus, choosing an IoT database depends on which query is mostly used and the application requirements.

2.5 WEB application as a medical staffs’ UI

After getting the requirement from TTMed staff, then we implement the UI for management the patient profile, browse the degree of five fingers at the same time, and enter the symptoms and detail for next time examination and history.

Our web application works use the novel technique that combined between sensor monitoring with multiple flex sensors devices each hand that is designed to display real-time physical mapping and related recent movement graphs for diagnostic approaches. All of these physicians and patients will be able to track those results on a web application that can be displayed on desktop computers’ and mobile devices’ browsers.

3. METHODOLOGY

This section describes the method of system architecture, and 3 layers including software and IoT device of IoT-Rehabilitation Software Architecture.

A. System Architecture

Figure 1. Overview of System Architecture

In Fig. 1 shown the structure of tracking system and related elements, including the connectivity of IoT systems. First, a microcontroller collects the patients’ data and then transforms the value which obtained from five flex sensors to the Google cloud platform through Wi-Fi. Second, the system transfers these tests; {date-time, patient’s token-key, angle, {thumb|index|Middle|Ring|Little}, {dominant-| non-dominant hand}} as JSON Structure, to MySQL database on the local server that located in TTMed place. Finally, the value on this database will be shown on TTMed staff’s web application; for example, each finger’s exercise activities and comparison graph of all exercises.

Request/response

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B. IoT Software Architecture Layer

When the rehabilitation system concerns to IoT device, the layers of everything are occurred. In Fig. 2 shown three layers of IoT device covering all ecosystem as follows.

Figure 2. IoT-Rehabilitation Software Architecture

Application layer “IoT Rehabilitation Tracking System for Trigger Finger Patients by ICT-

TTMed, PSU”

Three user roles are TTMed staff, patient and administrator. TTMed staff and patients need to register for access the system. When TT-doctor login, they can view information of all patients; however, patients can only see their own data in testing purposes. When administrator login, they can download .CSV file for analysis on external tool.

In detail, TTMed Staffs need to register in “create user” page. After that, they can login and access to system. They can edit their profile in “edit profile” page anytime. In “patient information” page, they can edit their own take-care patients diagnosis data and status of patient. In “search patient” page, they can search the list of patients by entering the name or ID or Hospital ID. Next, patient’s test transaction is sent to the local database and process “appointment management” is scheduled a next time plan for patient with TTMed staff by selecting the date and write a notable message.

Patients can register, login and access to system. When they logged in, patients can see the value of testing and compare the value by choosing from a date. Furthermore, they can receive the appointment data in appropriate GUI design.

Admin can login to his/her pages. From Fig. 2, this role can see every page without editing the other profiles which differed to the other roles allowing to see only their roles. After login, admin can download all the patient tested value as CSV form for analysis on extended tool, for instance, R, Power BI.

Storage/Management Layer “IoT Device Management”

Fig.2 shown an IoT Device Management in the middle layer. There are many programming languages and a local database. Two categories of code are C on Arduino IDE used for IoT Hardware in IoT device layer, and Markup Languages family for web application connected to local data storage. It is MySQL that has many tables which are important consisting of ‘users’, ‘user status’, ‘appointments’, ‘website stats’, ‘test master’, and ‘test detail’. The ‘user status’ is used for action confirmation of membership for doing login to web application until logout. ‘Appointments’ table of TTMed staff and patient which can check appointment date and time for next time test on each other side. This table is matched test between patient and TTMed staff. Next, the ‘test detail’ table is kept each patient test date, time, fingers’ angle value and diagnosis detail. The ‘test master’ table is recorded symptom’ s level of each patient with 3 levels; normal, remind and severe. Finally, ‘website stats’ table

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is stored the number of visitors who use web application and retrieve IP address and browser type of visitors, only admin role can use this section.

According MySQL local database, patient’s test data are retrieved from Google Firebase and sent to display on the screen for TTMed staffs at upper layer. While data base is collecting data and aligning on relational database waiting for TTMed staffs requested the information and converting SQL, then sent to the website on browser. The representation from HTML, JavaScript, CSS, and PHP are transferred the same website through HTTPS. About C on Arduino that will upload to IoT device layer managing microcontroller on IoT device layer.

IoT Device Layer

In the bottom layer of Fig.2 shown “IoT Device layer” or “Sensor layer”. There is a set of flex sensors equipment. The microcontroller board is Arduino Nano connected Internet through NodeMCU ESP 8266 WiFi module. The electric wires combining flex sensors for 5 fingers with 47kΩ resistor and Arduino Nano. The algorithm of resistance calculation to estimate the sensor's bend angle in Algo.1, Firebase connection, upload tested value to Firebase, and extracting Firebase to local database are explained in Fig.3. In Algorithm 1, we focused on the loop function, Line 15 - 22, that is the most important to calculate the bend angle of each finger.

Algorithm1. Resistance calculation of bend angle algorithm for flex sensors

Resistance calculation of sensor's bend angle algorithm

Input: Read Analog to Digital Converter (ADC)

Output: Bend angle of a finger 1. Include Adafruit_ADS1015 //include Adafruit_ADS1015 with other 2. const int FLEX_SENSOR_PIN = A0; //set analog pin 3. const float Voltage = 4.98; // 5 voltage for Arduino Nano 4. const float Resistor = 47500.0; // measured resistance of ~ 4.7K resistor 5. const float STRAIGHT_RESISTANCE = 37300.0; //resistance at 0-degree int 6. const float BEND_RESISTANCE = 90000.0; //resistance at 90-degree int 7. int bend_angle_value = 0; 8. Construct an instance of an ADS1015 with the default address (0x48) 9. Set date, time format 10. Setup function: 11. Connect WiFi 12. Initialize ADS1015 13. Begin Serial->9600 //set baud rate 14. Set FLEX_SENSOR_PIN(A0) as input 15. Loop function: 16. int16_t adc0 //read ADC then results are 16bit integer 17. adc0 = read ADC_SingleEnded of 0 18. int ADC_of_flex_sensor = Read FLEX_SENSOR_PIN as analog 19. float Voltage_of_flex_sensor = ADC x Voltage / 1023.0 20. float Resistance_of_Flex

= Resistor x (Voltage/Voltage_of_flex_sensor - 1.0)

21. float angle0

=map(Resistance_of_Flex,STRAIGHT_RESISTANCE,BEND_RESISTANCE,0,90.0)

22. int bend_angle_value = (int) angle0

From Algorithm.1, we described a voltage divider circuit creation that combined a flex sensor

with a 47k resistor. It connects between analog to digital conversions (ADC) pin A0 and ground (GND). Each flex sensor connects from A[0..4] to 5V. In development environment, we test the ADC value until stable run for every finger.

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In this section, we have got an IoT Rehabilitation Tracking System for Trigger Finger Patients System on web application. Each role can see his/her own page. At the same time, we can perform an IoT device test from the healthy person and can store the test values of each patient's sensor. Moreover, the test values can be displayed in graph. Thus, the TT doctor and patient can compare the test values of rehabilitation between a customized period.

In detail, we collect each web page of each user role. TTMed staff role can manage the pages in Fig. 3-6 about handling his/her patients management system. TTMed staffs can also manage web pages in Fig. 7-8. about mutual appointments between them and patient. However, the patients can only browse them. The last picture, Fig. 9, is an export page that used by administrator for exporting, e.g., CSV, XLS to import for analysis tool like R, Power BI or any tool that can ready these kinds of file.

Figure 3. This page is a TTMed staff activity. Routine task is aligned in 3 menus at left hand side. There are “TT doctor Account management, Patients, and Appointments.

Figure 4. Realtime monitoring dashboard in TTMed examination room environment. Staff demonstrates the pressing and releasing hand exercise to the patient before testing each round. Five charts represented for five fingers test. X-axis is time and Y-axis is bend angle.

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The other results have been examined from the IoT device test by healthy people. First, we tested the range of Analog to Digital Converter (ADC) of each finger’s flex sensor. The results are in the following set {lower bound of the ADC current range, are in the upper bound of the ADC current range, the lower bound of the ADC’s target range, the upper bound of the ADC’s target range}. The stable results of 5 finger set of resistance and bend angle are thumb: {9601, 5300, 0, 90}, index: {9601, 5300, 0, 90}, middle: {10430,5300, 0, 90}, ring: {5500, 140, 0, 90}, and little: {5500, 140, 0, 90}.

Second, our well-being people have tried to press and release their hand which is essential part of software engineering test. On three days test, we collected the data and improved the system accordingly. Final, this system has received the discussion with TTMed doctor and operated in development-operation (DevOps) phase again.

Figure 5. TTMed staff clicks on the view button of patient that will be linked to compare multiple chart per page. This page compares the test values both of two selected days.

Otherwise, this page shows a patient activity. Patient can also view the comparison charts.

Figure 6. “Result of test” page. When a TTMed staff clicks on the result button of patient will be linked to result of test. The staff can record the result of patient testing in this page, which have 3 levels of normal, remind (moderate) and danger (severe).

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Figure 7. Both of TTMed staff and patient can use the appointment date and time for next time testing or examination.

Figure 8. A patient activity that can view the appointment date and time for next time checking.

Figure 9. This page allows administrators to export all patient test values. Regarding a performance of system as Fig. 10 (a), we used a tool from Chrome Developer Tools

in inspect element names Audits testing and reporting the performance of web application. This web concerns four sections of testing; Performance, Accessibility, Best Practices, and SEO. The points are 98, 34, 71, and 60 from 100 of each section respectively. Our metrics’ results for a minute test including First Contentful Paint 2.1 seconds, it marks the time at which the first text or image is painted. Speed Index 2.1 seconds, Time to Interactive 2.1 seconds, First Meaningful Paint 2.1 seconds, First CPU Idle 2.1 seconds, and Estimated Input Latency for 10 milliseconds. It can be seen the specific details in Performance tab as Fig 10 (b).

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(a) Audits tab

(b) Performance tab

Figure 10. Performance of web application from Chrome Developer Tools in inspect element names (a) Audits and (b) Performance tabs

5. CONCLUSION

This work proposes a design of system architecture and development of a web application for tracking Trigger Finger patients. The web application was built on HTML5, PHP, CSS and JavaScript and connects to Real-time database, Google Firebase server’s database, MySQL to store and retrieve information inside TTMed hospital for the web application as well. The system will be used by physician and Traditional Thai Medicine in Prince of Songkla University (PSU) and patients hospitalized in this general examination room. Our system can help the physician monitoring the patient's symptoms closely and help on the treatment plan decide. It also helps patients knowing the results of the treatment. The test data is automatically sent from the NoSQL database to the SQL database, rendering it real-time, and then comparing the values of each test. It can be also exported values from the database to the external analysis tools. It also benefits for further TTMed’s rehabilitation analytics.

Telerehabilitation by IoT potentials many benefits for both of medical staffs and the remote users such as patients. However, an ethical issue has been identified before using on real patient. In this case, our researcher team will handle the ethical issues process of patient safety in this case.

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REFERENCES

[1] Valdes, K. (2012). A retrospective review to determine the long-term efficacy of orthotic devices for trigger finger. Journal of Hand Therapy, 25(1), 89-96.

[2] Langer, D., Maeir, A., Michailevich, M., & Luria, S. (2017). Evaluating hand function in clients with trigger finger. Occupational therapy international, 2017.

[3] Langer, D., Luria, S., Maeir, A., & Erez, A. (2014). Occupation‐based Assessments and Treatments of Trigger Finger: A Survey of Occupational Therapists from Israel and the United States. Occupational therapy international, 21(4), 143-155.

[4] Aaron, D. H., & Jansen, C. W. S. (2003). Development of the Functional Dexterity Test (FDT): construction, validity, reliability, and normative data. Journal of Hand Therapy, 16(1), 12-21.

[5] Tiffin J. Purdue Pegboard Test. Chicago, Ill, USA: Science Research; 1948. [6] SparkFun Electronics [Online]. Available https://www.sparkfun.com/, accessed 20 Dec 2018. [7] Flex Sensor 2.2Inch length [Online]. Available https://www.sparkfun.com/datasheets/

Sensors/Flex/flex22.pdf, accessed 19 Dec 2018. [8] Flex Sensor 4.5Inch length [Online].Available https://cdn.sparkfun.com/datasheets/Sensors/

ForceFlex/FLEX%20SENSOR%20DATA%20SHEET%202014.pdf,accessed 19 Dec 2018. [9] Saggio, G., Riillo, F., Sbernini, L., & Quitadamo, L. R. (2015). Resistive flex sensors: a survey. Smart

Materials and Structures, 25(1), 013001. [10] Shen, Z., Yi, J., Li, X., Lo, M. H. P., Chen, M. Z., Hu, Y., & Wang, Z. (2016). A soft stretchable bending

sensor and data glove applications. Robotics and biomimetics, 3(1), 22. [11] Saggio, G., Riillo, F., Sbernini, L., & Quitadamo, L. R. (2015). Resistive flex sensors: a survey. Smart

Materials and Structures, 25(1), 013001. [12] Dipietro, L., Sabatini, A. M., & Dario, P. (2008). A survey of glove-based systems and their applications.

IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 38(4), 461-482.

[13] Cognatus Innovations, LLC, Jamar Smart Lite [Online], https://play.google.com/store/ apps/details?id=com.cognatus.jamar&hl=en_US, accessed Nov 2018.

[14] B. Dominik. (2017, October 29). Google’s Pixel 2 Phones Have A Hidden Bubble-Squeezing Game [Online]. Available https://www.androidheadlines.com/2017/10/googles -pixel-2-phones-have-a-hidden-bubble-squeezing-game.html, accessed 20 Dec 2018.

[15] ORIORITECH. (2018, April 09). Oriori mini strength grip measure hand dynamom ball [Online]. Available https://www.orioritech.co/shop/oriori-ball

[16] S. Rautmare and D. M. Bhalerao, "MySQL and NoSQL database comparison for IoT application," 2016 IEEE International Conference on Advances in Computer Applications (ICACA), Coimbatore, 2016, pp. 235-238.

[17] Firebase [Online], Available https://firebase.google.com/, accessed Nov 2018. ACKNOWLEDGMENTS

This project is supported by PSUPrototypeMED-1/61 Fund, the Southern Thailand Science Park, Prince of Songkla University for IoT device, and RAP61K0004 Fund, RGJ Advanced Programme, Thailand Research Fund for programming technology. Thank you to Lecturer Kotchakorn Sookchan Intanuchit, Faculty of Traditional Thai Medicine, Prince of Songkla University who gave us the valuable knowledge of Trigger Finger rehabilitation for TTMed patients.

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CSE-02 Corrosion Depth Prediction of an Onshore Gas

Pipeline by Using Artificial Neural Network

Wassamon Phusakulkajorn1,*, Piyamabhorn Uttamung1, Foifon Srisawat1,

Dhritti Tanprayoon1, and Namurata Sathirachinda Palsson1

1National Metal and Materials Technology Center (MTEC), National Science and Technology

Development Agency (NSTDA), 114 Thailand Science Park, Thanon Pahonyothin, Tambon Khlong

Nueng, Amphoe Khlong Luang, Pathum thani 12120, Thailand * E-mail: [email protected]; Fax: +66 2 564 6370; Tel. +66 2 564 6500 ext. 4359

ABSTRACT

Pipelines are well-known as the most reliable means of transporting oil and gas. Since oil and gas pipelines are subjected to deterioration and degradation during service, their conditions need to be periodically monitored and assessed. Inspected by means of the widely employed Magnetic Flux Leakage method, external corrosion was found to be the predominant form of pipeline failure. In order to prevent catastrophic environmental damage due to oil and gas spillage, inspection data thus need to be accurately interpreted to evaluate corrosion. Poor and conservative corrosion estimations lead to an unnecessary expenses and production time delay. It is well known that the linear model gives conservative estimation, surprisingly, it continues being used by many pipeline inspectors. The reason is that it is simple and practical to predict the corrosion growth rate and depth by considering only detected metal loss. However, other factors such as environmental and operational conditions are also responsible for oil and gas pipelines’ deterioration and degradation. Including these external factors into an external corrosion evaluation, relationship between such factors and the corrosion depth has to be investigated and defined. Unfortunately, the effect of environments on external corrosion is not straightforward to factor into a closed mathematical form as it is nonlinear and, sometimes, unknown. Therefore, in this work, a nondeterministic artificial intelligent model was employed to predict external corrosion of an onshore gas pipeline. The pigging data for external corrosion provided by various inspection vendors was used to develop a model. The result indicated good agreement between the estimation obtained from the model and the corresponding inspection data.

Keywords: Corrosion depth prediction, Artificial neural network, Onshore gas pipeline, External corrosion. 1. INTRODUCTION

A pipeline system is an important means of medium transportation in oil and gas industry. In general, pipelines may be deteriorated by corrosion throughout its service. Due to the corrosive nature of the contents within, a pipeline has to undergo the periodical inspection to prevent an unforeseen catastrophe. Since maintenance of a pipeline requires a lot of expenses and effort, routine maintenance of every pipeline sections is economically unrealistic and unnecessary when no evidence of corrosion is presented. Therefore, inspection data needs to be accurately interpreted to evaluate corrosion growth [4].

In order to evaluate corrosion growth of a gas pipeline, several techniques are available in both model-driven [1,2,5,8,11] and data-driven [3-9,11-12] approaches. It is known that many factors related to materials, operational processes, and environments have effects on corrosion growth of a pipeline [6,12]. To obtain an accurate interpretation and prediction, the underlying mechanisms and relationships between such factors and corrosion growth have to be well understood and cooperated into a model [8,11]. This is of important aspect of a model-driven approach. Often, it is not straightforward to factor those parameters into a closed mathematical form as relationships are nonlinear and, mechanisms are, sometimes, unknown. Therefore, many researchers opted to simplify and neglect certain factors.

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A theoretical model like linear estimation is an example in which the corrosion rate is determined from only detected metal loss data, without taking other environmental factors into consideration. Such model is considered as the simplest predictor and is widely used among others even though it gives conservative estimation [5]. The short-term prediction obtained from the linear model can assume linearity. However, it might not be accurate enough for a long-term maintenance planning.

Unlike a model-driven approach, a data-driven approach such as artificial neural network (ANN) includes available factors into a corrosion evaluation with the aim to increase the accuracy of the prediction [11,12]. With the benefit of ANN’s black-box nature, relationship between such factors and the corrosion depth does not have to be investigated and defined prior to a model development. All of the relevant data have to be presented so that ANN can learn trends and patterns. The accuracy of ANN is strongly dependent on the completeness of the data themselves [4,6]. However, a negative corrosion rate and the limited amount of sample ILI data are common issues in corrosion growth calculation based on ILI data mentioned in [6]. Usually, the factors that are incomplete have to be omitted from the study [7,8] or their values have to be interpolated [4,10].

It can be understood that poor and conservative prediction may be obtained from neglecting some important factors in model-driven approach or lack and incomplete data in data-driven approach. Either leads to unnecessary expenses and production time delay. This work compared the reliability of the corrosion-depth prediction by ANN to the linear model when limited amount of data was provided. The prediction focused on the external corrosion of the selected onshore gas pipeline. The in-line inspection data used in model developments are elaborated in section 2. Detailed descriptions of the predictors and the design of the established ANN are demonstrated in section 3 and section 4. Experimental results and comparisons between two techniques are given in section 5. Section 6 concludes the paper.

2. COLLECTED DATA OF THE GAS PIPELINE

In this work, an onshore gas pipeline was studied in order to investigate its deterioration and degradation during service. The pipeline was constructed from longitudinally welded pipe material conforming to API 5L Grade X60. Its conditions have been periodically monitored and assessed every four or six years by means of the widely employed Magnetic Flux Leakage (MFL) method. Since the inspections showed that external corrosion was found to be the predominant form of pipeline failure, only external-corrosion inspection dataset was considered in a model development at this stage. Table 1. Number of historical ILI data for each inspection year.

Inspection year

1 2 3 4 5

Number of external corrosions 5 56 107 113 95

Table 2. An example of the provided inspection dataset for physical information.

Distance

(m)

Orientation

(hh:mm)

Depth

(%wt)

Length

(mm)

Width

(mm)

Wall

thickness

3819.20 6:04 11 15 15 8.47 3901.75 9:56 15 15 26 8.47 3914.17 9:27 35 20 22 8.47 4106.35 2:03 18 19 25 8.47 5463.73 8:47 10 14 15 8.47 5477.54 4:33 10 16 15 8.47 5480.38 4:48 10 14 15 8.47

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The pigging data for external pipeline inspection used in this paper were provided by two inspection vendors. The data were collected through in-line inspection (ILI) activities on the selected onshore pipeline with distance of 57.5 kilometres from 5 different inspection years. Numbers of historical ILI data for each inspection year are shown in Table 1. The nominal wall thickness (nwt) of the pipeline was 8.74 mm with sections of 12.70 mm. The physical dimensions such as depth, width, length of corrosion defects were obtained from the MFL inspection method as illustrated in Table 2.

The other available data that demonstrated relation to external corrosion of the pipeline was pipe-to-soil parameter obtained by the method of Close Interval Potential Survey (CIPS). The CIPS method was employed to monitor the effectiveness of cathodic protection monitoring system and to show the exact location of coating defects. In this work, instant-off potential was determined as an implication of information associated with the surrounding environmental conditions of the pipeline. As the pipe-to-soil potentials (measured at test points installed every 1 kilometre intervals along the pipeline route) were not measured at the same locations as those of the external corrosions, its corresponding value was obtained by means of interpolation [4]. Figure 1 illustrates the sixth-order polynomial approximation of the pipe-to-soil instant-off potentials used in this work.

Figure 1. The sixth-order polynomial approximation of the pipe-to-soil instant-off potential.

3. CORROSION DEPTH PREDICTORS

3.1 Back-propagation artificial neural network

Artificial neural network (ANN) is a mathematical tool which is capable of representing arbitrarily complex non-linear processes. It is inspired by the way the human brain processes information. ANN consists of three main parts; input layer, hidden layer and output layer. Each layer consists of nodes, analogous to neurons in the brain. The nodes or artificial neurons communicate with others in the next layer by multiplying each of the inputs by a weight. Then the multiplications are combined and passed to an activation function as shown in Figure 2. In general, a one-hidden layer neural network can represent any finite input-output nonlinear relationship arbitrarily well, given enough hidden neurons [14]. An accuracy of ANN results can be improved from increasing number of nodes used in the hidden layer.

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Figure 2. Graphical sketch of a neural network with one hidden layer.

A back-propagation neural network is one of algorithms defining how weights are adjusted in order to achieve the desired outputs of the network. It is the most popular type in artificial neural networks due to their good ability of nonlinear mapping [9,11]. The weights of a back-propagation network are determined from the difference between the targeted and actual output values of all output and hidden neurons. This is done by a backward propagation of errors during the training phase in order to minimise the output error.

3.2 Linear corrosion growth model

There are many deterministic models available to estimate corrosion depth. Generally, such models are developed based on a defined relationship between the growth of corrosion depth and its affecting environmental factors. Unlike other deterministic models, the linear model is developed by considering only metal volume loss data. Furthermore, this model assumes a linear growth and is normally determined by comparing two corresponding corrosion depths at different time. It can be mathematically obtained as:

dt2 = CRt1× (t2-t1) + dt1,

where dt2, dt1, dt0 denote corrosion depth inspected at the year t2 t1 and t0, respectively,

t2, t1, and t0 denote the inspection year2, year1, and year0, respectively,

CRt1 denotes corrosion rate at the year t1 and can be obtained as:

CRt1 = 1 0

1 0d dt t

t t−−

.

4. THE DEVELOPMENT OF AN ANN MODEL

4.1 Determination of input parameters

It is a known practice that accuracy of an artificial neural network model is dependent on an input variable selection [6,8]. El-Abbasy et al. [8] reported on environmental factors influencing the external corrosion depth. Soil water content, soil temperature, soil resistivity, and cathodic protection were found to be of importance factors in establishing a model for predicting external corrosion depth. In addition to those environmental factors, corrosion defect geometries were included into such a model [5-7]. Therefore, the pipeline’s corrosion defect geometry such as depth, width, length, and orientation provided in the pigging data were determined as inputs to ANN models in this work. In addition, the pipe-to-soil off potential obtained from CIPS was also considered as another input parameter representing underlying information associated with the surrounding environmental condition of the pipeline.

Combinations of input parameters considered in our ANN model developments are elaborated in Table 3. The parameter T denoting exposure time of corrosion was determined in order to specify the inspection year interval between the predicted corrosion depth and the previous inspection. As only five

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previous inspection years were available, each input parameter was taken from only one previous inspection year. Consequently, the number of input nodes in an ANN architecture equaled to the number of input parameters considered in the model.

Table 3. Combinations of input parameters considered in an ANN model development.

Model Input

parameters

A number of

Input nodes

1 d,r,T 3 2 d,r,cp,T 4 3 d,w,l,T 4 4 d,w,l,r,T 5 5 d,w,l,r,cp,T 6

*** d=depth, l=length, w=width, r=orientation, cp=CIPS 4.2 An ann architecture

In the present work, a three-layer ANN with back-propagation learning algorithm was developed in MATLAB programming environment. It comprised one input layer, one hidden layer and one output layer in which the output Y(X) of the network was designed to be the external corrosion depth at the predicted inspection year. A number of nodes in the input layer was considered as described in section 4.1. A number of nodes used in the hidden layer was experimentally varied from 1 to 200 in order to find the ANN architecture associated with the input nodes used. The collected ILI data were divided into three groups for network training, testing and validation with ratio of 60:20:20. A group separation was done according to year variable. The Levenberg-Marquardt training algorithm was selected [9,13]. The activation function was the widely-employed log-sigmoid [3] which is defined as follow:

11

( )x

ef x −=

+

4.3 Model evaluation metrics

To evaluate the performance of an ANN model, the collected ILI data was compared against the corrosion depth predicted by ANN. Various statistical measurements can be used as a performance criteria. In this study, the coefficient of determination (R2) and root mean squared error (RMSE) were employed. The notations used in the following statistical formula were xi and xre

i, i, and N, in which xi

and xrei denote respectively the ANN prediction and the corresponding ILI of the ith data. N is the number

of data.

4.3.1 Root mean squared error: RMSE

Root mean squared error is a statistical measure of the average magnitude over the verification sample of the squared values of the differences between the prediction and the corresponding experimental data. It is mathematically expressed as:

1

21)(

Nre

i i

i

RMSE x xN =

= −

The RMSE can range from zero to infinity. It is a negatively-oriented score, i.e., lower values are better. The greater value of RMSE means higher variance in the predictions.

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4.3.2 Coefficient of determination: R2

The coefficient of determination (R2) is one of the widely used measurements of model performance described as follow:

( )2

2 12

1

( )1

N re

i ii

N

i i

xR

x x

x=

=

−= −

−

The R2 ranges from zero to one. Zero value of R2 indicates that the model fails to predict while one indicates perfect prediction. It can be seen from the formula that the computational value of R2 can yield negative values which means the model fails to capture the data behaviour.


5.1 Prediction by ANN

After data cleansing and formatting, 173 datasets were used to establish and test ANN models for external corrosion depth prediction. There were 104 datasets for training, 35 datasets for testing and 34 datasets for validation. The training set was used to train the network whereas the testing set was used to test the network during the model development and also to continuously correct it by adjusting the weights of network links. The validation dataset, which is not presented to the network during the network training, was used to validate the established ANN architecture of each input combination. After a number of hidden nodes were experimentally varied, the obtained ANN architecture of each input combination is presented in Table 4. This shows performance of the well-demonstrating model during the training, testing, and validating schemes, obtained from comparison between the ILI and predicted data. It can be seen that Model 2 and Model 5 exhibited relatively good performance in the training set and validation set. Generally, less input parameters are preferable as it is more practical and lower computational time. Moreover, Model 2 showed slightly better performance than Model 5 when implemented with real field data. Therefore, Model 2 was selected as the ANN model for external corrosion depth prediction of the selected gas pipeline. Table 4. Comparison of ANN models with different input parameters considered.

Model Input

parameters

Architecture Training set Testing set Validation set

R2 RMSE R2 RMSE R2 RMSE

1 d,r,T 3-1-1 0.694 5.382 0.573 4.496 0.627 5.179 2 d,r,cp,T 4-1-1 0.715 5.189 0.503 4.848 0.726 4.439 3 d,w,l,T 4-2-1 0.709 5.241 0.606 4.313 0.584 5.469 4 d,w,l,r,T 5-1-1 0.695 5.371 0.575 4.484 0.619 5.228 5 d,w,l,r,cp,T 6-1-1 0.717 5.179 0.503 4.486 0.716 4.517

*** d=depth, l=length, w=width, r=orientation, cp=CIPS, T=exposure time

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The selected ANN model comprised four input nodes in which each node representing corrosion depth, orientation, pipe-to-soil off potential, and exposure time, with only one hidden neuron in the design architecture. The model can be expressed mathematically as

dt2 = f (dt1, rt1, cpt1, T).

The predicted corrosion depths were consistent with the associated ILI data as illustrated in Figure 3. The maximum discrepancy was approximately 12%wt.

Figure 3. External corrosion depths obtained from the ANN model 2.

5.2 Comparison to the linear growth model

It is interestingly surprised that many pipeline inspectors still employ the linear model. This linear model is conventional and its idea is based on the corrosion rate technique that assumes to be linear. Although it is practical and easy to utilise, poor and conservative estimations can lead to an unnecessary expenses and production time delay. Hence, this section provides a comparison between the ANN model established in section 5.1 and the corrosion rate technique. With the limited amount of data used, the capability of the developed ANN was determined and compared.

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Figure 4 illustrates a comparison between the external corrosion depth predicted by the established ANN model and by the corrosion rate technique. It can be seen that external corrosion behaviour could not be accurately predicted by the corrosion rate technique. This made error obtained from the linear model relatively large with RMS error of more than 20%wt. Unlike the ANN model, the selected architecture demonstrated relatively good performance with RMS error of approximately 12%wt. This led to an improvement of 40% approximately.

Figure 4. Comparison between ILI data and the external corrosion depth prediction obtained from ANN and linear model.

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6. CONCLUSION

In this paper, an artificial neural network model was developed to predict the external corrosion depth of an onshore gas pipeline based on five years available in-line inspection data. The gas pipeline material was API 5L Grade X60. The inputs to the selected ANN were external corrosion depths, their orientation, their associated pipe-to-soil off potential, and the corresponding exposure time from the previous inspections. Our training and testing results showed good agreement between the prediction obtained from the ANN technique and the associated ILI data. To enable the manufacturing companies to trust the model, ILI datasets of a real field data were used to validate the model. In the validation set, the network performance demonstrated RMSE of 4.4339%wt and R2 of 0.726. The maximum discrepancy between the prediction and the ILI data was approximately 12%wt. Moreover, our developed ANN model outperformed the conventional linear growth model with an improvement of 40% approximately. REFERENCES

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ACKNOWLEDGMENTS

This research is supported by the National Metal and Materials Technology Center (MTEC), the National Science and Technology Development Agency (NSTDA) under Ministry of Science, Thailand.

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