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.
حاجی موسیٰ میاں سملکی سملک ڈابھیل کی ایک اطلاع سے یہ معلوم ہوکر افسوس ہواکہ جوہانسبرگ (جنوبی افریقہ) کے مشہور ومعروف صاحبِ خیر بزرگ جناب محترم حاجی موسیٰ میاں صاحب سملکی کی وفات ہوگئی۔مرحوم ہمارے محبِ قدیم جناب مولانا الحاج محمدموسیٰ صاحب سملکی کے والد بزرگوار تھے۔ بہت بڑے صاحبِ ثروت ہونے کے باوجود اول درجہ کے مسلمان، صورت وسیرت میں پہلے بزرگوں کا نمونہ، بڑے باحوصلہ ،بڑے صاحبِ خیر تھے،علمی اورمذہبی کاموں میں دل کھول کر حصہ لیتے تھے۔ڈابھیل کے عربی مدرسہ کوبرسوں تک ایک ہزار روپیہ ماہانہ دیتے رہے۔ دارالعلوم دیوبند کے دارالطلبہ کے بہت سے کمرے سب سے پہلے انھوں نے تعمیر کرائے تھے۔حضرت الاستاذ علامہ سیدمحمد انورشاہ صاحبؒ کے علوم کی خدمت کے لیے مجلس علمی ڈابھیل کی بنیاد آپ ہی کی توجہ سے پڑی جوآج بھی ایک مفید عربی تالیفی ادارے کی حیثیت سے بہت اچھا کام کررہی ہے۔ دعا ہے حق تعالیٰ مرحوم کو اپنے دامن ِ رحمت میں لے لے۔ ہم اس صدمہ میں مرحوم کے جانشین صادق مولانا الحاج محمد موسیٰ میاں اور ان کے متعلقین کے ساتھ شریک ہیں اور یقین ہے کہ موصوف اپنے والدِ ماجد کی روایات کو ہمیشہ زندہ و تازہ رکھیں گے۔ [مارچ ۱۹۴۴ء]
During the Dark middle ages of Europe, The Holy Prophet Muhammad (PBUH) established the first ever Islamic state, in the Arab soil, at Medinah. The successors of the Prophet, known as Khulfa-i- Rashideen (the Glorious Caliphs) not only maintained it rather they extended with further development. The Caliphate was not only a model statefor the world but also a unique one with respect to its political appratus, principles and the governance. This paper discovers the same uniqueness of the Caliphate in past and modern perspective.
This study investigated the issue of corporate social responsibility (CSR) in context of small and medium sized enterprises (SMEs) of Khyber Pakhtunkhwa (KP). The study focused on exploring CSR related activities of SMEs in the province and their underlying motivations. Moreover, major barriers faced by SMEs in addressing social, economic, and environmental concerns are identified. Mixed-method research methodology is used in two phases of research. The first phase of the research was based on qualitative study which helped in exploring practices of CSR in SMEs along with motivations and barriers. In-depth interviews were conducted from managers and owners of SMEs. Moreover, through adopting ground theory approach, the rich information from interviews was analyzed to extract emerging themes. Qualitative study helped in exploring six major themes related to CSR practice, three major themes on motivations of the CSR, and two themes on barriers of CSR in SMEs. Furthermore, a questionnaire was constructed based on themes emerged during first phase of the study and a conceptual model was formed with relational hypothesis. In the second phase, quantitative methodology is used to test the validity of developed instrument and hypotheses testing. A randomly selected sample of 469 respondents from SMEs was subjected to analysis. Unifactoriality of the constructs was tested with Principal Component Analysis. The results of tests indicated that items related to each construct correlate with each other and hence provide a sufficient evidence of constructs validity. In order to test the relational hypothesis, structural equation modeling (SEM) technique is deployed.