Reduced representation bisulfite sequencing (RRBS) is one of the most comprehensive yet economic ways of mapping whole genome DNA-methylation. Here, we have substantially modified the RRBS protocol by combining end-repair and A-tailing steps, and by introducing a bead-based method for rapid and easy size selection of the library molecules. The method has been optimized for myeloma clinical samples, where the input DNA concentration can be as low as 100 ng. The method developed can be accomplished in 3 days, including the initial overnight MspI enzyme digestion. Although the protocol has been optimized in myeloma samples, it is broadly applicable to any clinical sample, which is restricted by very low input DNA concentrations.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1007/978-1-4939-7865-6_13 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Background white noise increases neuronal activity by reducing membrane fluctuations and slow-wave oscillations in auditory cortex.
Prog Neurobiol
January 2025
Department of Biomedicine, University of Basel, Klingelbergstrasse 50, 4056 Basel, Switzerland. Electronic address:
The brain faces the challenging task of preserving a consistent portrayal of the external world in the face of disruptive sensory inputs. What alterations occur in sensory representation amidst noise, and how does brain activity adapt to it? Although it has previously been shown that background white noise (WN) decreases responses to salient sounds, a mechanistic understanding of the brain processes responsible for such changes is lacking. We investigated the effect of background WN on neuronal spiking activity, membrane potential, and network oscillations in the mouse central auditory system.
View Article and Find Full Text PDFRMKD: Relaxed matching knowledge distillation for short-length SSVEP-based brain-computer interfaces.
Neural Netw
January 2025
Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), 138632, Singapore. Electronic address:
Accurate decoding of electroencephalogram (EEG) signals in the shortest possible time is essential for the realization of a high-performance brain-computer interface (BCI) system based on the steady-state visual evoked potential (SSVEP). However, the degradation of decoding performance of short-length EEG signals is often unavoidable due to the reduced information, which hinders the development of BCI systems in real-world applications. In this paper, we propose a relaxed matching knowledge distillation (RMKD) method to transfer both feature-level and logit-level knowledge in a relaxed manner to improve the decoding performance of short-length EEG signals.
View Article and Find Full Text PDFUsing Inertial Measurement Units and Machine Learning to Classify Body Positions of Adults in a Hospital Bed.
Sensors (Basel)
January 2025
Curtin School of Allied Health, Curtin University, Perth 6102, Australia.
In hospitals, timely interventions can prevent avoidable clinical deterioration. Early recognition of deterioration is vital to stopping further decline. Measuring the way patients position themselves in bed and change their positions may signal when further assessment is necessary.
View Article and Find Full Text PDFA Dual-Channel and Frequency-Aware Approach for Lightweight Video Instance Segmentation.
Sensors (Basel)
January 2025
The Higher Educational Key Laboratory for Measuring & Control Technology and Instrumentation of Heilongjiang Province, Harbin University of Science and Technology, Harbin 150080, China.
Video instance segmentation, a key technology for intelligent sensing in visual perception, plays a key role in automated surveillance, robotics, and smart cities. These scenarios rely on real-time and efficient target-tracking capabilities for accurate perception and intelligent analysis of dynamic environments. However, traditional video instance segmentation methods face complex models, high computational overheads, and slow segmentation speeds in time-series feature extraction, especially in resource-constrained environments.
View Article and Find Full Text PDFA Combined Frame Difference and Convolution Method for Moving Vehicle Detection in Satellite Videos.
Sensors (Basel)
January 2025
Faculty of Land and Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China.
To address the challenges of missed detections caused by insufficient shape and texture features and blurred boundaries in existing detection methods, this paper introduces a novel moving vehicle detection approach for satellite videos. The proposed method leverages frame difference and convolution to effectively integrate spatiotemporal information. First, a frame difference module (FDM) is designed, combining frame difference and convolution.
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!