Characterizing transport of lignin breakdown products into P. chrysosporium E. lignolyticus S. cerevisiae and E. coli
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Applied Spectroscopy
Cosmic ray spikes are especially problematic for hyperspectral imaging because of the large number of spikes often present and their negative effects upon subsequent chemometric analysis. Fortunately, while the large number of spectra acquired in a hyperspectral imaging data set increases the probability and number of cosmic spikes observed, the multitude of spectra can also aid in the effective recognition and removal of the cosmic spikes. Zhang and Ben-Amotz were perhaps the first to leverage the additional spatial dimension of hyperspectral data matrices (DM). They integrated principal component analysis (PCA) into the upper bound spectrum method (UBS), resulting in a hybrid method (UBS-DM) for hyperspectral images. Here, we expand upon their use of PCA, recognizing that principal components primarily present in only a few pixels most likely correspond to cosmic spikes. Eliminating the contribution of those principal components in those pixels improves the cosmic spike removal. Both simulated and experimental hyperspectral Raman image data sets are used to test the newly developed UBS-DM-hyperspectral (UBS-DM-HS) method which extends the UBS-DM method by leveraging characteristics of hyperspectral data sets. A comparison is provided between the performance of the UBS-DM-HS method and other methods suitable for despiking hyperspectral images, evaluating both their ability to remove cosmic ray spikes and the extent to which they introduce spectral bias.
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Journal of Biomedical Optics
Fungi in the Candida genus are the most common fungal pathogens. They not only cause high morbidity and mortality but can also cost billions of dollars in healthcare. To alleviate this burden, early and accurate identification of Candida species is necessary. However, standard identification procedures can take days and have a large false negative error. The method described in this study takes advantage of hyperspectral confocal fluorescence microscopy, which enables the capability to quickly and accurately identify and characterize the unique autofluorescence spectra from different Candida species with up to 84% accuracy when grown in conditions that closely mimic physiological conditions.
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Data Handling in Science and Technology
Analyses of hyperspectral images with multivariate curve resolution (MCR) can be enhanced with the use of automated data preprocessing and improved MCR methods described and demonstrated in this chapter. These new approaches serve to greatly reduce or eliminate the need for user input and to increase the success, sensitivity, and accuracy of hyperspectral image analyses. We have pioneered the use of a dark spectral region, which is a general approach that can be introduced into any hyperspectral imaging system, to automatically remove offsets, structured noise, and uninformative pixels from the raw spectral images without the requirement of user input. The dark spectral region can also be used to minimize mixing between MCR-estimated spectral components to further improve the accuracy of the final pure spectra and their corresponding concentration images. The success of these improved preprocessing and MCR analysis methods is demonstrated with both realistically simulated images and experimentally measured spectral images.
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Journal of the Royal Society Interface
The brilliantly coloured cone oil droplets of the avian retina function as long-pass cut-off filters that tune the spectral sensitivity of the photoreceptors and are hypothesized to enhance colour discrimination and improve colour constancy. Although it has long been known that these droplets are pigmented with carotenoids, their precise composition has remained uncertain owing to the technical challenges of measuring these very small, dense and highly refractile optical organelles. In this study, we integrated results from high-performance liquid chromatography, hyperspectral microscopy and microspectrophotometry to obtain a comprehensive understanding of oil droplet carotenoid pigmentation in the chicken (Gallus gallus). We find that each of the four carotenoid-containing droplet types consists of a complex mixture of carotenoids, with a single predominant carotenoid determining the wavelength of the spectral filtering cut-off. Consistent with previous reports, we find that the predominant carotenoid type in the oil droplets of long-wavelength-sensitive, medium-wavelength-sensitive and short-wavelength-sensitive type 2 cones are astaxanthin, zeaxanthin and galloxanthin, respectively. In addition, the oil droplet of the principal member of the double cone contains a mixture of galloxanthin and two hydroxycarotenoids (lutein and zeaxanthin). Short-wavelength-absorbing apocarotenoids are present in all of the droplet types, providing filtering of light in a region of the spectrum where filtering by hydroxy- and ketocarotenoids may be incomplete. Thus, birds rely on a complex palette of carotenoid pigmentswithin their cone oil droplets to achieve finely tuned spectral filtering.
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Photosynthetic organisms use complex and regulated multichromophore assemblies, called lightharvesting (LH) antennas, to capture, concentrate and direct solar radiation to reaction centers that then carry out concomitant chemistry. Nature’s LH antennas are remarkable, operating with high efficiency in fluctuating environmental and photic conditions as well as being assembled with nanoscale precision thus, they often serve as inspiration in material design. The presented work was inspired by a natural LH antenna. We show that a diblock copolymer amphiphile enables the generation and integration of optically dense chromophore arrays, within a biomimetic polymer membrane. The entire construct is solution-processable, scalable and exhibits intra and inter-supramolecular energy transfer in a completely noncovalent design. This work demonstrates the potential of polymer membrane materials in generating spatial-energetic landscapes for photonic applications.
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