A monolithic sulfobetaine polymethacrylate micro-column BIGDMA-MEDSA designed in our laboratory, shows dual retention mechanism: In acetonitrile-rich mobile phase, hydrophilic interactions control the retention (HILIC system), whereas in more aqueous mobile phases the column shows essentially reversed-phase behavior with major role of hydrophobic interactions. The zwitterionic polymethacrylate micro-column can be used in the first dimension of two-dimensional LC in alternating reversed-phase (RP) and HILIC modes, coupled with an alkyl-bonded core-shell or silica-based monolithic column in the second dimension, for HILIC×RP and RP×RP comprehensive two-dimensional separations. During the HILIC×RP period, a gradient of decreasing acetonitrile gradient is used for separation in the first dimension, so that at the end of the gradient the polymeric monolithic micro-column is equilibrated with a highly aqueous mobile phase and is ready for repeated sample injection, this time for separation under reversed-phase gradient conditions with increasing concentration of acetonitrile in the first dimension. The fast repeating reversed-phase gradients on a short silica-monolithic or core-shell column in the second dimension can be optimized independently of the actual running first-dimension gradient program. As the alternating HILIC and RP separations on the first-dimension zwitterionic methacrylate column are based on complementary retention mechanisms, the instrumental setup essentially represents two coupled two-dimensional systems. It is first time that such an automated dual LCxLC approach is reported. The novel system allows obtaining three-dimensional data in a relatively short time and can be applied not only to multidimensional gradient separations of flavones and related polyphenolic compounds.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.chroma.2016.04.007 | DOI Listing |
Sensors (Basel)
January 2025
Centre of Mechanical Technology and Automation (TEMA), Department of Mechanical Engineering, University of Aveiro, 3810-193 Aveiro, Portugal.
To automate the quality control of painted surfaces of heating devices, an automatic defect detection and classification system was developed by combining deflectometry and bright light-based illumination on the image acquisition, deep learning models for the classification of non-defective (OK) and defective (NOK) surfaces that fused dual-modal information at the decision level, and an online network for information dispatching and visualization. Three decision-making algorithms were tested for implementation: a new model built and trained from scratch and transfer learning of pre-trained networks (ResNet-50 and Inception V3). The results revealed that the two illumination modes employed widened the type of defects that could be identified with this system, while maintaining its lower computational complexity by performing multi-modal fusion at the decision level.
View Article and Find Full Text PDFSensors (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 PDFSensors (Basel)
January 2025
School of Automation, Beijing Institute of Technology, Beijing 100081, China.
Existing autonomous driving systems face challenges in accurately capturing drivers' cognitive states, often resulting in decisions misaligned with drivers' intentions. To address this limitation, this study introduces a pioneering human-centric spatial cognition detecting system based on drivers' electroencephalogram (EEG) signals. Unlike conventional EEG-based systems that focus on intention recognition or hazard perception, the proposed system can further extract drivers' spatial cognition across two dimensions: relative distance and relative orientation.
View Article and Find Full Text PDFBiomolecules
January 2025
Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Background: The mammalian NAD-dependent deacetylase sirtuin-1 family (named also silent information regulator or SIRT family, where NAD stands for "nicotinamide adenine dinucleotide" (NAD)) appears to have a dual role in several human cancers by modulating cell proliferation and death. This study examines how SIRT1 protein levels correlate with clinicopathological characteristics and survival outcomes in patients with breast cancer.
Methods: A total of 407 BC formalin-fixed paraffin-embedded (FFPE) samples were collected from King Abdulaziz University Hospital, Saudi Arabia.
Diagnostics (Basel)
January 2025
Roche Diagnostics Solutions (Ventana Medical Systems, Inc.), 1910 E. Innovation Park Dr., Tucson, AZ 85755, USA.
Performing hematoxylin and eosin (H&E) staining and immunohistochemistry (IHC) on the same specimen slide provides advantages that include specimen conservation and the ability to combine the H&E context with biomarker expression at the individual cell level. We previously used invisible deposited chromogens and dual-camera imaging, including monochrome and color cameras, to implement simultaneous H&E and IHC. Using this approach, conventional H&E staining could be simultaneously viewed in color on a computer monitor alongside a monochrome video of the invisible IHC staining, while manually scanning the specimen.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!