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1. Full-field optical spectroscopy at a high spectral resolution using atomic vapors

   https://doi.org/10.1364/OE.479253  

   Hutchins, Romanus; Zanini, Giulia; Scarcelli, Giuliano (January 2023, Optics Express)

   Spectral imaging techniques extract spectral information using dispersive elements in combination with optical microscopes. For rapid acquisition, multiplexing spectral information along one dimension of imaged pixels has been demonstrated in hyperspectral imaging, as well as in Raman and Brillouin imaging. Full-field spectroscopy, i.e., multiplexing where imaged pixels are collected in 2D simultaneously while spectral analysis is performed sequentially, can increase spectral imaging speed, but so far has been attained at low spectral resolutions. Here, we extend 2D multiplexing to high spectral resolutions of ∼80 MHz (∼0.0001 nm) using high-throughput spectral discrimination based on atomic transitions. 

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2. Microscopy is better in color: development of a streamlined spectral light path for real-time multiplex fluorescence microscopy

   https://doi.org/10.1364/BOE.453657  

   Browning, Craig_M; Mayes, Samantha; Mayes, Samuel_A; Rich, Thomas_C; Leavesley, Silas_J (June 2022, Biomedical Optics Express)

   Spectroscopic image data has provided molecular discrimination for numerous fields including: remote sensing, food safety and biomedical imaging. Despite the various technologies for acquiring spectral data, there remains a trade-off when acquiring data. Typically, spectral imaging either requires long acquisition times to collect an image stack with high spectral specificity or acquisition times are shortened at the expense of fewer spectral bands or reduced spatial sampling. Hence, new spectral imaging microscope platforms are needed to help mitigate these limitations. Fluorescence excitation-scanning spectral imaging is one such new technology, which allows more of the emitted signal to be detected than comparable emission-scanning spectral imaging systems. Here, we have developed a new optical geometry that provides spectral illumination for use in excitation-scanning spectral imaging microscope systems. This was accomplished using a wavelength-specific LED array to acquire spectral image data. Feasibility of the LED-based spectral illuminator was evaluated through simulation and benchtop testing and assessment of imaging performance when integrated with a widefield fluorescence microscope. Ray tracing simulations (TracePro) were used to determine optimal optical component selection and geometry. Spectral imaging feasibility was evaluated using a series of 6-label fluorescent slides. The LED-based system response was compared to a previously tested thin-film tunable filter (TFTF)-based system. Spectral unmixing successfully discriminated all fluorescent components in spectral image data acquired from both the LED and TFTF systems. Therefore, the LED-based spectral illuminator provided spectral image data sets with comparable information content so as to allow identification of each fluorescent component. These results provide proof-of-principle demonstration of the ability to combine output from many discrete wavelength LED sources using a double-mirror (Cassegrain style) optical configuration that can be further modified to allow for high speed, video-rate spectral image acquisition. Real-time spectral fluorescence microscopy would allow monitoring of rapid cell signaling processes (i.e., Ca²⁺and other second messenger signaling) and has potential to be translated to clinical imaging platforms. 

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3. The critical role that spectral libraries play in capturing the metabolomics community knowledge

   https://doi.org/10.1007/s11306-022-01947-y  

   Bittremieux, Wout; Wang, Mingxun; Dorrestein, Pieter C. (December 2022, Metabolomics)

   Background: Spectral library searching is currently the most common approach for compound annotation in untargeted metabolomics. Spectral libraries applicable to liquid chromatography mass spectrometry have grown in size over the past decade to include hundreds of thousands to millions of mass spectra and tens of thousands of compounds, forming an essential knowledge base for the interpretation of metabolomics experiments. Aim of Review: We describe existing spectral library resources, highlight different strategies for compiling spectral libraries, and discuss quality considerations that should be taken into account when interpreting spectral library searching results. Finally, we describe how spectral libraries are empowering the next generation of machine learning tools in computational metabolomics, and discuss several opportunities for using increasingly accessible large spectral libraries. Key Scientific Concepts of Review: This review focuses on the current state of spectral libraries for untargeted LC-MS/MS based metabolomics. We show how the number of entries in publicly accessible spectral libraries has increased more than 60-fold in the past eight years to aid molecular interpretation and we discuss how the role of spectral libraries in untargeted metabolomics will evolve in the near future. 

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4. Coupling spectral and resource-use complementarity in experimental grassland and forest communities

   https://doi.org/10.1098/rspb.2021.1290  

   Schweiger, Anna K.; Cavender-Bares, Jeannine; Kothari, Shan; Townsend, Philip A.; Madritch, Michael D.; Grossman, Jake J.; Gholizadeh, Hamed; Wang, Ran; Gamon, John A. (September 2021, Proceedings of the Royal Society B: Biological Sciences)

   Reflectance spectra provide integrative measures of plant phenotypes by capturing chemical, morphological, anatomical and architectural trait information. Here, we investigate the linkages between plant spectral variation, and spectral and resource-use complementarity that contribute to ecosystem productivity. In both a forest and prairie grassland diversity experiment, we delineated n -dimensional hypervolumes using wavelength bands of reflectance spectra to test the association between the spectral space occupied by individual plants and their growth, as well as between the spectral space occupied by plant communities and ecosystem productivity. We show that the spectral space occupied by individuals increased with their growth, and the spectral space occupied by plant communities increased with ecosystem productivity. Furthermore, ecosystem productivity was better explained by inter-individual spectral complementarity than by the large spectral space occupied by productive individuals. Our results indicate that spectral hypervolumes of plants can reflect ecological strategies that shape community composition and ecosystem function, and that spectral complementarity can reveal resource-use complementarity. 

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5. SF-GRASS: solver-free graph spectral sparsification

   https://doi.org/10.1145/3400302.3415629  

   Zhang, Ying; Zhao, Zhiqiang; Feng, Zhuo (November 2020, International Conference on Computer-Aided Design)

   null (Ed.)

   Recent spectral graph sparsification techniques have shown promising performance in accelerating many numerical and graph algorithms, such as iterative methods for solving large sparse matrices, spectral partitioning of undirected graphs, vectorless verification of power/thermal grids, representation learning of large graphs, etc. However, prior spectral graph sparsification methods rely on fast Laplacian matrix solvers that are usually challenging to implement in practice. This work, for the first time, introduces a solver-free approach (SF-GRASS) for spectral graph sparsification by leveraging emerging spectral graph coarsening and graph signal processing (GSP) techniques. We introduce a local spectral embedding scheme for efficiently identifying spectrally-critical edges that are key to preserving graph spectral properties, such as the first few Laplacian eigenvalues and eigenvectors. Since the key kernel functions in SF-GRASS can be efficiently implemented using sparse-matrix-vector-multiplications (SpMVs), the proposed spectral approach is simple to implement and inherently parallel friendly. Our extensive experimental results show that the proposed method can produce a hierarchy of high-quality spectral sparsifiers in nearly-linear time for a variety of real-world, large-scale graphs and circuit networks when compared with prior state-of-the-art spectral methods. 

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