別紙様式３学業及び研究等の進捗状況等報告書Report of Research Progress and Future Research Plan※ 本報告書は「4.指導教員からのコメント」を除き、北海道大学私費留学生特待プログラム」の HP に掲載します。（https://www.oia.hokudai.ac.jp/cier/own-scholarship/）*This report other than “4. Comments from your supervisor” will be published on thewebsite of “Hokkaido University Special Grant Program for Self-SupportedInternational Students”.
(https://www.global.hokudai.ac.jp/admissions/scholarships/scholarships-for-prospective-students/)氏名Onuh Amarachukwu FaithName所属学院等名

学年D3YearEnvironmental ScienceGraduate School所 属 専 攻 名Major所属研究室等名Division of Biosphere ScienceMolecular Biology CourseMiwa laboratoryLaboratory指導教員職・氏名Miwa, KyokoSupervisor１．研究テーマ名 Research themeThe involvement of Golgi localized proton pyrophosphatase (AVP2;1) in amelioration oflow boron stress in plants

２．研究等の進捗状況等 Research progress, etc.
研究の概要、独創性、状況等を含めて具体的に記入のこと。
※研究成果の発表･公表実績がある場合については学会名、掲載紙等の情報を含め詳細を記載することBoron (B) is a trace mineral nutrient required by plants and its deficiency affectscrop yield (Yan et al., 2006). The major role of B identified thus far in plants is thecrosslinking of rhamnogalacturonan II (RG-II), a pectin polysaccharide in cell wall. RG-IIis synthesized in the Golgi in an acidified state. In the Golgi compartment, the type IIproton pyrophosphatase (AVP2;1) plays a role of proton pumping coupled withpyrophosphate hydrolysis possibly for maintenance of Golgi acidification. While theenzymatic activity and expression patterns of AVP2;1 have been previously revealed, itsmutant phenotype and role in plants remains unknown. The aim of this study thereforewas to explore the physiological role of AVP2;1 in Arabidopsis thaliana. In the screeningof mutants under low boron, a mutant carrying a missense mutation in AVP2;1 wasisolated. This mutant showed increased primary root growth under low B conditions butno significant difference under normal B conditions compared to wildtype plants. T-DNAinsertion mutants showed a similar phenotype of enhanced root growth under low Bsuggesting that reduced function of AVP2;1 was responsible for the improved rootgrowth. The root cell observation of plant lines revealed an increase in meristematic zonelength, cell number in meristem and length of matured cell in avp2;1 mutants comparedto wildtype under low B. To explore the possible mechanism behind the increased rootgrowth and root cell under low B in avp2;1 mutants compared to wildtype Col-0, changesin cell wall components were considered as B function in cell wall stability. Analysis ofthe root cell wall revealed that calcium concentration was reduced in mutant root cellwall under low B condition and RG-II specific sugars also tended to be decreased inmutant root cell wall under low and normal B conditions, showing changes in avp2;1mutants cell wall. Together with the previous study and my analysis of mutant oftonoplast localized proton pyrophosphatase AVP1, a homolog of AVP2;1, it is suggestedthat the proton pumping component of AVP2;1 may possibly be the key component forplant growth. With the assumption of the proton pumping activity as the key forimproved root growth, reduced proton pumping due to the reduced function of AVP2;1 inavp2;1 mutants were considered to affect the activity of pectin (RG-II) synthesis enzymessuch as glycosyltransferase in the Golgi apparatus. This effect of AVP2;1 mutation onglycosyltransferase could lead to reduced amount of pectin (RG-II) been synthesized. Theimplication of the reduction of RG-II in mutants is that the amount of boron required forcrosslinking of RG-II would be reduced and hence the improved root growth of mutantsunder low boron supply. To support this, previous studies have implied that pectinamount or RG-II amount determines sensitivity to B deficiency hence the reducedsensitivity to low B in avp2;1 mutants. Taken together, this study proposes that reducedB requirement by changes in cell wall component may be the probable mechanism behindavp2;1 mutant phenotypes. This supports the idea that AVP2;1 plays a role in protonpumping and acidification of Golgi apparatus for maintenance of pectin synthesis.Published works: Onuh AF, Miwa K. Mutations in type II Golgi-localized proton pyrophosphataseAVP2;1/VHP2;1 affect pectic polysaccharide rhamnogalacturonan-II and alterroot growth under low boron condition in Arabidopsis thaliana. Frontiers in PlantScience 14:1255486 (2023) Onuh AF, Miwa K. Regulation, diversity and evolution of boron transporters inplants. Plant and Cell Physiology, Vol.62, No. 4, pp590-599 (2021)Academic conferences: Onuh AF, Miwa K. Functional characterization of AVP2;1 under low boroncondition. Annual boron meeting, Wakayama, Japan. (2019/11). (Oralpresentation) Onuh AF, Miwa K. Reduced function of AVP2;1 confers low-boron tolerancein Arabidopsis thaliana.61st Japanese Society of Plant Physiologists, Osaka,Japan. (2020/03). (Poster presentation) Onuh AF, Miwa K. Mutations in a Golgi localized proton pyrophosphataseAVP2;1 alleviates low-boron stress in Arabidopsis thaliana. 62nd JapaneseSociety of Plant Physiologists, Shimae, Japan. web meeting. (2021/03). (Posterpresentation) Onuh AF, Miwa K. The effect of Golgi localized proton pyrophosphatase, AVP2;1,mutation on boron stress. Annual boron meeting, Tokyo, Japan. (2022/09). (Oralpresentation) Onuh AF, Miwa K. Mutations in Golgi-localised proton pyrophosphatase, AVP2;1enhances root growth under limited boron supply by changes in cell wall stability.
33rd International Conference on Arabidopsis Research, Makuhari Messe, Chiba,Japan. (2023/06). (Poster presentation)３．今後の研究計画等 Future research plan現在までの進捗状況等を踏まえ、今後の研究発表等を含めて具体的に記入のこと。
In detail, based on current progress, including a future research presentation plan.

Preparations are currently ongoing for the presentation of this work during my PhDdefense as a requirement for the completion of my doctoral study.別紙様式３学業及び研究等の進捗状況等報告書Report of Research Progress and Future Research Plan※ 本報告書は「4.指導教員からのコメント」を除き、北海道大学私費留学生特待プログラム」の HP に掲載します。（https://www.oia.hokudai.ac.jp/cier/own-scholarship/）*This report other than “4. Comments from your supervisor” will be published on thewebsite of “Hokkaido University Special Grant Program for Self-SupportedInternational Students”.
(https://www.global.hokudai.ac.jp/admissions/scholarships/scholarships-for-prospective-students/)

氏名Name所属学院等名Graduate School所 属 専 攻 名Major所属研究室等名Laboratory指導教員職・氏名SupervisorMd Menhazul Abedin

学年YearD3Graduate School of Chemical Sciences and EngineeringChemical Sciences and EngineeringLaboratory of Molecule and Life Nonlinear ScienceProf. Tamiki Komatsuzaki１．研究テーマ名 Research themeMulti-armed Bandit Algorithm for Sequential Experiments of Molecular Properties with DynamicFeature Selection２．研究等の進捗状況等 Research progress, etc.

My research topic is the acceleration of designing chemical reactions and finding the targetmolecule whose property (such as drug efficacy and solvation affinity) is the highest using areinforcement learning, linear bandit (Optimistic in the face of uncertainty linear bandit-OFUL)framework. Precisely, the objective of the research is to find the molecule including catalyst toown the highest/lowest quantity desired by chemists such as hydration free energy, and reactionenantioselectivity, solvation affinity etc., by fewer experiments as possible via bridging chemistryand information science.For instance, to find molecules having lowerhydration free energies is important because itimplies the larger solubility of the molecules. Myproblem setting in this research is to find thelowest hydration free energy molecule by fewerexperiments as much as possible using linearbandit algorithm. Linear bandit is an algorithmwhichassumesthefeaturevariablestocharacterize each molecule or their power seriesare linearly related to a target quantity. Currently,I have developed a method based on thealgorithm and evaluating the performance byusing FreeSolv benchmark data, which containsexperimental and calculated hydration freeenergiesof642molecules.

Well-knownFigure 1. Boxplots for the time of finding themolecule of ground truth (upper) and stoppingtime (lower) in case of Hydration free energytopological fragment’s features are used in the current analysis. However, having the cleaningsteps, the total number of features are more?? than the sample size. Therefore, LASSO (leastabsolute shrinkage and selection operator) regression analysis is used for feature selection to cutaway the irrelevant features. Before applying our algorithm on the real data, I investigated twosynthetic datasets. Four analyzing protocols has been applied on the data such as i) OFUL with allfeatures (OFUL-AF), ii) OFUL with selected features (OFUL-SF), iii) Bayesian Optimization(BO) and iv) Random search (RS).
Findings of my research is briefly explained bellow: Figure 1 illustrates the boxplots for the timerequired to find the molecule of ground truth and the stopping time to confirm the found moleculeis the best within some error bound for hydration free energy of molecule. The statistics are basedon 50 runs. The median values for the best molecule finding time are approximately 370.0, 7.0,7.0 and 13.0 iteration steps for RS, BO, OFUL-AF, and OFUL-SF, respectively. I observed thatBO and OFUL-AF gets the same score, and OFUL-SF takes more 6 iterations to finds the bestmolecule, but RS takes huge iteration. So, theperformance of BO, OFUL-AF and OFULSF are almost nearby.
To confirm the performance of the methods, Ifocus on the stopping time. Median stoppingtime for RS, BO, OFUL-AF, and OFUL-SFare around 632.0, 217.0, 499.0 and 59.0iteration steps, respectively. It is observedthat OFUL-SF can stop experiments withinone ninth of the total number of candidateFigure 2. Average of RMSE for FreeSolvmolecules whereas the nearest competitor isBO, but RS and OFUL-AF shows far distant from OFUL-SF. I also confirmed methods apart fromOFUL-SF attain a success rate of 100 percent, whereas OFUL-SF achieves a success rate of 90percent. This success rate can be increased using smaller error rate.

To check the fitness of the models, I looked at the RMSE of each of the models. Figure 2 (upper)illustrates the average root mean square error (RMSE) across the different methods over the 50runs along with standard error. It shows average RMSE of OFUL-SF and BO close to each otheri.e., comparable but RS and OFUL-AF are much larger. This is an indication that OFUL-SF andBO give a better model than RS and OFUL-AF. Figure 2 (lower) shows that some of runs ofOFUL-SF stops at the very beginning and all the runs stopped before 200 iteration steps, but anyruns of BO never stops before 200. Again, since OFUL-SF does not find features for some of theruns, the average RMSE is calculated from less than 50 runs at the beginning of the experiment.
Conference and poster presentation:•“Desired property screening of molecules utilizing Linear Bandit Algorithm”, 14th CSEInternational Summer School and the 11th ALP International Symposium; 31 August- 1September 2023, Chippubetsu, Japan.

•“Linear Bandit Algorithm for the Sequential Experiments of Molecular Properties”, Hokkaidouniversity theoretical chemistry workshop; 17 August 2023, Japan.

•“Sequential Experiments of Molecular Properties Under Linear Bandit Framework”, ICATInternational Symposium on Catalysis 2023; July 18-19, 2023; Hokkaido University, Japan.

３．今後の研究計画等 Future research plan現在までの進捗状況等を踏まえ、今後の研究発表等を含めて具体的に記入のこと。
In detail, based on current progress, including a future research presentation plan.
In this year I would like to publish my research in a scientific journal. Currently mymanuscript is under evaluation within my research team and collaborators. I amoptimistic that I will be able to publish my research in the near future. I also interested inexpanding this research to include some other chemistry data as well. Ultimately, thisstudy will contribute significantly to the existing body of knowledge and pave the way forfurther advancements in the field.