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Expression of Bacterial Cellulose Synthase Bcs Gene in Cotton

Thesis Info

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Author

Sidra Akhtar

Program

PhD

Institute

University of the Punjab

City

Lahore

Province

Punjab

Country

Pakistan

Thesis Completing Year

2019

Thesis Completion Status

Completed

Subject

Molecular Biology

Language

English

Link

http://prr.hec.gov.pk/jspui/bitstream/123456789/12408/1/Sidra%20Akhtar_Molecular%20Bio_2019_UoPunajb_PRR.pdf

Added

2021-02-17 19:49:13

Modified

2024-03-24 20:25:49

ARI ID

1676726170141

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Cotton fibers serve as the chief raw material for the textile industry. Cost of production and requirement of high-quality products is the main challenge encountered by the textile industry in Pakistan. Fiber quality parameters that have a major impact on the textile processing include fiber length, fiber strength and fiber fineness. Efforts to improve cotton fiber quality have been underway since many years. Out of all the approaches used so far, genetic improvement in cotton through Agrobacterium mediated transformation has proved to be the most feasible and productive technique in enhancing the fiber quality of various cotton cultivars. Cotton fibers are chiefly composed of cellulose therefore, any change in the cellulose synthesis within the fiber can affect the fiber quality to a great extent. Due to this fact, this study was aimed to explore the expression of bacterial cellulose synthase (Bcs) genes, namely acsA and acsB (GenBank ID: X54676.1), under the regulation of fiber-specific promoter (GhSCFP) in a local cotton variety (CEMB-00) of Gossypium hirsutum. In silico studies were conducted to understand the structures of bacterial and cotton fiber cellulose synthase enzymes and to find compatibility and similarities among them. A number of domains were found to be highly conserved among the bacterial and cotton fiber cellulose synthases. These conserved domains were observed to be functionally essential for the process of cellulose synthesis in the bacterial cells of Gluconacetobacter xylinus as well as the cotton fiber. The 6586 bp fragment of Bcs gene cassette was isolated from the synthesized pUC57-Bcs construct through restriction digestion analysis and cloned into pCAMBIA 1301 vector under the regulation of the fiber-specific promoter. The recombinant plant expression vector, pCAMBIA-Bcs, was introduced into the local non-transgenic cotton variety of Gossypium hirsutum via shoot apex method of Agrobacterium-mediated transformation which resulted in the transformation efficiency of 1.27%. Four out of ten putative transgenic plants, which were PCR positive for both acsA and acsB genes, were selected on the basis of improved fiber quality and better growth potential to be further analyzed through the molecular and biochemical analysis in the advanced generations. Quantitative expression of the acsA and acsB genes in the T1 generation transgenic plant lines was many folds higher at 35 DPA (during the secondary cell wall synthesis phase of fiber) as compared to expression at 10 DPA (during the elongation phase of fiber). Fiber analysis of the transgenic cotton plant lines showed up to 17.52% increase in fiber length, up to 26.45% increase in fiber strength and up to 22.45% increase in the cellulose contents as compared to the control (non-transgenic) plant line. SEM analysis showed that the transgenic fibers had smoother surfaces with more number of twists as compared to the control fibers. Improvement in some of the morphological traits was also observed in the transgenic plant lines showing an increase of 28.06-32.21% in plant height, 28-40% increase in number of bolls per plant and 31.21-44.57% increase in yield per plant. Furthermore, karyotyping results obtained through FISH analysis revealed the presence of single copy number of the transgenes on the chromosome no. 11 in the transgenic plant line S-00-16 and on chromosome no. 13 in the transgenic plant line S-00-11, during the metaphase. In light of the results obtained, it can be concluded that the transformation of the acsA and acsB genes in cotton fiber resulted in enhanced fiber quality on the basis of analyses performed on a small number of T1 plants. In order to meet the high standards of the cotton fiber quality for the national textile industry, replicated experimentation is required on a larger scale before firm conclusions can be made on parameters such as yield and fiber processing quality.
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نہال سیوہاروی

نہالؔ سیوہاروی
جناب نہال ؔ سیوہاروی کی شہرت کاآغاز ’’برہان کے شاعر خاص‘‘ کی حیثیت سے ہوا جس میں تقسیم ہند سے قبل وہ بالا لتزام غزلیں اورنظمیں لکھتے رہے۔ مرحوم کاذوقِ شعر وسخن اور ملکۂ شعر گوئی فطری اوروہبی تھاجس کوانھوں نے خلاف طبع معمولی سی سرکاری ملازمت کے باوجود مسلسل مطالعہ اور مشق و مزاولت کے ذریعہ جلا دے کراتنا اجاگرکرلیا تھاکہ ان کاشمار پختہ کار اورصاحب فن اساتذہ کے زمرہ میں ہونے لگاتھا۔ ان کے کلام میں دردواثر، سوزو گداز، عمقِ خیال،نزاکتِ احساس اورلطافت وشستگیِ بیان، جوحسنِ شعر کی جان ہیں یہ سب اوصاف پائے جاتے تھے۔ علاوہ سینکڑوں منتشر غزلوں اورنظموں کے آزادی پران کی رباعیات کاایک مجموعہ مکتبۂ برہان سے اور نظموں اورغزلوں کا ایک مجموعہ ’’شباب و انقلاب‘‘ کے نام سے دلی کے ہی کسی ایک مکتبہ کی طرف سے شائع ہوچکے ہیں۔
؂ حق مغفرت کرے عجب آزاد مرد تھا
[جنوری ۱۹۵۲ء]

Understanding the anti-Mughal Struggle of Khushal Khan Khattak

Khushal Khan Khattak, a seventeenth century Pakhtun writer, poet and swordsman, and his forefathers had served the Mughal for a long time. However, his fortune took a sudden twist when Mughal Emperor Aurangzeb imprisoned him in 1664, and kept him in solitary confinement at Ranthambore fort. After his release from prison, Khushal Khan was a different person. He remained no more a loyal Mughal official afterwards. Although, Aurangzeb Alamgir and a number of Mughal governors of Kabul tempted him several time to accept a position in the frontier areas but he out-rightly declined. This transformation is clearly visible in his poetry. He took up arms against the Mughals in 1673 and declared a war against them despite the fact that some of his family members even his son had sided with the Mughals. He continued his anti-Mughal struggle till his death in 1689. Some of the critics look at the antiMughal role of Khushal Khan with suspicion and have raised a few queries in this connection. This study looks into the circumstances that saw transformation in his outlook towards the Mughals. Then it explores, whether it was a personal vendetta or the start of a collective anti-Mughal Pakhtun struggle. The article looks into various dimensions, nature and direction of his struggle. This research paper is an attempt to evaluate objectively as to why and how Khushal Khan joined the anti-Mughal camp in the borderland area. Some more related questions are also discussed in details in this article.

Adaptive Genetic Algorithms: Simulation-Based Optimization Techniques

In the last three decades, engineers and decision makers expressed a growing interest in the development of effective modeling and simulation methods to understand and predict the behavior of many phenomena in engineering and science. Many of these phenomena are translated into mathematical models for convenience and comparatively elaborative interpretation. Methods commonly employed for this purpose include, neural networks, simulated annealing, particle swarm optimization, ant colony optimization, tabu search, genetic algorithms, and many more. These methods search for the optimal or near optimal values of parameters of a model built a priori. But for such a case, a suitable model should be well known beforehand. When this is not the case, the problem can be seen from another angle where the goal is to find a program or a mathematical representation which can solve the problem. According to this idea the modeling step is performed automatically thanks to a quality criterion which drives the building process. The aim of the research, presented in this thesis, is to use genetic algorithms for large scale optimization and Non-deterministic polynomial (NP-hard) problems. More specifically, binary-based representation of genetic algorithm is used for non-convex function optimization. The path-based representation is used to solve one of the combinatorial optimization problems i.e. traveling salesman problem. Genetic algorithm is meta-heuristic optimization approach based on the principles and mechanisms of natural evolution and can be used to solve problems with higher order of difficulty developed by John Holland. There are three main operators; selection, crossover and mutation for running this algorithm. In this thesis, we focus mainly on crossover operators but a significant consideration is also given to selection operator as well. In third chapter, we develop a selection procedure which gives a reasonable opportunity to worst individuals along with the best ones. Also in chapter five, a new version of rank-based selection operator which is a fine tradeoff between exploration and exploitation is introduced. The effectiveness and the stability of the proposed selection schemes are then evaluated using a wide range of benchmark instances and the solutions are compared and cross checked with the results published in the relevant peer reviewed literature. Rest of the thesis focuses on two crossover representations: binary for function optimization and permutation for traveling salesman problems. All the developed crossover operators showed an improved and significant performance of the genetic algorithm with fewer generations and lower convergence time in achieving optimal solutions. The operators used are capable of introducing new fitter offspring and without being trapped in a local optimum. Therefore it can be stated that all the proposed operators are efficient to solve non-convex benchmark functions and NP-hard problems like traveling salesman problem. We used MATLAB software to compare the performance of all new operators with existing ones. In this thesis, we provide pseudo-codes for all new developed operators along with those that are used for comparison in our study.