AI Article Synopsis
- Deamination of cytosine and the misincorporation of dUMP can lead to the presence of uracil in DNA.
- The study introduces AI-seq, a method that allows for the detailed profiling of uracil at a single nucleotide level and highlights its concentration in centromeric regions.
- Findings suggest that the detected uracil sites originate from cytosine deamination and demonstrate the potential importance of uracil as an epigenetic modification in biological processes.
Article Abstract
Deamination of cytosine and dUMP misincorporation have been found to be capable of producing uracil in the genome. This study presents the AI-seq (artificial incorporation modified nucleobase for sequencing), a "base substitution", which not only is capable of profiling uracil at single-nucleotide resolution and showing its centromeric enrichment but could also reveal that the identified uracil sites are derived from cytosine deamination. All the results indicate the potential biological significance of uracil as the epigenetic modification.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8227591 | PMC |
| http://dx.doi.org/10.1021/acscentsci.0c01504 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Deep learning multi-classification of middle ear diseases using synthetic tympanic images.
Acta Otolaryngol
January 2025
Department of Otorhinolaryngology, Institute of Science Tokyo, Tokyo, Japan.
Background: Recent advances in artificial intelligence have facilitated the automatic diagnosis of middle ear diseases using endoscopic tympanic membrane imaging.
Aim: We aimed to develop an automated diagnostic system for middle ear diseases by applying deep learning techniques to tympanic membrane images obtained during routine clinical practice.
Material And Methods: To augment the training dataset, we explored the use of generative adversarial networks (GANs) to produce high-quality synthetic tympanic images that were subsequently added to the training data.
Review of Recent Advances in Predictive Maintenance and Cybersecurity for Solar Plants.
Sensors (Basel)
January 2025
Laboratory Engineering System, Hassania School of Public Works, Casablanca BP 8108, Morocco.
This paper presents a systematic review that explores the latest advancements in predictive maintenance methods and cybersecurity for solar panel systems, shedding light on the advantages and challenges of the most recent developments in predictive maintenance techniques for solar plants. Numerous important research studies, reviews, and empirical studies published between 2018 and 2023 are examined. These technologies help in detecting defects, degradation, and anomalies in solar panels by facilitating early intervention and reducing the probability of inverter failures.
View Article and Find Full Text PDFNovel Design on Knee Exoskeleton with Compliant Actuator for Post-Stroke Rehabilitation.
Sensors (Basel)
December 2024
Institute of Robotics, Autonomous System and Sensing, School of Electronic and Electrical Engineering, University of Leeds, Leeds LS2 9JT, UK.
Knee joint disorders pose a significant and growing challenge to global healthcare systems. Recent advancements in robotics, sensing technologies, and artificial intelligence have driven the development of robot-assisted therapies, reducing the physical burden on therapists and improving rehabilitation outcomes. This study presents a novel knee exoskeleton designed for safe and adaptive rehabilitation, specifically targeting bed-bound stroke patients to enable early intervention.
View Article and Find Full Text PDFAn Improved Method for Enhancing the Accuracy and Speed of Dynamic Object Detection Based on YOLOv8s.
Sensors (Basel)
December 2024
School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China.
Accurate detection and tracking of dynamic objects are critical for enabling skill demonstration and effective skill generalization in robotic skill learning and application scenarios. To further improve the detection accuracy and tracking speed of the YOLOv8s model in dynamic object tracking tasks, this paper proposes a method to enhance both detection precision and speed based on YOLOv8s architecture. Specifically, a Focused Linear Attention mechanism is introduced into the YOLOv8s backbone network to enhance dynamic object detection accuracy, while the Ghost module is incorporated into the neck network to improve the model's tracking speed for dynamic objects.
View Article and Find Full Text PDFA Comparative Analysis of Explainable Artificial Intelligence Models for Electric Field Strength Prediction over Eight European Cities.
Sensors (Basel)
December 2024
2nd Department of Radiology, Medical School, National and Kapodistrian University of Athens, 12462 Athens, Greece.
The widespread propagation of wireless communication devices, from smartphones and tablets to Internet of Things (IoT) systems, has become an integral part of modern life. However, the expansion of wireless technology has also raised public concern about the potential health risks associated with prolonged exposure to electromagnetic fields. Our objective is to determine the optimal machine learning model for constructing electric field strength maps across urban areas, enhancing the field of environmental monitoring with the aid of sensor-based data collection.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!