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Background: The uncontrolled growth due to accumulation of genetic and epigenetic changes as a result of loss or reduction in the normal function of Tumor Suppressor Genes (TSGs) and Pro-oncogenes is known as cancer. TSGs control cell division and growth by repairing of DNA mistakes during replication and restrict the unwanted proliferation of a cell or activities, those are the part of tumor production. Objectives: This study aims to propose a novel, accurate, user-friendly model to predict tumor suppressor proteins, which would be freely available to experimental molecular biologists to assist them using in vitro and in vivo studies. Methods: The predictor model has used the input feature vector (IFV) calculated from the physicochemical properties of proteins based on FCNN to compute the accuracy, sensitivity, specificity, and MCC. The proposed model was validated against different exhaustive validation techniques i.e. self-consistency and cross-validation. Results: Using self-consistency, the accuracy is 99%, for cross-validation and independent testing has 99.80% and 100% accuracy respectively. The overall accuracy of the proposed model is 99%, sensitivity value 98% and specificity 99% and F1-score was 0.99. Conclusion: It concludes, the proposed model for prediction of the tumor suppressor proteins can predict the tumor suppressor proteins efficiently, but it still has space for improvements in computational ways as the protein sequences may rapidly increase, day by day.

Background: Likewise the fingerprints and palm prints, footprints are also helpful in solving a crime puzzle; however, very few studies have been reported targeting the identification of sex-based upon footprint features. Therefore, the present study aims at the identification of sex using footprint features from the population of Punjab, Pakistan. The foot measurements, i.e., toe length ratio, individual toe lengths, foot breadth, and foot index, are used as features for the identification of sex. Footprint samples were collected from 280 volunteers (142 males and 138 females) from all over Punjab (age range 18-50 years). A sex identification method is proposed in this study employing various machine learning algorithms, i.e., Naïve Bayes, J48, Random Forest, Random Tree, and REP Tree, and compared them. Results: The designed model was cross-validated using 10-fold cross-validation. The results demonstrated the varying accuracy of the machine learning algorithms, using different combinations of footprint features. However, the Naïve Bayes algorithm demonstrated an accuracy of 87.8%, for sex identification, using the combination of toe length and foot indexes. Conclusions: It is concluded that by using a combination of toe length and foot indexes and employing the Naïve Bayes algorithm, sex can be identified more accurately as compared to the other methods.

The use of natural dyes for textiles has attained attention due to their ecology, minimum impact on the environment and pollution. Therefore the objective of this study is to dye Lyocell fabric with natural dye extracted from orange peel for comparative analysis of colour efficiencies (K/S), CIE L*a*b* values and the colour fastness properties. The mordants applied were ferrous (II) sulphate and copper (II) sulphate. For the extraction of the dye, the aqueous extraction method was used. The pre-mordanting method was used and the dyeing effect on Lyocell fabric was analyzed at concentrations of 2% and 4%. It was observed that the mordant type employed had an influence on the colour efficiency and the colour coordinates of fabric dyed with Citrus aurantium dye. The colour efficiency (K/S: 4) and colour fastness to washing, light, rubbing and perspiration in all the dyed samples were better and excellent (grade 4–5) at 4% concentration. In overall results, the pre-mordanting method at 4% concentration gives the best results of colour efficiency and colour fastness properties. The performance analysis of colour fastness was also statistically significant at the 0.05 level.

Worldwide Oral carcinomas considerable problem. It is crucial to know the molecular incidences of molecular carcinogenesis, as it has a significant association with tobacco in Pakistani population. So, this study aimed to screen out the savage high risk of papilloma virus 16/18 and p16 in oral premalignant lesion and oral squamous cell carcinoma. Moreover, the p16 correlation between HPV and OPL and OSCC was also analyzed. Method: A total of 150 samples from the oral cavity were taken from the Hayatabad Medical complex (50 samples of OPL and 100 samples of OSCC).In this study we used immunohistochemistry to look for p16 in OPL and OSCC, and we used polymerase chain reaction to find HPV. SPSS 21 was used to input and evaluate the data. The relationship between HPVandp16withdifferentvariables was determined using Chi-square and Fisher exact tests. Results:For p16, 14% of 50 oral premalignant lesions were found positive and 86% were negative. Moreover, while screening for the OSCC, the 18 percent individuals were found positive for OSCC, while the remaining (82%) screened out negative. HPV was diagnosed in 6% of 50 OPL. The co-occurrence of HPV with p16 was found in all the 15 (100%) individual’s with a p value of 0.001. However, three of the 18 cases with p16 expression did not have HPV infection. Conclusion: The current research supports the use of p16 as a unique marker for human papilloma virus in oral squamous cell carcinomas. Furthermore, a chemical carcinogen like cigarettes is thought to be one of the main risk factors for p16 and HPV infection, as well as other things.