Browsing by Subject "Spectroscopy"

Now showing 1 - 6 of 6
  • Thumbnail Image
    Item
    Advanced Modeling of Longitudinal Spectroscopy Data
    (2014) Kundu, Madan Gopal; Harezlak, Jaroslaw; Randolph, Timothy W.; Sarkar, Jyotirmoy; Steele, Gregory K.; Yiannoutsos, Constantin T.
    Magnetic resonance (MR) spectroscopy is a neuroimaging technique. It is widely used to quantify the concentration of important metabolites in a brain tissue. Imbalance in concentration of brain metabolites has been found to be associated with development of neurological impairment. There has been increasing trend of using MR spectroscopy as a diagnosis tool for neurological disorders. We established statistical methodology to analyze data obtained from the MR spectroscopy in the context of the HIV associated neurological disorder. First, we have developed novel methodology to study the association of marker of neurological disorder with MR spectrum from brain and how this association evolves with time. The entire problem fits into the framework of scalar-on-function regression model with individual spectrum being the functional predictor. We have extended one of the existing cross-sectional scalar-on-function regression techniques to longitudinal set-up. Advantage of proposed method includes: 1) ability to model flexible time-varying association between response and functional predictor and (2) ability to incorporate prior information. Second part of research attempts to study the influence of the clinical and demographic factors on the progression of brain metabolites over time. In order to understand the influence of these factors in fully non-parametric way, we proposed LongCART algorithm to construct regression tree with longitudinal data. Such a regression tree helps to identify smaller subpopulations (characterized by baseline factors) with differential longitudinal profile and hence helps us to identify influence of baseline factors. Advantage of LongCART algorithm includes: (1) it maintains of type-I error in determining best split, (2) substantially reduces computation time and (2) applicable even observations are taken at subject-specific time-points. Finally, we carried out an in-depth analysis of longitudinal changes in the brain metabolite concentrations in three brain regions, namely, white matter, gray matter and basal ganglia in chronically infected HIV patients enrolled in HIV Neuroimaging Consortium study. We studied the influence of important baseline factors (clinical and demographic) on these longitudinal profiles of brain metabolites using LongCART algorithm in order to identify subgroup of patients at higher risk of neurological impairment.
  • Thumbnail Image
    Item
    Impairment of Motor Function Correlates with Neurometabolite and Brain Iron Alterations in Parkinson’s Disease
    (MDPI, 2019-01-29) Pesch, Beate; Casjens, Swaantje; Woitalla, Dirk; Dharmadhikari, Shalmali; Edmondson, David A.; Zella, Maria Angela Samis; Lehnert, Martin; Lotz, Anne; Herrmann, Lennard; Muhlack, Siegfried; Kraus, Peter; Yeh, Chien-Lin; Glaubitz, Benjamin; Schmidt-Wilcke, Tobias; Gold, Ralf; van Thriel, Christoph; Brüning, Thomas; Tönges, Lars; Dydak, Ulrike; Department of Radiology and Imaging Sciences, Indiana University School of Medicine
    We took advantage of magnetic resonance imaging (MRI) and spectroscopy (MRS) as non-invasive methods to quantify brain iron and neurometabolites, which were analyzed along with other predictors of motor dysfunction in Parkinson's disease (PD). Tapping hits, tremor amplitude, and the scores derived from part III of the Movement Disorder Society-Sponsored Revision of the Unified Parkinson Disease Rating Scale (MDS-UPDRS3 scores) were determined in 35 male PD patients and 35 controls. The iron-sensitive MRI relaxation rate R2* was measured in the globus pallidus and substantia nigra. γ-aminobutyric acid (GABA)-edited and short echo-time MRS was used for the quantification of neurometabolites in the striatum and thalamus. Associations of R2*, neurometabolites, and other factors with motor function were estimated with Spearman correlations and mixed regression models to account for repeated measurements (hands, hemispheres). In PD patients, R2* and striatal GABA correlated with MDS-UPDRS3 scores if not adjusted for age. Patients with akinetic-rigid PD subtype (N = 19) presented with lower creatine and striatal glutamate and glutamine (Glx) but elevated thalamic GABA compared to controls or mixed PD subtype. In PD patients, Glx correlated with an impaired dexterity when adjusted for covariates. Elevated myo-inositol was associated with more tapping hits and lower MDS-UPDRS3 scores. Our neuroimaging study provides evidence that motor dysfunction in PD correlates with alterations in brain iron and neurometabolites.
  • Thumbnail Image
    Item
    Pitching single-focus confocal data analysis one photon at a time with Bayesian nonparametrics
    (American Physical Society, 2020) Tavakoli, Meysam; Jazani, Sina; Sgouralis, Ioannis; Shafraz, Omer M.; Sivasankar, Sanjeevi; Donaphon, Bryan; Levitus, Marcia; Pressé, Steve; Physics, School of Science
    Fluorescence time traces are used to report on dynamical properties of molecules. The basic unit of information in these traces is the arrival time of individual photons, which carry instantaneous information from the molecule, from which they are emitted, to the detector on timescales as fast as microseconds. Thus, it is theoretically possible to monitor molecular dynamics at such timescales from traces containing only a sufficient number of photon arrivals. In practice, however, traces are stochastic and in order to deduce dynamical information through traditional means-such as fluorescence correlation spectroscopy (FCS) and related techniques-they are collected and temporally autocorrelated over several minutes. So far, it has been impossible to analyze dynamical properties of molecules on timescales approaching data acquisition without collecting long traces under the strong assumption of stationarity of the process under observation or assumptions required for the analytic derivation of a correlation function. To avoid these assumptions, we would otherwise need to estimate the instantaneous number of molecules emitting photons and their positions within the confocal volume. As the number of molecules in a typical experiment is unknown, this problem demands that we abandon the conventional analysis paradigm. Here, we exploit Bayesian nonparametrics that allow us to obtain, in a principled fashion, estimates of the same quantities as FCS but from the direct analysis of traces of photon arrivals that are significantly smaller in size, or total duration, than those required by FCS.
  • Thumbnail Image
    Item
    Stable water isotope and surface heat flux simulation using ISOLSM: Evaluation against in-situ measurements
    (Elsevier, 2015-04) Cai, Mick Y.; Wang, Lixin; Parkes, Stephen D.; McCabe, Matthew F.; Evans, Jason P.; Griffiths, Alan D.
    The stable isotopes of water are useful tracers of water sources and hydrological processes. Stable water isotope-enabled land surface modeling is a relatively new approach for characterizing the hydrological cycle, providing spatial and temporal variability for a number of hydrological processes. At the land surface, the integration of stable water isotopes with other meteorological measurements can assist in constraining surface heat flux estimates and discriminate between evaporation (E) and transpiration (T). However, research in this area has traditionally been limited by a lack of continuous in-situ isotopic observations. Here, the National Centre for Atmospheric Research stable isotope-enabled Land Surface Model (ISOLSM) is used to simulate the water and energy fluxes and stable water isotope variations. The model was run for a period of one month with meteorological data collected from a coastal sub-tropical site near Sydney, Australia. The modeled energy fluxes (latent heat and sensible heat) agreed reasonably well with eddy covariance observations, indicating that ISOLSM has the capacity to reproduce observed flux behavior. Comparison of modeled isotopic compositions of evapotranspiration (ET) against in-situ Fourier Transform Infrared spectroscopy (FTIR) measured bulk water vapor isotopic data (10 m above the ground), however, showed differences in magnitude and temporal patterns. The disparity is due to a small contribution from local ET fluxes to atmospheric boundary layer water vapor (∼1% based on calculations using ideal gas law) relative to that advected from the ocean for this particular site. Using ISOLSM simulation, the ET was partitioned into E and T with 70% being T. We also identified that soil water from different soil layers affected T and E differently based on the simulated soil isotopic patterns, which reflects the internal working of ISOLSM. These results highlighted the capacity of using the isotope-enabled models to discriminate between different hydrological components and add insight into expected hydrological behavior.
  • Thumbnail Image
    Item
    Synthesis, Redox and Spectroscopic Properties of Pterin of Molybdenum Cofactors
    (MDPI, 2022-05-22) Colston, Kyle J.; Basu, Partha; Chemistry and Chemical Biology, School of Science
    Pterins are bicyclic heterocycles that are found widely across Nature and are involved in a variety of biological functions. Notably, pterins are found at the core of molybdenum cofactor (Moco) containing enzymes in the molybdopterin (MPT) ligand that coordinates molybdenum and facilitates cofactor activity. Pterins are diverse and can be widely functionalized to tune their properties. Herein, the general methods of synthesis, redox and spectroscopic properties of pterin are discussed to provide more insight into pterin chemistry and their importance to biological systems.
  • Thumbnail Image
    Item
    Utility of Plasma Protein Biomarkers and Mid-Infrared Spectroscopy for Diagnosing Fracture-Related Infections: A Pilot Study
    (Wolters Kluwer, 2022-04) Farooq, Hassan; Wessel, Robert P.; Brown, Krista; Slaven, James E.; Marini, Federico; Malek, Sarah; Natoli, Roman M.; Orthopaedic Surgery, School of MedicinE
    Objectives: To compare a large panel of plasma protein inflammatory biomarkers and mid-infrared (MIR) spectral patterns between patients with confirmed fracture related infections (FRIs) and controls without infection. Design: Prospective case-control. Setting: Academic, level 1 trauma center. Patients: Thirteen patients meeting confirmatory FRI criteria were matched to 13 controls based on age, time after surgery, and fracture region. Intervention: Plasma levels of 49 proteins were measured using enzyme-linked immunosorbent assay (ELISA) techniques. Fourier transform infrared (FTIR) spectroscopy of dried films was used to obtain MIR spectra of plasma samples. Main Outcome Measurements: Plasma protein levels and MIR spectra of samples. Results: Multivariate analysis-based predictive model developed utilizing ELISA-based biomarkers had sensitivity, specificity, and accuracy of 69.2±0.0%, 99.9±1.0%, and 84.5±0.6%, respectively, with PDGF-AB/BB, CRP, and MIG selected as the minimum number of variables explaining group differences (P<0.05). Sensitivity, specificity, and accuracy of the predictive model based on MIR spectra were 69.9±6.2%, 71.9±5.9%, and 70.9±4.8%, respectively, with six wavenumbers as explanatory variables (P<0.05). Conclusions: This pilot study demonstrates the feasibility of using a select panel of plasma proteins and FTIR spectroscopy to diagnose FRI. The preliminary data suggest that measurement of these select proteins and MIR spectra may be potential clinical tools to detect FRI. Further investigation of these biomarkers in a larger cohort of patients is warranted.