Background: Methylation is one of the most important post-translational modifications in the human body which usually arises on lysine among.the most intensely modified residues. It performs a dynamic role in numerous biological procedures, such as regulation of gene expression, regulation of protein function and RNA processing. Therefore, to identify lysine methylation sites is an important challenge as some experimental procedures are time-consuming. Objective: Herein, we propose a computational predictor named iMethylK-PseAAC to identify lysine methylation sites. Methods: Firstly, we constructed feature vectors based on PseAAC using position and composition relative features and statistical moments. A neural network is trained based on the extracted features. The performance of the proposed method is then validated using cross-validation and jackknife testing. Results: The objective evaluation of the predictor showed accuracy of 96.7% for self-consistency, 91.61% for 10-fold cross-validation and 93.42% for jackknife testing. Conclusion: It is concluded that iMethylK-PseAAC outperforms the counterparts to identify lysine methylation sites such as iMethyl-PseACC, BPB-PPMS and PMeS.

: Carboxylation is one of the most biologically important post-translational modifications and occurs on lysine, arginine and glutamine residues of a protein. Among all these three, the covalent attachment of the carboxyl group with the lysine side chain is the most frequent and biologically important type of carboxylation. The lysine residues play an important part in catalytic reactions, calcium absorption and muscle protein construction. For studying such biological functions, it is essential to correctly determine the lysine sites sensitive to carboxylation. Experimental determination through site-directed mutagenesis and high throughput mass spectrometry is a costly and time taking process, thus, the reliable computational model is required for identification of carboxylysine sites. In this paper, we present a computational model for the prediction of the carboxylysine site which is based on machine learning. Training of the model is performed by a neural network using experimentally verified and updated data. Statistical moments have been used to train a neural network. The model is validated by jackknife, cross-validation, self-consistency and independent testing. Accuracy determined through validation was 93.93% for jackknife test, 95.16% for cross-validation, 94.3% for self-consistency and 94.3% for independent testing. The proposed model has better performance as compared to the existing model PreLysCar, however, the accuracy can be improved further, in future, due to increasing number of carboxylysine sites in proteins.

This research paper is based on investigating the factors affecting the purchasing decision of counterfeit product consumers in the city of Karachi. The study was conducted through convenient sampling by developing a questionnaire based on behavioural as well as demographic questions. The questionnaire was circulated through the social mediums and feedback was collected to carry out statistical tests for the conclusion purpose. A total of 87 responses were received through the survey questionnaire. Descriptive statistics and regression analysis were conducted to deduce quantitative inferences. The findings of the study will be helpful in planning and executing the production of CF products. The study provides data related to the buying behaviour of the CF product’s consumers and the scale of sales as to how often they are sold. The study also helps focus on the need for modifications in CF products as to how much extent the consumers want modifications. This will also help the firms or brands producing CF products to launch modified versions of CF products with competitive prices, availability, and customer preferences.

Background: Different pharmaceutical topical agents are available in the market for the treatment of fungal infections. A simple, precise, and cost-effective RP-HPLC method was developed and validated for the determination of hydrocortisone and clotrimazole simultaneously in a topical cream dosage form. Chromatographic separation was done on USP L1 (250 × 4.6) mm column with a particle size of 5 μm. The mobile phase employed for this study consists of acetonitrile and buffer in the ratio of 75:25, respectively. The flow rate was kept at 1 mL per minute. The detection of the drug was carried out at 254 nm using a UV detector. The retention times of HYD and CL were 3.0 min and 7.3 min, respectively. Result: The method is developed and validated for linearity, precision, specificity, accuracy, and robustness. Conclusion: The stability of finished products gives us knowledge about the effect of different environmental factors like humidity, light, and temperature, and these factors give us information about the quality of finished products.

Research in the analysis of cytokine plays an important role because of the importance that cytokine has in the treatment and analysis of disease, but the current method for cytokine identification have numerous weaknesses, such as low affectability and low F-score. In this paper we purposed a new prediction method by consolidating the protein place explicit propensity into general type of pseudo amino acid sequences. Our predictor model has used CSM, PRIM, RPRIM, FMD, AAPIV, RAAPIV based on ANN or RFF algorithm to compute the Accuracy, Sensitivity, Specificity and MCC which are 96.28%,88.96%,99.94%,91.73% respectively using 10-fold cross validation. RFA shows 96.28% result. Our model has given the more accuracy other than research models using SVM.