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The KEAP1-NRF2 system plays a crucial role in responding to oxidative and electrophilic stress. Its dysregulation can cause the overexpression of downstream genes, a known cancer hallmark. Understanding and detecting abnormal KEAP1-NRF2 activity is essential for understanding disease mechanisms and identifying therapeutic targets. This study presents an approach that analyzes splicing patterns by a naive Bayes-based classifier to identify constitutive activation of the KEAP1-NRF2 system, focusing on the higher presence of abnormal splicing junctions as a subproduct of overexpression of downstream genes. Our splicing-based classifier demonstrated robust performance, reliably identifying activation of the KEAP1-NRF2 pathway across extensive datasets, including The Cancer Genome Atlas and the Sequence Read Archive. This shows the classifier's potential to analyze hundreds of thousands of transcriptomes, highlighting its utility in broad-scale genomic studies and provides a new perspective on utilizing splicing aberrations caused by overexpression as diagnostic markers, offering potential improvements in diagnosis and treatment strategies. [ABSTRACT FROM AUTHOR]

Many disease-associated genomic variants disrupt gene function through abnormal splicing. With the advancement of genomic medicine, identifying disease-associated splicing associated variants has become more important than ever. Most bioinformatics approaches to detect splicing associated variants require both genome and transcriptomic data. However, there are not many datasets where both of them are available. In this study, we develop a methodology to detect genomic variants that cause splicing changes (more specifically, intron retention), using transcriptome sequencing data alone. After evaluating its sensitivity and precision, we apply it to 230,988 transcriptome sequencing data from the publicly available repository and identified 27,049 intron retention associated variants (IRAVs). In addition, by exploring positional relationships with variants registered in existing disease databases, we extract 3,000 putative disease-associated IRAVs, which range from cancer drivers to variants linked with autosomal recessive disorders. The in-silico screening framework demonstrates the possibility of near-automatically acquiring medical knowledge, making the most of massively accumulated publicly available sequencing data. Collections of IRAVs identified in this study are available through IRAVDB (https://iravdb.io/). This paper proposed a novel in-silico framework for automatically screening disease-related variants and applied it to over 200,000 transcriptomes, providing an example to acquire medically relevant knowledge from publicly available sequence data. [ABSTRACT FROM AUTHOR]

This paper presents the theoretical support and experimental results of the application of advanced and intelligent control techniques to the drive control and trajectory tracking systems on a robotic wheelchair. The adaptive optimal control of the differential drive helps to improve the automatic guidance system's safety and comfort taking into consideration operating conditions such as load and distribution changes or motion actuator limitations. Furthermore, the incorporation of an optimal controller to minimize location errors and a fuzzy controller to adapt the linear velocity to the characteristics of the trajectory, provide the vehicle with a high degree of intelligence and autonomy, even when faced with obstacles. The global control solution implemented increases the features of the wheelchair for handicapped people, especially for those with a high degree of disability. [ABSTRACT FROM AUTHOR]