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پروفیسر ٹی گجر

پروفیسر ٹی گجر

            پروفیسر ٹی گجر، جنھوں نے ماہِ گذشتہ میں وفات پائی، مغربی ہندوستان کے بہترین ماہر کیمیائیات تھے، ان کی سائنٹفک اور کیمیاوی عظمت یورپ کے علمی حلقوں میں مسلّم تھی، اور ان کے بعض کارنامے ان کے معاصرین کے لیے باعثِ رشک تھے۔

(ستمبر ۱۹۲۰ء)

رسول اللہ ﷺ کے اشارات: معنوی اور صوری تفہیم

The of life Holy source primary Prophet Muhammad (صلى الله عليه وسلم) is a guidance for Muslim Ummah. Prophetic guidance is not restricted to theverbal instructions only but he has at times used the Gestures to expressand explain the things. This is a significant area of Hadith sciences whichHis. (صلى الله عليه وسلم) Prophet the of Language body and gestures the with dealscompanions (R. A) not only preserved his verbal instructions, dictatedwords, silent approvals, and actions of their beloved Prophet (ﷺ) but theyalso preserved his (ﷺ) gestures and body language. This paper discussesthe Gestures of the Holy Prophet with special reference to their virtual andvisual interpretations. The purpose of this research is to critically analyzethe Gestures of the Holy prophet and their importance in communicatingthe message to the audience. The method used for this research paper isdescriptive and analytical. The analysis of the prophetic traditions revealed(صلى الله عليه وسلم) Prophet where Hadith in examples significant been have there thatused the gestures to explain his words and thoughts which helped inconveying the message. Visual explanations and diagrams of some of thegestures have also been included in the article to explore and highlight theirsignificance. This article reveals that use of gestures is helpful incommunicating the message to the audience, and this aspect of Sīrah mustbe utilized during interactive sessions and verbal discussions. Furthermore, current research paper recommends that adequate body language andGestures are the vital means of teaching, tablīgh, and successfulcommunication in the light of Sīrah studies.

Prediction of Membrane Proteins Using Machine Learning Approaches

Membrane proteins are the basic constituent of a cell that manage intra and extracellular processes of a cell. About 20-30% of genes of eukaryotic organisms are encoded from membrane proteins. In addition, almost 50% of drugs are directly targeted against membrane proteins. Owing to the significant role of membrane proteins in living organisms, the identification of membrane proteins with substantial accuracy is essential. However, the annotation of membrane proteins through conventional methods is difficult, sometimes even impossible. Therefore, membrane proteins are predicted from topogenic sequences using computational intelligence techniques. In this study, we conducted our research in two phases regarding the prediction of membrane protein types and structures. In Phase-I, regarding the prediction of membrane protein types, four different ways are explored in order to enhance true prediction. In the first part of phase-I, membrane protein types are predicted using Composite protein sequence representation followed by the application of principal component analysis in conjunction with individual classifiers. In the second part, the notion of ensemble classification is utilized. In part three, an error correction code is incorporated with Support Vector Machine using evolutionary profiles (Position Specific Scoring Matrix) and SAAC based features. Finally, in part four, a two-layer web predictor Mem- PHybrid is developed. Mem-PHybrid accomplishes the prediction in two steps. First, a protein query is identified as a membrane or a non-membrane protein. In case of membrane protein, then its type is predicted. In the second phase of this research, the structure of membrane protein is recognized as alpha-helix transmembrane or outer membrane proteins. In case of alpha- helix transmembrane proteins, features are explored from protein sequences by two feature extraction schemes of distinct natures; including physicochemical properties and compositional index of amino acids. Singular value decomposition is employed to extract high variation features. A hybrid feature vector is formed by combining the different types of features. Weighted Random Forest is then used as a classification algorithm. On the other hand, in case of outer membrane proteins, protein sequences are represented by Amino acid composition, PseAA composition, and SAAC along with their hybrid models. Genetic programming, K-nearest neighbor, and fuzzy K-nearest neighbor are adopted as classification algorithms. Through the simulation study, we observed that the prediction performance of our proposed approaches in case of both types and structures prediction is better compared to existing state of the arts/approaches. Finally, we conclude that our proposed approach for membrane proteins might play a significant role in Computational Biology, Molecular Biology, Bioinformatics, and thus might help in applications related to drug discovery. In addition, the related web predictors provide sufficient information to researchers and academicians in future research.
Asian Research Index Whatsapp Chanel
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