The principle and instrumental structure of dispersion Hadamard transform spectral imager were briefly described in the present paper, and the disadvantages of the imager both in dislocation of spatial and spectral information and in spectral resolution limited by the width of Hadamard encoding mask were pointed out. A new instrumental principle and design of spatially modulated interference Hadamard transform spectral imager was proposed. A lateral shearing interferometer was used to acquire interference signals of all the Hadamard encoding information at different optical path difference. Then the methods of Fourier transform and Hadamard transform for interferogram were performed to acquire the spectra of objectives. Theory analysis of this imager demonstrated that the modulation of interferogram would not be affected by some factors such as the form and size of Hadamard encoding mask, and the spectral resolution would not be influenced by the size of Hadamard encoding mask. Furthermore, such technique not only effectively eradicated the dislocation of spatial information and spectral information existing in dispersion Hadamard transform spectral imager, but also made it convenient to image with high-throughput, high spatial resolution and high spectral resolution.
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Biosensors (Basel)
October 2024
Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
The autofluorescence of erythrocyte porphyrins has emerged as a potential method for multi-cancer early detection (MCED). With this method's dependence on research-grade spectrofluorometers, significant improvements in instrumentation are necessary to translate its potential into clinical practice, as with any promising medical technology. To fill this gap, in this paper, we present an automated ratio porphyrin analyzer for cancer screening (ARPA-CS), a low-cost, portable, and automated instrument for MCED via the ratio fluorometry of porphyrins.
View Article and Find Full Text PDFAnal Chem
October 2024
Department of Chemistry and Biochemistry and Bio5 Institute, University of Arizona, Tucson, Arizona 85721, United States.
Charge detection mass spectrometry (CD-MS) is a powerful technique for the analysis of large, heterogeneous biomolecules. By directly measuring the charge states of individual ions, CD-MS can measure the masses from spectra where conventional deconvolution approaches fail due to the lack of isotopic resolution or distinguishable charge states. However, CD-MS is inherently slow because hundreds or thousands of spectra need to be collected to produce adequate ion statistics.
View Article and Find Full Text PDFiScience
September 2024
Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR 999078, China.
J Am Soc Mass Spectrom
October 2024
Center for Innovative Technology, Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States.
Global discovery lipidomics can provide comprehensive chemical information toward understanding the intricacies of metabolic lipid disorders such as dyslipidemia; however, the isomeric complexity of lipid species remains an analytical challenge. Orthogonal separation strategies, such as ion mobility (IM), can be inserted into liquid chromatography-mass spectrometry (LC-MS) untargeted lipidomic workflows for additional isomer separation and high-confidence annotation, and the emergence of high-resolution ion mobility (HRIM) strategies provides marked improvements to the resolving power ( > 200) that can differentiate small structural differences characteristic of isomers. One such HRIM strategy, high-resolution demultiplexing (HRdm), utilizes multiplexed drift tube ion mobility spectrometry (DTIMS) with post-acquisition algorithmic deconvolution to access high IM resolutions while retaining the measurement precision inherent to the drift tube technique; however, HRdm has yet to be utilized in untargeted studies.
View Article and Find Full Text PDFNeural Netw
December 2024
School of Information Engineering, Guangdong University of Technology, Guangzhou, 510006, China. Electronic address:
Vision Transformer have achieved impressive performance in image super-resolution. However, they suffer from low inference speed mainly because of the quadratic complexity of multi-head self-attention (MHSA), which is the key to learning long-range dependencies. On the contrary, most CNN-based methods neglect the important effect of global contextual information, resulting in inaccurate and blurring details.
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