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    A physically informed multi-scale deep neural network for estimating foliar nitrogen concentration in vegetation
    (Elsevier B.V., 2024-05-28) Dehghan-Shoar MH; Kereszturi G; Pullanagari RR; Orsi AA; Yule IJ; Hanly J
    This study introduces a Physically Informed Deep Neural Network (PINN) that leverages spectral data and Radiative Transfer Model insights to improve nitrogen concentration estimation in vegetation, addressing the complexities of physical processes. Utilizing a comprehensive spectroscopy dataset from various species across dry/ground (n = 2010), leaf (n = 1512), and canopy (n = 6007) scales, the study identifies 13 spectral bands key for chlorophyll and protein quantification. Key bands at 2276 nm, 755 nm, 1526 nm, 2243 nm, and 734 nm emerged vital for accurate N% prediction. The PINN outperforms partial least squares regression and standard deep neural networks, achieving an R2 of 0.71 and an RMSE of 0.42 (%N) on an independent validation set. Results indicate dry/ground data performed best (R2 = 0.9, RMSE = 0.24 %N), with leaf and canopy data showing lower efficacy (R2 = 0.67, RMSE = 0.45 %N; R2 = 0.65, RMSE = 0.46 %N, respectively). This multi-scale approach provides insights into spectral and N% relationships, enabling precise estimation across vegetation types and facilitating the development of transferable models. The PINN offers a new avenue for analyzing remote sensing data, demonstrating the significant potential for accurate, scale-spanning N% estimation in vegetation. Further enriching our analysis, the inclusion of seasonal data significantly enhanced our model's performance in field spectroscopy, with notable improvements observed across summer, spring, autumn, and winter. This adjustment underlines the model's increased accuracy and predictive capability at the field spectroscopy scale, emphasizing the vital role of integrating environmental factors, including climatic and physiological aspects, in future research.
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    Infrared spectroscopy of serum fails to identify early biomarker changes in an equine model of traumatic osteoarthritis
    (Elsevier Ltd on behalf of Osteoarthritis Research Society International (OARSI), 2022-12) Panizzi L; Vignes M; Dittmer KE; Waterland MR; Rogers CW; Sano H; McIlwraith CW; Pemberton S; Owen M; Riley CB
    OBJECTIVE: to determine the accuracy of infrared (IR)-based serum biomarker profiling to differentiate horses with early inflammatory changes associated with a traumatically induced model of equine carpal osteoarthritis (OA) from controls. METHOD: unilateral carpal OA was induced in 9 of 17 healthy Thoroughbred fillies, while the remainder served as sham operated controls. Serum samples were obtained before induction of OA (Day 0) and weekly thereafter until Day 63 from both groups. Films of dried serum were created, and IR absorbance spectra acquired. Following pre-processing, partial least squares discriminant analysis (PLSDA) and principal component analysis (PCA) were used to assess group and time differences and generate predictive models for wavenumber ranges 1300-1800 ​cm-1 and 2600-3700  ​cm-1. RESULTS: the overall correct classification rate when classifying samples by group (OA or Sham) was 52.7% (s.d. ​= ​12.8%), while it was 94.0% (s.d. ​= ​1.4%) by sampling Day. The correct classification results by group-sampling Day combinations with pre-intervention serum (Day 0) was 50.5% (s.d. ​= ​21.7%). CONCLUSION: with the current approach IR spectroscopic analysis could not differentiate serum of horses with induced carpal OA from that of controls. The high classification rate obtained by Day of sampling may reflect the effect of exercise on the biomarker profile. A longer study period (advanced disease) or naturally occurring disease may provide further information on the suitability of this technique in horses.
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    A digital correlator for use in intensity fluctuation spectroscopy : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Physics at Massey University
    (Massey University, 1982) O'Driscoll, Robert Charles
    A digital correlator suitable for applications in intensity fluctuation spectroscopy is described. Intensity fluctuation spectroscopy is a technique in which temporal fluctuations in the intensity of scattered laser light are analysed in order to obtain information about the motion of the scatterers. However a drawback of intensity fluctuation spectroscopy is that even very small amounts of contaminant dust can make the measured data difficult, if not impossible, to interpret. To help overcome this problem a device, known as the "blinker", is incorporated in the correlator. This device enables light scattering measurements to be made on less than scrupulously clean samples by monitoring the scattered light intensity and inhibiting data collection whenever the presence of dust in the scattering sample is suspected. An outline of intensity fluctuation spectroscopy is given followed by a review of correlation techniques with particular emphasis on photocount correlation, and on methods of reducing the complexity of the correlator circuits. The reasons for adopting the single clipping technique are noted, and the specification and design of the instrument discussed. The electronic circuits and their operation are described. The instrument has several different modes of operation. These include: single-clipped, double-clipped, or scaled and clipped autocorrelation; cross-correlation; probability density and distribution analysis; and multichannel signal averaging. The effect of dust on the measured intensity correlation function is examined and techniques which have been developed to minimise this effect are reviewed. The blinker technique is described in detail together with a description of the required electronic circuits. The procedure for testing the correlator is given. Since much of the testing was performed on the complete intensity fluctuation system, this system is described and details given of the sample preparation and correlation data analysis techniques. Results are presented to demonstrate the correct operation of the instrument. Experimental results are also presented to show how the blinker was used to minimise the effect of dust contamination in a dilute solution of 91nm diameter polystyrene latex spheres. Examples are included of the application of the blinker in the study of concentrated latex sphere solutions, and in the study of dilute and concentrated solutions of polystyrene random coils. Finally, some proposals are made for future developments which include a software correlator and a hardware full correlator, both of which are based on the existing instrument.
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    PAW - the Protein Analysis Workshop for 2D nuclear magnetic resonance spectroscopy : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Physics at Massey University, New Zealand
    (Massey University, 1999) Lie, Wilford; Lie, Wilford
    An X Window-based software package for SGI workstations has been developed to process and assign NMR spectra. Special consideration has been given to the assignment of two-dimensional 1H NMR spectra of proteins. The program combines features from the packages PROSPA [Eccles 1995], EASY [Eccles 1991] and FELIX [Biosym 1995] as well as having its own capabilities. It allows simultaneous display of multiple toolboxes and spectra, which can be flexibly manipulated by mouse operations, command entries, and user-editable macros. NMR spectra can be processed either interactively or with macros containing commands with parameters. A unique filter that combines the exponential and sine-bell functions has been frequently used. A water suppression technique based on fitting averaged time-domain data, as well as an efficient algorithm for calculating fast Fourier transform and Hilbert transform [Eccles 1995] are discussed and implemented. NMR spectral assignment is done interactively in three steps: peak picking, spin-system identification, and sequence-specific assignment. The process utilises three peak lists: a raw-peak list that contains records of all possible peaks in a NOESY spectrum, a diagonal peak list that contains records of peaks that define a curve about which the spectrum is symmetric, and a cross-peak list that contains records of peaks that are assigned. Details of the peak-picking methods are discussed. By reference to a list of diagonal peaks, a common calibration problem caused by Bloch-Siegert shifts [Bloch and Siegert 1940, Ernst 1987] has been minimised. Automatically produced NOE summaries allow a quick identification of peaks that are unassigned or incorrectly assigned. The peak position and integration parameters can be calculated through non-linear curve fitting with Gaussians. NMR data processing and spectral assignment using the package has been completed for Caerin 4.1, a 23-residue protein. Linear-prediction has been applied to increase the spectral resolution. Detailed results for this protein are presented. The NOE summary of the sequential assignments indicates a well-defined secondary structure that is different from Caerin 1.1 [Wong 1996, 1997].