Rhodamines are the most important class of fluorophores for applications in live-cell fluorescence microscopy. This is mainly because rhodamines exist in a dynamic equilibrium between a fluorescent zwitterion and a nonfluorescent but cell-permeable spirocyclic form. Different imaging applications require different positions of this dynamic equilibrium, and an adjustment of the equilibrium poses a challenge for the design of suitable probes. We describe here how the conversion of the -carboxy moiety of a given rhodamine into substituted acyl benzenesulfonamides and alkylamides permits the systematic tuning of the equilibrium of spirocyclization with unprecedented accuracy and over a large range. This allows one to transform the same rhodamine into either a highly fluorogenic and cell-permeable probe for live-cell-stimulated emission depletion (STED) microscopy or a spontaneously blinking dye for single-molecule localization microscopy (SMLM). We used this approach to generate differently colored probes optimized for different labeling systems and imaging applications.
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http://dx.doi.org/10.1021/jacs.1c05004 | DOI Listing |
Front Comput Neurosci
December 2024
Department of Radiology, The Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, Jiangsu, China.
It is a universal phenomenon for patients who do not know which clinical department to register in large general hospitals. Although triage nurses can help patients, due to the larger number of patients, they have to stand in a queue for minutes to consult. Recently, there have already been some efforts to devote deep-learning techniques or pre-trained language models (PLMs) to triage recommendations.
View Article and Find Full Text PDFSmall Methods
January 2025
Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea.
Rolling circle amplification (RCA)-derived ultra-long DNA is highly attractive and versatile because of its diverse functionalities conferred by repeated DNA nanostructures. However, magnesium pyrophosphate (MgPPi) crystals, as byproducts of RCA, electrostatically interact with the DNA to form DNA microhybrids and hamper its broad bioapplications, as its large size is unfavorable for cellular uptake and decreases the density of functional DNA nanostructures. In this study, finely tuned synthesis strategies are developed to condense the microhybrids and replace non-functional MgPPi crystals with various functional metal nanostructures by reducing metal ions.
View Article and Find Full Text PDFHealth Inf Sci Syst
December 2025
Institute of Mathematics and Computer Science, University of São Paulo, São Carlos, São Paulo 13566-590 Brazil.
Purpose: Deep learning-based radiomics techniques have the potential to aid specialists and physicians in performing decision-making in COVID-19 scenarios. Specifically, a Deep Learning (DL) ensemble model is employed to classify medical images when addressing the diagnosis during the classification tasks for COVID-19 using chest X-ray images. It also provides feasible and reliable visual explicability concerning the results to support decision-making.
View Article and Find Full Text PDFJ Colloid Interface Sci
December 2024
School of Physics and Astronomy, The University of Edinburgh, Peter Guthrie Tait Road, Edinburgh EH9 3FD, UK; Department of Physics, University of Gothenburg, SE-41296 Gothenburg, Sweden; University of Münster, Institute of Physical Chemistry, Corrensstr. 28/30, 48149 Münster, Germany. Electronic address:
Hypothesis: Ellipsoidal particles confined at liquid interfaces exhibit complex self-assembly due to quadrupolar capillary interactions, favouring either tip-to-tip or side-to-side configurations. However, predicting and controlling which structure forms remains challenging. We hypothesize that introducing a polymer-based soft shell around the particles will modulate these capillary interactions, providing a means to tune the preferred self-assembly configuration based on particle geometry and shell properties.
View Article and Find Full Text PDFSci Rep
December 2024
School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.
With the advancement of artificial intelligence technology, unmanned boats utilizing deep learning models have shown significant potential in water surface garbage classification. This study employs Convolutional Neural Network (CNN) to extract features of water surface floating objects and constructs the VGG16-15 model based on the VGG-16 architecture, capable of identifying 15 common types of water surface floatables. A garbage classification dataset was curated to obtain 5707 images belonging to 15 categories, which were then split into training and validation sets in a 4:1 ratio.
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