Spectrally Resolved Absorption Tomography for Reacting, Turbulent Gas Phase Systems: Theory and Application

This work proposes tomographic absorption spectroscopy as a complementary measurement method to other non-intrusive methods that are applied in the research of reactive gas-phase flows. A coherent methodological framework based on conventional Bayesian inference is presented, that contains new methods and improvements in several key procedures. The framework relies on linear hyperspectral absorption tomography, that is favored for its higher computational efficiency compared to nonlinear tomography, and separates tomographic reconstruction and spectroscopic regression. The methods target the analysis of direct absorption spectroscopic measurements like direct tunable diode laser absorption spectroscopy. The improved key procedures include a spatial resolution measure based on a modified Maximum-a-posteriori covariance matrix. This resolution measure is applicable to sparse and dense beam arrangements alike, without inconsistencies arising from unprobed mesh nodes. The compatibility with resolution measures based on point spread functions is demonstrated in simulations. Additionally, the design question of the spatial-temporal resolution trade-off is discussed on spatio-temporal correlation maps with a constraint imposed by the effective measurement data-rate. Typical data-rates of spectrally resolved tomographic absorption spectroscopy setups often do not allow for capturing turbulent structures. In consequence, the optimum trade-off for quasi-stationary systems often is the focus on spatial resolution, neglecting temporal resolution. A regularization parameter choice method, relying on residuals of the spectroscopic regressions, is introduced. The idea is to balance noise amplification through under-regularization, and incompatibility with the spectroscopic model through excessive spatial-averaging of temperature structures due to over-regularization. This method allows to partially reclaim the informative advantage of nonlinear tomography, by inferring information on temperature structures from the nonlinear temperature dependence of the spectroscopic model. The selected prior parameters are shown to result in spatial resolutions matching spatial structures in the application cases. The same model error used to judge the compatibility with the spectroscopic model for parameter selection, leads to a temperature bias if temporally averaged data of a turbulent system is fitted by a homogeneous spectroscopic model. Ideas from methods to prevent this bias in spatial averaging are transferred to temporal averaging. The resulting temperature fluctuation model reduces the bias and additionally gives a qualitative measure of temperature fluctuations. The often neglected problem of estimating absorbance spectra from intensity traces is treated with Bayesian inference. This new Bayesian absorbance estimation method is shown to be numerically efficient if large numbers of absorbance traces are to be inferred like in tomography. Unlike fitting methods it is compatible with inhomogeneous line-of-sights without modification or computational penalties. Further, the incident intensity shape is not restricted to arbitrary model functions, but modeled with all degrees of freedom. The framework of methods is applied to practically relevant scenarios in the industrial characterization of selective catalytic reduction systems, and in the research of oxy-fuel combustion. The application cases feature different levels of complexity, with turbulent and laminar flows, stationary and instationary processes, axisymmetric and two dimensional flows, as well as homogeneous and inhomogeneous temperature distributions. Also the scalability of the methods is demonstrated by experiments with beam counts from 8 to 10440, and (pseudo) temporal resolutions of up to 5 kHz. For all application cases a specific discussion of uncertainty and spatial resolution is provided

Comparing deep belief networks with support vector machines for classifying gene expression data from complex disorders

Genomics data provide great opportunities for translational research and the clinical practice, for example, for predicting disease stages. However, the classification of such data is a challenging task due to their high dimensionality, noise, and heterogeneity. In recent years, deep learning classifiers generated much interest, but due to their complexity, so far, little is known about the utility of this method for genomics. In this paper, we address this problem by studying a computational diagnostics task by classification of breast cancer and inflammatory bowel disease patients based on high-dimensional gene expression data. We provide a comprehensive analysis of the classification performance of deep belief networks (DBNs) in dependence on its multiple model parameters and in comparison with support vector machines (SVMs). Furthermore, we investigate combined classifiers that integrate DBNs with SVMs. Such a classifier utilizes a DBN as representation learner forming the input for a SVM. Overall, our results provide guidelines for the complex usage of DBN for classifying gene expression data from complex diseases

Data analysis and uncertainty estimation in supercontinuum laser absorption spectroscopy

A set of algorithms is presented that facilitates the evaluation of super continuum laser absorption spectroscopy (SCLAS) measurements with respect to temperature, pressure and species concentration without the need for simultaneous background intensity measurements. For this purpose a non-linear model fitting approach is employed. A detailed discussion of the influences on the instrument function of the spectrometer and a method for the in-situ determination of the instrument function without additional hardware are given. The evaluation procedure is supplemented by a detailed measurement precision assessment by applying an error propagation through the non-linear model fitting approach. While the algorithms are tailored to SCLAS, they can be transferred to other spectroscopic methods, that similarly require an instrument function. The presented methods are validated using gas cell measurements of methane in the near infrared region at pressures up to 8.7 bar

Reliability of IMU-Derived Static Balance Parameters in Neurological Diseases

Static balance is a commonly used health measure in clinical practice. Usually, static balance parameters are assessed via force plates or, more recently, with inertial measurement units (IMUs). Multiple parameters have been developed over the years to compare patient groups and understand changes over time. However, the day-to-day variability of these parameters using IMUs has not yet been tested in a neurogeriatric cohort. The aim of the study was to examine day-to-day variability of static balance parameters of five experimental conditions in a cohort of neurogeriatric patients using data extracted from a lower back-worn IMU. A group of 41 neurogeriatric participants (age: 78 ± 5 years) underwent static balance assessment on two occasions 12-24 h apart. Participants performed a side-by-side stance, a semi-tandem stance, a tandem stance on hard ground with eyes open, and a semi-tandem assessment on a soft surface with eyes open and closed for 30 s each. The intra-class correlation coefficient (two-way random, average of the k raters' measurements, ICC2, k) and minimal detectable change at a 95% confidence level (MDC95%) were calculated for the sway area, velocity, acceleration, jerk, and frequency. Velocity, acceleration, and jerk were calculated in both anterior-posterior (AP) and medio-lateral (ML) directions. Nine to 41 participants could successfully perform the respective balance tasks. Considering all conditions, acceleration-related parameters in the AP and ML directions gave the highest ICC results. The MDC95% values for all parameters ranged from 39% to 220%, with frequency being the most consistent with values of 39-57%, followed by acceleration in the ML (43-55%) and AP direction (54-77%). The present results show moderate to poor ICC and MDC values for IMU-based static balance assessment in neurogeriatric patients. This suggests a limited reliability of these tasks and parameters, which should induce a careful selection of potential clinically relevant parameters

Cell-Based HIF1α Gene Therapy Reduces Myocardial Scar and Enhances Angiopoietic Proteome, Transcriptomic and miRNA Expression in Experimental Chronic Left Ventricular Dysfunction

Recent preclinical investigations and clinical trials with stem cells mostly studied bone-marrow-derived mononuclear cells (BM-MNCs), which so far failed to meet clinically significant functional study endpoints. BM-MNCs containing small proportions of stem cells provide little regenerative potential, while mesenchymal stem cells (MSCs) promise effective therapy via paracrine impact. Genetic engineering for rationally enhancing paracrine effects of implanted stem cells is an attractive option for further development of therapeutic cardiac repair strategies. Non-viral, efficient transfection methods promise improved clinical translation, longevity and a high level of gene delivery. Hypoxia-induced factor 1α is responsible for pro-angiogenic, anti-apoptotic and anti-remodeling mechanisms. Here we aimed to apply a cellular gene therapy model in chronic ischemic heart failure in pigs. A non-viral circular minicircle DNA vector (MiCi) was used for in vitro transfection of porcine MSCs (pMSC) with HIF1α (pMSC-MiCi-HIF-1α). pMSCs-MiCi-HIF-1α were injected endomyocardially into the border zone of an anterior myocardial infarction one month post-reperfused-infarct. Cell injection was guided via 3D-guided NOGA electro-magnetic catheter delivery system. pMSC-MiCi-HIF-1α delivery improved cardiac output and reduced myocardial scar size. Abundances of pro-angiogenic proteins were analyzed 12, 24 h and 1 month after the delivery of the regenerative substances. In a protein array, the significantly increased angiogenesis proteins were Activin A, Angiopoietin, Artemin, Endothelin-1, MCP-1; and remodeling factors ADAMTS1, FGFs, TGFb1, MMPs, and Serpins. In a qPCR analysis, increased levels of angiopeptin, CXCL12, HIF-1α and miR-132 were found 24 h after cell-based gene delivery, compared to those in untreated animals with infarction and in control animals. Expression of angiopeptin increased already 12 h after treatment, and miR-1 expression was reduced at that time point. In total, pMSC overexpressing HIF-1α showed beneficial effects for treatment of ischemic injury, mediated by stimulation of angiogenesis

Effect of Ischemic Preconditioning and Postconditioning on Exosome-Rich Fraction microRNA Levels, in Relation with Electrophysiological Parameters and Ventricular Arrhythmia in Experimental Closed-Chest Reperfused Myocardial Infarction

We investigated the antiarrhythmic effects of ischemic preconditioning (IPC) and postconditioning (PostC) by intracardiac electrocardiogram (ECG) and measured circulating microRNAs (miRs) that are related to cardiac conduction. Domestic pigs underwent 90-min. percutaneous occlusion of the mid left anterior coronary artery, followed by reperfusion. The animals were divided into three groups: acute myocardial infarction (AMI, n = 7), ischemic preconditioning-acute myocardial infarction (IPC-AMI) (n = 9), or AMI-PostC (n = 5). IPC was induced by three 5-min. episodes of repetitive ischemia/reperfusion cycles (rI/R) before AMI. PostC was induced by six 30-s rI/R immediately after induction of reperfusion 90 min after occlusion. Before the angiographic procedure, a NOGA endocardial mapping catheter was placed again the distal anterior ventricular endocardium to record the intracardiac electrogram (R-amplitude, ST-Elevation, ST-area under the curve (AUC), QRS width, and corrected QT time (QTc)) during the entire procedure. An arrhythmia score was calculated. Cardiac MRI was performed after one-month. IPC led to significantly lower ST-elevation, heart rate, and arrhythmia score during ischemia. PostC induced a rapid recovery of R-amplitude, decrease in QTc, and lower arrhythmia score during reperfusion. Slightly higher levels of miR-26 and miR-133 were observed in AMI compared to groups IPC-AMI and AMI-PostC. Significantly lower levels of miR-1, miR-208, and miR-328 were measured in the AMI-PostC group as compared to animals in group AMI and IPC-AMI. The arrhythmia score was not significantly associated with miRNA plasma levels. Cardiac MRI showed significantly smaller infarct size in the IPC-AMI group when compared to the AMI and AMI-PostC groups. Thus, IPC led to better left ventricular ejection fraction at one-month and it exerted antiarrhythmic effects during ischemia, whereas PostC exhibited antiarrhythmic properties after reperfusion, with significant downregulaton of ischemia-related miRNAs

Quantifying the spatial resolution of the maximum a posteriori estimate in linear, rank-deficient, Bayesian hard field tomography

Image based diagnostics are interpreted in the context of spatial resolution. The same is true for tomographic image reconstruction. Current empirically driven approaches to quantify spatial resolution in chemical species tomography rely on a deterministic formulation based on point-spread functions which neglect the statistical prior information, that is integral to rank-deficient tomography. We propose a statistical spatial resolution measure based on the covariance of the reconstruction (point estimate). By demonstrating the resolution measure on a chemical species tomography test case, we show that the prior information acts as a lower limit for the spatial resolution. Furthermore, the spatial resolution measure can be employed for designing tomographic systems under consideration of spatial inhomogeneity of spatial resolution

Comparing deep belief networks with support vector machines for classifying gene expression data from complex disorders

Genomics data provide great opportunities for translational research and the clinical practice, for example, for predicting disease stages. However, the classification of such data is a challenging task due to their high dimensionality, noise, and heterogeneity. In recent years, deep learning classifiers generated much interest, but due to their complexity, so far, little is known about the utility of this method for genomics. In this paper, we address this problem by studying a computational diagnostics task by classification of breast cancer and inflammatory bowel disease patients based on high-dimensional gene expression data. We provide a comprehensive analysis of the classification performance of deep belief networks (DBNs) in dependence on its multiple model parameters and in comparison with support vector machines (SVMs). Furthermore, we investigate combined classifiers that integrate DBNs with SVMs. Such a classifier utilizes a DBN as representation learner forming the input for a SVM. Overall, our results provide guidelines for the complex usage of DBN for classifying gene expression data from complex diseases.publishedVersionPeer reviewe