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  1. Article: Combine strategy of treated activated charcoal and cell surface protein curli induction for enhanced performance in Escherichia coli immobilization

    Pachelles, Samson / Fuzi, Siti Fatimah Zaharah Mohamad / Man, Rohaida Che / Abdullah, Azian Azamimi / Illias, Rosli Md

    Process biochemistry. 2021 Nov., v. 110

    2021  

    Abstract: Immobilization of Escherichia coli (E. coli) on commercial activated charcoal was enhanced by mild chemical treatment coupled with curli production from E. coli. The chemical used to treat the activated charcoal were sodium hydroxide, hydrochloric acid, ... ...

    Abstract Immobilization of Escherichia coli (E. coli) on commercial activated charcoal was enhanced by mild chemical treatment coupled with curli production from E. coli. The chemical used to treat the activated charcoal were sodium hydroxide, hydrochloric acid, ammonium hydroxide, and acetic acid while nickel (II) chloride was used to promote the production of curli. Characteristics of the activated charcoal before and after chemical treatments were analyzed including its surface properties, pore size, and crystalline structure. The immobilization of E. coli was enhanced greatly after sodium hydroxide treatment which gave rise to more than 120 % cell immobilized compared to the untreated activated charcoal which was mainly attributed to the larger size of macropore, surface area, and pore volume. Curli were produced by the induction of nickel (II) chloride and further enhanced the cell immobilization by at least 50 %. Overall, the combine strategy enhanced cell immobilization by more than 160 %. The resulting biocatalyst from the enhanced cell immobilization managed to be reused up to 10 cycles for the enzyme cyclodextrin glucanotransferase expression while retaining up to 60 % of the enzyme’s initial activity.
    Keywords Escherichia coli ; acetic acid ; activated carbon ; ammonium hydroxide ; biocatalysts ; chemical treatment ; chlorides ; crystal structure ; cyclomaltodextrin glucanotransferase ; hydrochloric acid ; immobilized cells ; nickel ; porosity ; sodium hydroxide ; surface area ; surface proteins
    Language English
    Dates of publication 2021-11
    Size p. 26-36.
    Publishing place Elsevier Ltd
    Document type Article
    ISSN 1359-5113
    DOI 10.1016/j.procbio.2021.06.012
    Database NAL-Catalogue (AGRICOLA)

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  2. Article ; Online: The Effect of Temporal EEG Signals While Listening to Quran Recitation

    Azian Azamimi Abdullah / Zainab Omar

    International Journal on Advanced Science, Engineering and Information Technology, Vol 1, Iss 4, Pp 372-

    2011  Volume 375

    Abstract: Human brain which is one of the most complex organic systems, involves billons of interacting physiological and chemical process that will give rise to experimentally observed neuroelectrical activity, which is called an electroencephalogram (EEG). Many ... ...

    Abstract Human brain which is one of the most complex organic systems, involves billons of interacting physiological and chemical process that will give rise to experimentally observed neuroelectrical activity, which is called an electroencephalogram (EEG). Many researchers have investigated the effect of various events to the EEG signals such as meditation and classical music [1]-[3]. From their analysis result, they claimed that meditation and classical music can help a person to be in relaxing conditions. This study is performed in order to extend the research findings of the effect of religious activities to the human brain. EEG signals from subject at rests, as well as in different cognitive states; listening to Quran recitation and listening to hard music are measured and analysed. Statistical analysis using SPSS software is performed in order to test the validity of obtained data. The analysis results from this study show that listening to Quran recitation can generate alpha wave and can help a person always in relax condition compared with listening to hard rock music.
    Keywords EEG ; Quran Recitation ; Alpha Wave ; Science (General) ; Q1-390 ; Science ; Q
    Subject code 780
    Language English
    Publishing date 2011-01-01T00:00:00Z
    Publisher Indonesian Society for Knowledge and Human Development (INSIGHT)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Lung Cancer Cell Classification Method Using Artificial Neural Network

    Azian Azamimi Abdullah / Syamimi Mardiah Shaharum

    Information Engineering Letters , Vol 2, Iss 1.48, Pp 48-

    2012  Volume 57

    Abstract: Lung cancer seems to be the common cause of death among people throughout the world. Early detection of lung cancer can increase the chance of survival among people. However, problem seemed to merge due to time constraint in detecting the present of lung ...

    Abstract Lung cancer seems to be the common cause of death among people throughout the world. Early detection of lung cancer can increase the chance of survival among people. However, problem seemed to merge due to time constraint in detecting the present of lung cancer regarding on the several diagnosing method used. Hence, a Computer Aided Diagnosis (CAD) system using Artificial Neural Network (ANN) to classify the lung cancer present is developed in order to detect and to classify the present of lung cancer in an x-ray images. In this study, MATLAB have been used through every procedures made. These include image processing and ANN procedures. In image processing procedures, process such as image re-processing, lung field segmentation, lung nodule detection, and feature extraction have been discussed in detail, followed by the methods used for classification process using ANN. Finally, summary of the system and an outlook into the future application for the system are presented.
    Keywords Lung cancer ; Image processing ; Neural network ; X-ray images ; MATLAB. ; Electronic computers. Computer science ; QA75.5-76.95 ; Instruments and machines ; QA71-90 ; Mathematics ; QA1-939 ; Science ; Q
    Subject code 006
    Language English
    Publishing date 2012-03-01T00:00:00Z
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: Novel Approach to Classify Plants Based on Metabolite-Content Similarity

    Kang Liu / Azian Azamimi Abdullah / Ming Huang / Takaaki Nishioka / Md. Altaf-Ul-Amin / Shigehiko Kanaya

    BioMed Research International, Vol

    2017  Volume 2017

    Abstract: Secondary metabolites are bioactive substances with diverse chemical structures. Depending on the ecological environment within which they are living, higher plants use different combinations of secondary metabolites for adaptation (e.g., defense against ...

    Abstract Secondary metabolites are bioactive substances with diverse chemical structures. Depending on the ecological environment within which they are living, higher plants use different combinations of secondary metabolites for adaptation (e.g., defense against attacks by herbivores or pathogenic microbes). This suggests that the similarity in metabolite content is applicable to assess phylogenic similarity of higher plants. However, such a chemical taxonomic approach has limitations of incomplete metabolomics data. We propose an approach for successfully classifying 216 plants based on their known incomplete metabolite content. Structurally similar metabolites have been clustered using the network clustering algorithm DPClus. Plants have been represented as binary vectors, implying relations with structurally similar metabolite groups, and classified using Ward’s method of hierarchical clustering. Despite incomplete data, the resulting plant clusters are consistent with the known evolutional relations of plants. This finding reveals the significance of metabolite content as a taxonomic marker. We also discuss the predictive power of metabolite content in exploring nutritional and medicinal properties in plants. As a byproduct of our analysis, we could predict some currently unknown species-metabolite relations.
    Keywords Medicine ; R
    Subject code 580
    Language English
    Publishing date 2017-01-01T00:00:00Z
    Publisher Hindawi Limited
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: Novel Approach to Classify Plants Based on Metabolite-Content Similarity.

    Liu, Kang / Abdullah, Azian Azamimi / Huang, Ming / Nishioka, Takaaki / Altaf-Ul-Amin, Md / Kanaya, Shigehiko

    BioMed research international

    2017  Volume 2017, Page(s) 5296729

    Abstract: Secondary metabolites are bioactive substances with diverse chemical structures. Depending on the ecological environment within which they are living, higher plants use different combinations of secondary metabolites for adaptation (e.g., defense against ...

    Abstract Secondary metabolites are bioactive substances with diverse chemical structures. Depending on the ecological environment within which they are living, higher plants use different combinations of secondary metabolites for adaptation (e.g., defense against attacks by herbivores or pathogenic microbes). This suggests that the similarity in metabolite content is applicable to assess phylogenic similarity of higher plants. However, such a chemical taxonomic approach has limitations of incomplete metabolomics data. We propose an approach for successfully classifying 216 plants based on their known incomplete metabolite content. Structurally similar metabolites have been clustered using the network clustering algorithm DPClus. Plants have been represented as binary vectors, implying relations with structurally similar metabolite groups, and classified using Ward's method of hierarchical clustering. Despite incomplete data, the resulting plant clusters are consistent with the known evolutional relations of plants. This finding reveals the significance of metabolite content as a taxonomic marker. We also discuss the predictive power of metabolite content in exploring nutritional and medicinal properties in plants. As a byproduct of our analysis, we could predict some currently unknown species-metabolite relations.
    MeSH term(s) Cluster Analysis ; Metabolome ; Plants/classification ; Plants/metabolism ; Statistics as Topic ; Support Vector Machine
    Language English
    Publishing date 2017-01-09
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2698540-8
    ISSN 2314-6141 ; 2314-6133
    ISSN (online) 2314-6141
    ISSN 2314-6133
    DOI 10.1155/2017/5296729
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Development and Mining of a Volatile Organic Compound Database

    Azian Azamimi Abdullah / Md. Altaf-Ul-Amin / Naoaki Ono / Tetsuo Sato / Tadao Sugiura / Aki Hirai Morita / Tetsuo Katsuragi / Ai Muto / Takaaki Nishioka / Shigehiko Kanaya

    BioMed Research International, Vol

    2015  Volume 2015

    Abstract: Volatile organic compounds (VOCs) are small molecules that exhibit high vapor pressure under ambient conditions and have low boiling points. Although VOCs contribute only a small proportion of the total metabolites produced by living organisms, they play ...

    Abstract Volatile organic compounds (VOCs) are small molecules that exhibit high vapor pressure under ambient conditions and have low boiling points. Although VOCs contribute only a small proportion of the total metabolites produced by living organisms, they play an important role in chemical ecology specifically in the biological interactions between organisms and ecosystems. VOCs are also important in the health care field as they are presently used as a biomarker to detect various human diseases. Information on VOCs is scattered in the literature until now; however, there is still no available database describing VOCs and their biological activities. To attain this purpose, we have developed KNApSAcK Metabolite Ecology Database, which contains the information on the relationships between VOCs and their emitting organisms. The KNApSAcK Metabolite Ecology is also linked with the KNApSAcK Core and KNApSAcK Metabolite Activity Database to provide further information on the metabolites and their biological activities. The VOC database can be accessed online.
    Keywords Medicine ; R
    Language English
    Publishing date 2015-01-01T00:00:00Z
    Publisher Hindawi Limited
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: Development and mining of a volatile organic compound database.

    Abdullah, Azian Azamimi / Altaf-Ul-Amin, Md / Ono, Naoaki / Sato, Tetsuo / Sugiura, Tadao / Morita, Aki Hirai / Katsuragi, Tetsuo / Muto, Ai / Nishioka, Takaaki / Kanaya, Shigehiko

    BioMed research international

    2015  Volume 2015, Page(s) 139254

    Abstract: Volatile organic compounds (VOCs) are small molecules that exhibit high vapor pressure under ambient conditions and have low boiling points. Although VOCs contribute only a small proportion of the total metabolites produced by living organisms, they play ...

    Abstract Volatile organic compounds (VOCs) are small molecules that exhibit high vapor pressure under ambient conditions and have low boiling points. Although VOCs contribute only a small proportion of the total metabolites produced by living organisms, they play an important role in chemical ecology specifically in the biological interactions between organisms and ecosystems. VOCs are also important in the health care field as they are presently used as a biomarker to detect various human diseases. Information on VOCs is scattered in the literature until now; however, there is still no available database describing VOCs and their biological activities. To attain this purpose, we have developed KNApSAcK Metabolite Ecology Database, which contains the information on the relationships between VOCs and their emitting organisms. The KNApSAcK Metabolite Ecology is also linked with the KNApSAcK Core and KNApSAcK Metabolite Activity Database to provide further information on the metabolites and their biological activities. The VOC database can be accessed online.
    MeSH term(s) Data Mining/methods ; Database Management Systems ; Databases, Chemical ; Natural Language Processing ; Pattern Recognition, Automated/methods ; Periodicals as Topic ; Volatile Organic Compounds/chemistry ; Volatile Organic Compounds/classification ; Volatile Organic Compounds/metabolism
    Chemical Substances Volatile Organic Compounds
    Language English
    Publishing date 2015
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2698540-8
    ISSN 2314-6141 ; 2314-6133
    ISSN (online) 2314-6141
    ISSN 2314-6133
    DOI 10.1155/2015/139254
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: In-vitro diagnosis of single and poly microbial species targeted for diabetic foot infection using e-nose technology.

    Yusuf, Nurlisa / Zakaria, Ammar / Omar, Mohammad Iqbal / Shakaff, Ali Yeon Md / Masnan, Maz Jamilah / Kamarudin, Latifah Munirah / Abdul Rahim, Norasmadi / Zakaria, Nur Zawatil Isqi / Abdullah, Azian Azamimi / Othman, Amizah / Yasin, Mohd Sadek

    BMC bioinformatics

    2015  Volume 16, Page(s) 158

    Abstract: Background: Effective management of patients with diabetic foot infection is a crucial concern. A delay in prescribing appropriate antimicrobial agent can lead to amputation or life threatening complications. Thus, this electronic nose (e-nose) ... ...

    Abstract Background: Effective management of patients with diabetic foot infection is a crucial concern. A delay in prescribing appropriate antimicrobial agent can lead to amputation or life threatening complications. Thus, this electronic nose (e-nose) technique will provide a diagnostic tool that will allow for rapid and accurate identification of a pathogen.
    Results: This study investigates the performance of e-nose technique performing direct measurement of static headspace with algorithm and data interpretations which was validated by Headspace SPME-GC-MS, to determine the causative bacteria responsible for diabetic foot infection. The study was proposed to complement the wound swabbing method for bacterial culture and to serve as a rapid screening tool for bacteria species identification. The investigation focused on both single and poly microbial subjected to different agar media cultures. A multi-class technique was applied including statistical approaches such as Support Vector Machine (SVM), K Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA) as well as neural networks called Probability Neural Network (PNN). Most of classifiers successfully identified poly and single microbial species with up to 90% accuracy.
    Conclusions: The results obtained from this study showed that the e-nose was able to identify and differentiate between poly and single microbial species comparable to the conventional clinical technique. It also indicates that even though poly and single bacterial species in different agar solution emit different headspace volatiles, they can still be discriminated and identified using multivariate techniques.
    MeSH term(s) Algorithms ; Bacteria/classification ; Bacteria/genetics ; Bacteria/isolation & purification ; Biosensing Techniques ; Data Mining ; Diabetic Foot/diagnosis ; Diabetic Foot/microbiology ; Discriminant Analysis ; Electronic Nose ; Gas Chromatography-Mass Spectrometry ; Humans ; In Vitro Techniques ; Neural Networks (Computer) ; Odorants/analysis ; Support Vector Machine
    Language English
    Publishing date 2015-05-14
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2041484-5
    ISSN 1471-2105 ; 1471-2105
    ISSN (online) 1471-2105
    ISSN 1471-2105
    DOI 10.1186/s12859-015-0601-5
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Clustering of 3D-Structure Similarity Based Network of Secondary Metabolites Reveals Their Relationships with Biological Activities.

    Ohtana, Yuki / Abdullah, Azian Azamimi / Altaf-Ul-Amin, Md / Huang, Ming / Ono, Naoaki / Sato, Tetsuo / Sugiura, Tadao / Horai, Hisayuki / Nakamura, Yukiko / Morita Hirai, Aki / Lange, Klaus W / Kibinge, Nelson K / Katsuragi, Tetsuo / Shirai, Tsuyoshi / Kanaya, Shigehiko

    Molecular informatics

    2014  Volume 33, Issue 11-12, Page(s) 790–801

    Abstract: Developing database systems connecting diverse species based on omics is the most important theme in big data biology. To attain this purpose, we have developed KNApSAcK Family Databases, which are utilized in a number of researches in metabolomics. In ... ...

    Abstract Developing database systems connecting diverse species based on omics is the most important theme in big data biology. To attain this purpose, we have developed KNApSAcK Family Databases, which are utilized in a number of researches in metabolomics. In the present study, we have developed a network-based approach to analyze relationships between 3D structure and biological activity of metabolites consisting of four steps as follows: construction of a network of metabolites based on structural similarity (Step 1), classification of metabolites into structure groups (Step 2), assessment of statistically significant relations between structure groups and biological activities (Step 3), and 2-dimensional clustering of the constructed data matrix based on statistically significant relations between structure groups and biological activities (Step 4). Applying this method to a data set consisting of 2072 secondary metabolites and 140 biological activities reported in KNApSAcK Metabolite Activity DB, we obtained 983 statistically significant structure group-biological activity pairs. As a whole, we systematically analyzed the relationship between 3D-chemical structures of metabolites and biological activities.
    Language English
    Publishing date 2014-12
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 2537668-8
    ISSN 1868-1751 ; 1868-1743
    ISSN (online) 1868-1751
    ISSN 1868-1743
    DOI 10.1002/minf.201400123
    Database MEDical Literature Analysis and Retrieval System OnLINE

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