While circular RNAs (circRNAs) exhibit lower abundance compared to corresponding linear RNAs, they demonstrate potent biological functions. Nevertheless, challenges arise from the low concentration and distinctive structural features of circRNAs, rendering existing methods operationally intricate and less sensitive. Here, we engineer an intelligent tetrahedral DNA framework (TDF) possessing precise spatial pattern-recognition properties with exceptional sensing speed and sensitivity for circRNAs. The signal output of TDF sensor occurs only when multivalent spatial pattern-recognition of a circRNA in unamplified samples. Using this sensor, we visualize the real-time response of endogenous circRNA expression in vitro neuronal cells and in vivo brain between pre-stroke and post-stroke male mice, identify the patients with acute ischemic stroke in clinical samples, as well as track the delivery of circRNA in photochromic stroked animal model. Thus, the TDF sensor provides a fast and sensitive tool for the detection of circRNA abundance in both physiological and pathophysiological conditions.
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http://dx.doi.org/10.1038/s41467-024-55364-x | DOI Listing |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11685481 | PMC |
Nat Commun
December 2024
Department of Pharmacology, Jiangsu Provincial Key Laboratory of Critical Care Medicine, School of Medicine, Southeast University, Nanjing, China.
While circular RNAs (circRNAs) exhibit lower abundance compared to corresponding linear RNAs, they demonstrate potent biological functions. Nevertheless, challenges arise from the low concentration and distinctive structural features of circRNAs, rendering existing methods operationally intricate and less sensitive. Here, we engineer an intelligent tetrahedral DNA framework (TDF) possessing precise spatial pattern-recognition properties with exceptional sensing speed and sensitivity for circRNAs.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Geographic Information System, Chinese Academy of Surveying and mapping, Beijing, 100036, China.
Geographic entity matching is an important means for multi-source spatial data fusion and information association and sharing. Corresponding matching methods have been designed by existing studies for different types of entity data characteristics, such as line and area. However, these approaches are often limited in the generalization ability for matching heterogeneous data from multiple sources and the accuracy for complex pattern matching.
View Article and Find Full Text PDFSci Rep
December 2024
School of Smart Health, Chongqing Polytechnic University of Electronic Technology, Chongqing, 401331, China.
In the field of rehabilitation, although deep learning have been widely used in multitype gesture recognition via surface electromyography (sEMG), their higher algorithmic complexity often leads to low computationally inefficient, which compromise their practicality. To achieve more efficient multitype recognition, We propose the Residual-Inception-Efficient (RIE) model, which integrates Inception and efficient channel attention (ECA). The Inception, which is a multiscale fusion convolutional module, is adopted to enhance the ability to extract sEMG features.
View Article and Find Full Text PDFBiomimetics (Basel)
December 2024
College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge UB8 3PH, UK.
In recent years, significant advancements have been made in the field of brain-computer interfaces (BCIs), particularly in the area of emotion recognition using EEG signals. The majority of earlier research in this field has missed the spatial-temporal characteristics of EEG signals, which are critical for accurate emotion recognition. In this study, a novel approach is presented for classifying emotions into three categories, positive, negative, and neutral, using a custom-collected dataset.
View Article and Find Full Text PDFHopfield neural networks (HNNs) promise broad applications in areas such as combinatorial optimization, memory storage, and pattern recognition. Among various implementations, optical HNNs are particularly interesting because they can take advantage of fast optical matrix-vector multiplications. Yet their studies so far have mostly been on the theoretical side, and the effects of optical imperfections and robustness against memory errors remain to be quantified.
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