Skin cancer, particularly melanoma, has been recognized as one of the most lethal forms of cancer. Detecting and diagnosing skin lesions accurately can be challenging due to the striking similarities between the various types of skin lesions, such as melanoma and nevi, especially when examining the color images of the skin. However, early diagnosis plays a crucial role in saving lives and reducing the burden on medical resources. Consequently, the development of a robust autonomous system for skin cancer classification becomes imperative. Convolutional neural networks (CNNs) have been widely employed over the past decade to automate cancer diagnosis. Nonetheless, the emergence of the Vision Transformer (ViT) has recently gained a considerable level of popularity in the field and has emerged as a competitive alternative to CNNs. In light of this, the present study proposed an alternative method based on the off-the-shelf ViT for identifying various skin cancer diseases. To evaluate its performance, the proposed method was compared with 11 CNN-based transfer learning methods that have been known to outperform other deep learning techniques that are currently in use. Furthermore, this study addresses the issue of class imbalance within the dataset, a common challenge in skin cancer classification. In addressing this concern, the proposed study leverages the vision transformer and the CNN-based transfer learning models to classify seven distinct types of skin cancers. Through our investigation, we have found that the employment of pre-trained vision transformers achieved an impressive accuracy of 92.14%, surpassing CNN-based transfer learning models across several evaluation metrics for skin cancer diagnosis.

Background: Submucous cleft palate is characterized by muscular diastasis of the velum in the presence of intact mucosa with variable combinations of bifid uvula and hard palatal defect. Submucous cleft palate is indicated as a separate entity in most previous classifications but it has never been properly classified on an anatomical basis. Objectives: To revise the Smith-modified Kernahan ‘Y’ classification of cleft lip and palate deformities, and to describe the different anatomical subtypes of submucous cleft palate. Methods: The present study was conducted in Hayatabad Medical Complex, Abasin Hospital and Aman Hospital Peshawar, Pakistan, from November 2010 to December 2011. All patients who presented to the outpatient departments with cleft lip and palate, with the exception of previously operated cases, were included. All cases were described according to the Smith-modified Kernahan ‘Y’ classification and the authors' revised Smith-modified Kernahan ‘Y’ classification. All of the data were organized and analyzed using SPSS version 17 (IBM Corporation, USA). Results: A total of 163 cases of cleft lip and palate deformities were studied, of which 59.5% were male and 40.5% were female. Smith modification of the Kernahan ‘Y’ classification completely described the cleft deformities in 93.9% of patients. However, while the Kernahan ‘Y’ classification represented the submucous cleft palate, it did not describe its different anatomical subtypes in 6.13% of patients. The revised Smith-modified Kernahan ‘Y’ classification completely described the cleft deformities of the entire study population, including the different submucous cleft palate patients. Discussion: The Smith alphanumeric modification of the Kernahan ‘Y’ classification of cleft lip and palate came into existence after a long search and a series of modifications over the past century. This classification system describes the cleft region, site of the cleft, degree of the cleft, rare and asymmetrical clefts, and are computer database friendly. However, this classification did not describe the different anatomical subtypes of submucous cleft palate that have variable relationships with velopharyngeal insufficiency. Conclusion: The revised Smith-modified Kernahan ‘Y’ classification described in the present study can describe all types of cleft lip and palate deformities in addition to the different types of submucous cleft palate deformities.

Promoters are important regulatory elements in the genome that control gene expression, and their abnormalities have been linked to various diseases. With the development of high-throughput sequencing techniques, there is a need for computational methods to identify promoters in large amounts of DNA sequence data. However, many existing methods rely on a single feature representation approach, which can potentially lead to information loss. In order to address this issue. We proposed a computational model iPro-TCN that combines Temporal Convolutional Network (TCN) with a word2vec feature representation. This model includes a new feature descriptor called K-mer word vector and has been shown to have high sensitivity and accuracy in distinguishing promoters, including strong and weak promoters. The iPro-TCN model was evaluated on a benchmark dataset and was able to achieve good performance in both promoter identification and promoter strength prediction. Specifically, the model had an accuracy of 91.86% in the first layer for promoter identification, and an accuracy of 84.63% in the second layer for promoter strength prediction. These results suggest that the iPro-TCN model is a strong performer in predicting promoter sequences and promoter strength in DNA. These results are far higher than the existing best predictor, which indicates that our model showcases a better performance compared with the existing approaches.

The 5-Methyluridine (m5U), predominantly present in RNA and especially enriched in transfer RNA (tRNA), significantly enhances translational accuracy and protein synthesis by ensuring precise genetic information decoding and optimal tRNA functionality within cellular mechanisms. The identification of m5U modification sites is crucial, as this modification has gained significant attention in diseases such as breast cancer, stress response, and viral infections, offering insights into its molecular mechanisms and regulatory functions in disease contexts. Nevertheless, due to the arduous nature, intricate procedures, reliance on sophisticated and expensive instrumentation, and the need for specialized expertise, conventional biochemical approaches for identifying m5U modification sites result in substantial resource expenditures and notable temporal investments. Consequently, the pressing need for a precise and efficient computational method highlights the urgency for alternative approaches in identifying m5U modification sites. In this study, we introduce a novel computational approach called “Deep-m5U,” which combines the strengths of Convolutional Neural Networks (CNNs) and tetranucleotide composition to accurately identify methyluridine modification sites and improve overall performance. The developed Deep-m5U method leverages CNNs to accurately detect protein-coding regions aand capture relevant motifs, while incorporating tetra-nucleotide composition to capture global compositional characteristics, resulting in a more robust model that significantly enhances performance. We evaluated the Deep-m5U model on two publicly available benchmark datasets: the full transcript and mature mRNA datasets. Our results showcase superior performance, achieving accuracies of 91.26% and 95.63% respectively, surpassing the current cutting-edge methods. Moreover, the open-source code for Deep-m5U is freely accessible at: https://github.com/waleed551/Deep-m5U .