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Home > Sequencing and Analysis of a Cdna Encoding a Putative Coclaurine N-Methyltransferase Cnmt from Aristolochia Fimbriata, a Basal Angiosperm

Sequencing and Analysis of a Cdna Encoding a Putative Coclaurine N-Methyltransferase Cnmt from Aristolochia Fimbriata, a Basal Angiosperm

Thesis Info

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External Link

Author

Roshan Ali

Program

PhD

Institute

University of Peshawar

City

Peshawar

Province

KPK

Country

Pakistan

Thesis Completing Year

2010

Thesis Completion Status

Completed

Subject

Applied Sciences

Language

English

Link

http://prr.hec.gov.pk/jspui/handle/123456789/1342

Added

2021-02-17 19:49:13

Modified

2024-03-24 20:25:49

ARI ID

1676727160791

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Alkaloids are produced in plants through various pathways involving several enzymes that lead to diverse alkaloids. One of the most important alkaloid biosynthetic enzymes is coclaurine N-methyltransferase (CNMT) which is an S-adenosyl-L- methionine-dependent methyltransferase (SAM-MTase). SAM-MTases utilize S- adenosyl-L-methionine (SAM) as a cofactor to methylate other molecules. CNMT catalyzes the methylation of coclaurine. Crystal structures of more than hundred SAM-MTases have been investigated. Several O-methyltransferases have been characterized at the molecular as well as structural levels, but there have been very few molecular studies of N-methyltransferases especially about CNMTs. In this study, the amino acids sequence of Aristolochia fimbriata putative CNMT has been determined by isolating and translating the full-length cDNA. In order to investigate the mechanism of methylation by this putative CNMT, three-dimensional homology model has been built and the ligand (SAM) as well as the substrate (S- Coclaurine) has been docked into its active site. Phylogenetic analyses were performed using the MEGA 4.0 software. The phylogenetic relationship of A. fimbriata putative CNMT with their homologs has also been analyzed. In order to identify the putative CNMT gene and determine its function, online similarity searches were performed by BLAST program using the cDNA sequence as well as the putative protein that could be encoded by the gene. All the methods, applied, predicted that the gene identified might be involved in the production of CNMT. The predicted homology model consists of two domains: the N-terminal catalytic core domain and the C-terminal domain. The catalytic core domain has a central sheet of β-strands surrounded by α helices. The catalytic core domain contains binding site for VSAM. The C-terminal domain consists of alpha helices and a few beta sheets creating a pocket for the substrate in between them. The SAM-binding pocket is located next to substrate binding pocket and there is an opening in between these two cavities through which the methyl group of SAM projects towards the substrate. The most important residues involved in the methyl transfer reaction seem to be Tyr-79 and Glu-96.
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24. Al-Nur/The Light

24. Al-Nur/The Light

I/We begin by the Blessed Name of Allah

The Immensely Merciful to all, The Infinitely Compassionate to everyone.

24:01
This is a Surah that WE have sent down, and made its Injunctions mandatory to be applied in matters of ethics, mannerism, and morality,
and WE have sent down Clear Messages in it so that you may contemplate and enact.

24:02
The adulterous and fornicator female and the adulterous and fornicator male, where both are equally guilty of engaging in an extramarital relationship, flog each one of them a hundred lashes.
And let no compassion, pity, or sympathy for both of them keep you away from executing the command of Allah’s Religion, if you truly believe in Allah – the One and Only God of everyone, and the Last Hour.
And ensure that a group of believers witness their punishment.

24:03
After the punishment has been executed the adulterous or fornicator male will not marry anyone but an adulterous or fornicator female, or a polytheist female.
And the adulterous or fornicator female will only marry an adulterous or fornicator male, or a polytheist male.
But marrying someone guilty of adultery or fornication is forbidden for the believers.

24:04
And those who falsely accuse a chaste/respectable lady of an extramarital affair on circumstantial evidence but then do not bring four eye witnesses in support of their accusation,
then flog them eighty lashes,
and never accept their testimony ever again.
And those - they are the ones who have overstepped the limits.

24:05
Exceptions are those who later repent and reform themselves,
for sure, Allah is All-Forgiving, All-Compassionate.

24:06
And as for those...

Analysis of Factors Affecting Gillnet Fisherman Income 10 GT at Fish Auction Place, Indramayu Regency

The purpose of this study is to analyze the income level of gillnet fishermen <10 GT, to analyze the factors that affect the level of income, and to formulate strategies and solutions to increase income. This research was conducted at the Fish Auction Place (TPI) Pabean Udik, Pabean Udik Village, Indramayu Regency. This location was chosen because the Udik Customs Fish Auction Place (TPI) is dominated by fishermen using gillnet fishing gear who have vessels <10 GT. The method used is the census method with samples taken as a whole. The analysis used in this research is the Logistic Regression Analysis and the SWOT Analysis. Research results obtained by using IBM SPSS, namely for the Sig Value Variable in the Equation is 100%, this value is interpreted by 8 (factors) Independent variables can affect income only educational factors that do not affect income and for the results obtained from SWOT analysis namely to increase the income of gillnet fishermen <10 GT at TPI Pabean Udik, namely the development of technology and fisheries business scale, development of access to capital for fishermen, development of marketing access, development of facilities and infrastructure at TPI Pabean Udik, Development of diversification of fish management and environmentally friendly fisheries management.

Multi-Resolution Tranaform Based Feature Extraction Techniques for Differentiating Glioma Grades Using Mri Images

Medical image processing is one of the most attention gaining research areas that utilizes the technology for improving the quality of human life through a more precise and rapid diagnosis systems. This thesis focuses on computer assisted diagnosis of brain neoplasms which is amongst the most fatal cancers. Though, their exact cause is still unknown but early detection anddiagnosisofcorrectneoplasmtypeisveryimportantforpatient’slifeandfurthertreatment planning. Currently, the treatment of brain neoplasm depends on clinically observed symptoms, appearance of radiological tests, and often the microscopic examination of neoplasm’s tissues (histopathology or biopsy report). Magnetic Resonance Imaging (MRI) is the state of art technique to diagnose brain neoplasms and monitor their treatment. It provides a noninvasivewaytoimprovethequalityofthepatient’slifethroughamoreaccurateandfastdiagnosis and with minor side-effects, leading to an effective overall treatment. However, MRI does not provide any information about exact type and grade of neoplasm. The final decision is based on biopsy report of patient which is considered as gold standard, despite all risks associated with surgery to obtain a biopsy. With rapid advancement in technology, the researchers are continuously working on computerized techniques or computer assisted diagnostic tools to provide fast identification, correct diagnosis and effective treatment of brain neoplasm. The aimofthepresentthesisistodesign,implement,andevaluateasoftwareclassificationsystem fordiscriminatingthreegradesofbrainneoplasmonMRI.Limitedbrainneoplasmimagedata isoneofthebiggestissuesinthisresearchareabecausecollectionofthistypeofdatarequires years and years. Normally, we find studies working on images of some specific hospital orwebsite. Inaddition,directcomparisonofthesestudiesisnotpossiblebecauseeachstudyhad worked on different types of neoplasm and various sizes of image data. We have addressed this issue by proposing a new image cropping technique for handling images of different dimension for the same classifier. This new system is capable of handling image datasets from different institutions with various image sizes and resolutions for comparing, regulating and sharing of research. It is also observed, that lesser training and testing images in a particular class of neoplasm badly effect the classification accuracy. By using this generalized system, moreimagesamplesofaneoplasmclasscanbetakenfromotherinstitutionsorwebsitestoimprovetheclassificationaccuracy. ForclassificationofMRIimages,majorityoftheresearchers haveworkedonstatisticalfeaturesofneoplasmregionbutmulti-resolutiontransformsforfeature extraction, are not much explored. Besides this, classification of normal and pathological brain is mostly addressed but very few studies are found on multi-classification of different neoplasm types. The main objective of this thesis is to explore the performance of different multi-resolution transform based feature extraction techniques for multi-classification problem of brain neoplasm type (grade II, grade III and grade IV gliomas). Discrete Wavelet Transform (DWT) is one of the most popular multi resolution transform, extensively used as feature extraction technique for binary (normal vs abnormal brains) brain neoplasm classification systems. In this thesis, a stationary and time invariant Non Subsampled Contourlet Transform (NSCT) with Gray Level Co-occurrence Matrix (GLCM) is used for computation of feature vector in brain neoplasm classification system. This NSCT-GLCM based classification system is also compared with conventional DWT-GLCM based classification system, for the same experimental setup. It is found that NSCT-GLCM based system perform better than DWT-GLCM based system. For further improvement in neoplasm discrimination accuracy, in last algorithm, a multi resolution transform based hybrid feature extraction technique is introduced. This hybrid technique is comprised of conventional DWT, NSCT and GLCM. The quantitative performance analysis showed that hybrid feature extraction technique per formed much better than the previous two techniques (DWT-GLCM and NSCT-GLCM) with the highest accuracy of 88.88%. The developed brain neoplasm classification techniques can better assist the physician’s ability to classify and analyze pathologies leading for a more reliable diagnosis and treatment of disease.