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Optical nature of non-substituted triphenylmethyl cation:Crystalline state emission,thermochromism,and phosphorescence

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Aggregate 2021年第6期2卷 99-108页

作者： Tomohiko Nishiuchi Hikaru Sotome Risa Fukuuchi kenji Kamada Hiroshi Miyasaka Takashi kubo Department of Chemistry Graduate School of ScienceOsaka UniversityToyonakaOsakaJapan Innovative Catalysis Science Division Institute for Open and Transdisciplinary Research Initiatives(ICS-OTRI)Osaka UniversitySuitaOsakaJapan Division of Frontier Materials Science and Center for Advanced Interdisciplinary Research Graduate School of Engineering ScienceOsaka UniversityToyonakaOsakaJapan Nanomaterial Research Institute(NMRI) National Institute of Advanced Industrial Science and Technology(AIST)IkedaOsakaJapan Department of Chemistry Graduate School of Science and TechnologyKwansei Gakuin UniversitySandaHyogoJapan

Since the discovery of the triphenylmethyl(trityl)cation 120 years ago,a variety of aromatic cations having various colors and luminescence properties have been rigorously ***,differently substituted trityl cations have been synthesized,and their optical properties have been ***,the optical properties of the parent,non-substituted and highly reactive trityl cation,which was observed to be very weakly luminescent,have not been subjected to detailed *** the effort described herein,we explored the optical nature of non-substituted trityl hexafluorophosphate(PF_(6))in the crystalline *** PF_(6) was found to exist as two crystal polymorphs including a yellow(Y)and an orange(O)***,we observed that these crystalline forms display crystalline-state emission with different *** results of X-ray crystallographic analysis showed that the two polymorphs have totally different molecular packing ***,an investigation of their optical properties revealed that the O-crystal undergoes a distinct color change to yellow upon cooling as a consequence of a change in the nature of the charge transfer interaction between the cation and PF6 anion,and that both the Y-and O-crystal exhibit phosphorescence.

关键词： aggregation induced emission carbocation phosphorescence crystal phase transition thermochromism

Study of High Power ICRF Antenna Design in LHD

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Plasma Science and Technology 2010年第1期12卷 15-19页

作者： Hiroshi KASAHARA Tetsuo SEKI kenji SAITO Ryuhei KUMAZAWA Takashi MUTOH Goro NOMURA Fujio SHIMPO Shin kubo Takashi SHIMOZUMA Yasuo YOSHIMURA Hiroe IGAMI Hiromi TAKAHASHI 赵燕平 Takuya OOSAKO Yuichi TAKASE National Institute for Fusion Science Institute of Plasma Physics Chinese Academy of Sciences University of Tokyo

In large helical device （LHD）, antenna loadings are different for minority ion cyclotron heating （MICH with 38.47 MHz） and mode-converted ion Bernstein wave heating （MC-IBW with 28.4 MHz）, and it is necessary to improve antenna loading with low heating efficiency to avoid arching on transmission line. To design a new ion cyclotron range of frequencies （ICRF） antenna in LHD, calculation for a simple antenna model is conducted using three-dimensional electrical magnetic code （high frequency structure simulator, HFSS） for an water loading as an imaginary plasma with low heating efficiency. At resonant frequencies, antenna loading is sensitive to strap width, and resonant frequencies are strongly related to strap height. There is no differences of RF current profile on the strap surface between resonant frequency and non-resonant frequency. The strap should be perpendicularly placed against the magnetic field line, since Faraday-shield angle will lead to a decrease in the effective antenna height.

关键词： ICRF minority heating mode conversion heating antenna design LHD

The Concept Study of Recombinant Human Soluble Thrombomodulin in Patients with Acute Respiratory Distress Syndrome

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International Journal of Clinical Medicine 2013年第11期4卷 488-495页

作者： kenji Tsushima Toshiki Yokoyama Tomonobu Koizumi Keishi kubo Koichiro Tatsumi Department of Pulmonary Medicine Shinonoi General Hospital Nagano Japan Department of Respirology Graduate School of Medicine Chiba Uni-versity Chiba Japan Department of Respirology Graduate School of Medicine Chiba Uni-versity Chiba Japan First Department of Internal Medicine Shinshu University School of Medicine Nagano Japan

Background: Recombinant human soluble thrombomodulin (rhTM) was approved for the treatment of disseminated intravascular coagulation in Japan, and rhTM has anti-inflammatory effects. Disordered coagulation is a part of the acute respiratory distress syndrome (ARDS) pathophysiology and thus we hypothesize that anticoagulant therapy may help. This preliminary study was to observe the safety of rhTM administration and the improvement on biomarker levels after the therapy for ARDS-patients. Objectives: Case series of ARDS-patients. Methods: Seventeen ARDS-patients that required ventilatory management were treated with rhTM and clinical and laboratory data were collected including platelets, thrombin-antithrombin complex (TAT), fibrinogen degradation products, oxygen saturation/the fraction of inspired oxygen (SpO2/FIO2), and high-mobility group-1 (HMG-1). The administration of rhTM was started during 6 days at a bolus dose of 0.06 mg/kg/day immediately after the diagnosis of ARDS. Results: Eleven of the 17 ARDS-patients were alive at 28 days after the beginning of the administration of rhTM. The serial pattern of the SpO2/FIO2 showed remarkable differences between the survivors and nonsurvivors from day 5 to day 7. The TAT in the survivors significantly decreased after treatment, and there were significantly lower levels in the TAT on day 7 in comparison to that of the nonsurvivors. The serial changes of HMG-1 showed increased levels in the nonsurvivors until day 5 after the administration of rhTM. Conclusions: Additional rhTM administration can safely improve the parameters in survival ARDS-patients, as demonstrated by significant improvements in the SpO2/FIO2, HMG-1 and TAT.

关键词： Acute Respiratory Distress Syndrome Recombinant Human Soluble Thrombomodulin Thrombin-Antithrombin Complex SpO2/FIO2 High-Mobility Group-1

Application of Word Embedding to Drug Repositioning

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Journal of Biomedical Science and Engineering 2016年第1期9卷 7-16页

作者： Duc Luu Ngo Naoki Yamamoto Vu Anh Tran Ngoc Giang Nguyen Dau Phan Favorisen Rosyking Lumbanraja Mamoru kubo kenji Satou Graduate School of Natural Science and Technology Kanazawa University Kanazawa Japan Institute of Science and Engineering Kanazawa University Kanazawa Japan

As a key technology of rapid and low-cost drug development, drug repositioning is getting popular. In this study, a text mining approach to the discovery of unknown drug-disease relation was tested. Using a word embedding algorithm, senses of over 1.7 million words were well represented in sufficiently short feature vectors. Through various analysis including clustering and classification, feasibility of our approach was tested. Finally, our trained classification model achieved 87.6% accuracy in the prediction of drug-disease relation in cancer treatment and succeeded in discovering novel drug-disease relations that were actually reported in recent studies.

关键词： Distributed Representation of Word Sense Discovery of Drug-Disease Relation Word Analogy

Cross Entropy Based Sparse Logistic Regression to Identify Phenotype-Related Mutations in Methicillin-Resistant <i>Staphylococcus aureus</i>

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Journal of Biomedical Science and Engineering 2020年第7期13卷 168-174页

作者： Bahriddin Abapihi Mohammad Reza Faisal Ngoc Giang Nguyen Mera Kartika Delimayanti Bedy Purnama Favorisen Rosyking Lumbanraja Dau Phan Mamoru kubo kenji Satou Graduate School of Natural Science and Technology Kanazawa University Kanazawa Japan Institute of Science and Engineering Kanazawa University Kanazawa Japan

Emergence of drug resistant bacteria is one of the serious problems in today’s public health. However, the relationship between genomic mutation of bacteria and the phenotypic difference of them is still unclear. In this paper, based on the mutation information in whole genome sequences of 96 MRSA strains, two kinds of phenotypes (pathogenicity and drug resistance) were learnt and predicted by machine learning algorithms. As a result of effective feature selection by cross entropy based sparse logistic regression, these phenotypes could be predicted in sufficiently high accuracy (100% and 97.87%, respectively) with less than 10 features. It means that we could develop a novel rapid test method in the future for checking MRSA phenotypes.

关键词： MRSA Phenotype Classification Feature Selection High-Dimensional Binary Data Cross Entropy

Improving Protein Sequence Classification Performance Using Adjacent and Overlapped Segments on Existing Protein Descriptors

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Journal of Biomedical Science and Engineering 2018年第6期11卷 126-143页

作者： Mohammad Reza Faisal Bahriddin Abapihi Ngoc Giang Nguyen Bedy Purnama Mera Kartika Delimayanti Dau Phan Favorisen Rosyking Lumbanraja Mamoru kubo kenji Satou Graduate School of Natural Science and Technology Kanazawa University Kanazawa Japan Institute of Science and Engineering Kanazawa University Kanazawa Japan

In protein sequence classification research, it is popular to convert a variable length sequence of protein into a fixed length numerical vector by using various descriptors, for instance, composition of k-mer composition. Such position-independent descriptors are useful since they are applicable to any length of sequence;however, positional information of subsequence is discarded even though it might have high contribution to classification performance. To solve this problem, we divided the original sequence into some segments, and then calculated the numerical features for them. It enables us to partially introduce positional information (for instance, compositions of serine in anterior and posterior segments of a sequence). Through comprehensive experiments on the number of segments and length of overlapping region, we found our classification approach with sequence segmentation and feature selection is effective to improve the performance. We evaluated our approach on three protein classification problems and achieved significant improvement in all cases which have a dataset with sufficient amino acid in each sequence. This result has shown the great potential of using additional segments in protein sequence classification to solve other sequence problems in bioinformatics.

关键词： Protein Sequence Classification Protein Descriptor Sequence Segmentation Feature Selection

Combined Use of k-Mer Numerical Features and Position-Specific Categorical Features in Fixed-Length DNA Sequence Classification

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Journal of Biomedical Science and Engineering 2017年第8期10卷 390-401页

作者： Dau Phan Ngoc Giang Nguyen Favorisen Rosyking Lumbanraja Mohammad Reza Faisal Bahriddin Abapihi Bedy Purnama Mera Kartika Delimayanti Mamoru kubo kenji Satou Graduate School of Natural Science and Technology Kanazawa University Kanazawa Japan Institute of Science and Engineering Kanazawa University Kanazawa Japan

To classify DNA sequences, k-mer frequency is widely used since it can convert variable-length sequences into fixed-length and numerical feature vectors. However, in case of fixed-length DNA sequence classification, subsequences starting at a specific position of the given sequence can also be used as categorical features. Through the performance evaluation on six datasets of fixed-length DNA sequences, our algorithm based on the above idea achieved comparable or better performance than other state-of-the art algorithms.

关键词： Sequence Classification Numerical and Categorical Features Feature Selection

DNA Sequence Classification by Convolutional Neural Network

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Journal of Biomedical Science and Engineering 2016年第5期9卷 280-286页

作者： Ngoc Giang Nguyen Vu Anh Tran Duc Luu Ngo Dau Phan Favorisen Rosyking Lumbanraja Mohammad Reza Faisal Bahriddin Abapihi Mamoru kubo kenji Satou Graduate School of Natural Science and Technology Kanazawa University Kanazawa Japan Institute of Science and Engineering Kanazawa University Kanazawa Japan

In recent years, a deep learning model called convolutional neural network with an ability of extracting features of high-level abstraction from minimum preprocessing data has been widely used. In this research, we proposed a new approach in classifying DNA sequences using the convolutional neural network while considering these sequences as text data. We used one-hot vectors to represent sequences as input to the model;therefore, it conserves the essential position information of each nucleotide in sequences. Using 12 DNA sequence datasets, we evaluated our proposed model and achieved significant improvements in all of these datasets. This result has shown a potential of using convolutional neural network for DNA sequence to solve other sequence problems in bioinformatics.

关键词： DNA Sequence Classification Deep Learning Convolutional Neural Network

Improved Protein Phosphorylation Site Prediction by a New Combination of Feature Set and Feature Selection

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Journal of Biomedical Science and Engineering 2018年第6期11卷 144-157页

作者： Favorisen Rosyking Lumbanraja Ngoc Giang Nguyen Dau Phan Mohammad Reza Faisal Bahriddin Abapihi Bedy Purnama Mera Kartika Delimayanti Mamoru kubo kenji Satou Graduate School of Natural Science and Technology Kanazawa University Kanazawa Japan Institute of Science and Engineering Kanazawa University Kanazawa Japan

Phosphorylation of protein is an important post-translational modification that enables activation of various enzymes and receptors included in signaling pathways. To reduce the cost of identifying phosphorylation site by laborious experiments, computational prediction of it has been actively studied. In this study, by adopting a new set of features and applying feature selection by Random Forest with grid search before training by Support Vector Machine, our method achieved better or comparable performance of phosphorylation site prediction for two different data sets.

关键词： Protein Phosphorylation Phosphorylation Site Prediction Sequence Feature Feature Selection with Grid Search