عارفؔ عباسی
عارف عباسی بھی اﷲ کو پیارے ہوگئے، وہ اس دور کے ممتاز غزل گو اور جگر کے طرز کے کامیاب مقلد تھے، انہوں نے اپنی ظاہری وضع و قطع بھی انہی کی جیسی بنالی تھی، ان کا مکان اعظم گڑھ سے متصل ضلع ملبا میں تھا اس لئے اعظم گڑھ میں اُن کے پرانے تعلقات تھے اور یہاں برابر اُن کی آمدورفت رہتی تھی پہلے جب اعظم گڑھ آنا ہوتا تھا تو دارالمصنفین ضرور آتے تھے، اور اپنے تازہ کلام سے محظوظ کرتے تھے، مگر ادھر کچھ دنوں سے اس وضعداری میں فرق آگیا تھا، عرصہ تک اُن کی غزلیں معارف میں چھپتی رہیں، اُن کے تعزل میں بڑی لطافت و پاکیزگی تھی ابتدا میں راجہ صاحب نانپارہ کے لڑکوں کے اتالیق رہے تھے، اس لئے درباری آداب اور علم مجلس کے بڑے ماہر تھے، اُن کی عمر ساٹھ باسٹھ سال کی رہی ہوگی، ادھر کچھ دنوں سے کچھ قلبی شکایت ہوگئی تھی، اسی نے مرض الموت کی شکل اختیار کرلی، اﷲ تعالیٰ ان کی مغفرت فرمائے۔ (شاہ معین الدین ندوی،نومبر ۱۹۷۱ء)
Plagiarism is a serious offense that defies the ethics of scholarship and research. Research students need to pay substantive attention to the dynamics and contours of plagiarism in their creative, ethical, and academic endeavors. Scholarship avenues such as online tutorials and work assignments are important sources of instructions for plagiarism-avoidance among students. The current study explores the frequency of consultation of scholarship avenues and the usage of plagiarism-avoidance techniques among research students in social sciences. The study also recommends a scale to investigate plagiarism-avoidance techniques. Furthermore, it also examines the level of the study in predicting the usage of plagiarism-avoidance. Using the online survey technique, 108 research students from Pakistan were sampled. The questionnaire was uploaded on several student-based research groups of social media, including; Facebook, and Yahoo groups. Bivariate linear regression analysis was used for hypothesis testing. Findings revealed that scholarship avenues lead to greater usage of plagiarism-avoidance techniques among research students (R2 =0.065). Supervisors, class-fellows, colleagues, and faculty of the department are prominent human scholarship avenues. Similarly, articles and books from the web, books from the library, the anti-plagiarism policy of the Higher Education Commission (HEC), and lectures delivered in the classroom were leading informational scholarship avenues. Stage of the study and consultation of the scholarship avenues were predictors of usage of plagiarism-avoidance techniques. It is recommended that (i) plagiarism-avoidance is promoted through prevention rather than detection, and that (ii) scholarship avenues (e.g. Delivering lectures, institutional policy, and interaction with relevant websites) are used for enhancing awareness about intellectual dishonesty.
G protein-coupled receptors (GPCRs) are located at the boundary of a cell, and are used for inter-cellular communications. They are mostly found in Eukaryotic cells; but can also be found in some Prokaryote cells. GPCRs modulate synaptic transmission in spinal cord and brain, and can trigger signaling pathways for the regulation of cell proliferation and gene expression. They are physiologically very important and according to an estimate, more than 50% of the marketed drugs target GPCRs. Computational prediction of unknown GPCRs has great importance in pharmacology because, malfunction of GPCRs can cause many diseases. The goal of this thesis is to propose new methods for the classification of GPCRs using Machine Learning approaches. The work in this thesis is divided into two parts. The first part is based on the classification of GPCRs using Machine Learning methods. We analyze biological, statistical, and transform-domain based feature extraction strategies and exploited various physiochemical properties to generate discriminate features of GPCR sequences. We have developed various GPCR classification methods. In the first method, GPCRs are predicted using the hybridization of pseudo amino acid composition and multi scale energy representation of physiochemical properties. In this method, our focus is on the introduction of various physiochemical properties (hydrophobicity, electronic and bulk property). In the second method, GPCRs are predicted using grey incidence degree measure and principal component analysis, whereby relation between various components of GPCR sequences is exploited. In the third method, we perform weighted ensemble classification of GPCRs using evolutionary information and multi-scale energy based features. The weights for each of the classifier are optimized using genetic algorithm, which provides an improvement in classification performance. Second part of the thesis is based on multiple sequence alignment of GPCRs, whereby, we utilize the structural information of GPCRs. The three-dimensional structures of several Rhodopsin like GPCRs have been resolved at atomic resolution and validates the prediction using sequence information alone that GPCRs fold has a bundle of seven transmembrane helices (TMs). The dataset is aligned initially using multiple sequence alignment methods and TMs are extracted. The dataset is composed of 19 sub families of Rhodopsin receptors, belonging to 62 species. Weights are assigned to avoid bias for a particular specie. Position specific scoring matrices (PSSM) are computed for the seven TMs data and pseudo counts are added. Pseudo 2counts are added using conventional Blosum62 scoring matrix. The unknown receptors are classified using PSSMs of the known receptors and by the TM similarity methods. Our research may have valuable contributions in the fields of Bioinformatics, Pattern Classification, and Computational Biology, and has yielded comparable results with the existing approaches. We conclude that our research may help the researchers in further exploring membrane protein classification or any other sub cellular localization classification.