2,574 research outputs found
Serial Correlations in Single-Subject fMRI with Sub-Second TR
When performing statistical analysis of single-subject fMRI data, serial
correlations need to be taken into account to allow for valid inference.
Otherwise, the variability in the parameter estimates might be under-estimated
resulting in increased false-positive rates. Serial correlations in fMRI data
are commonly characterized in terms of a first-order autoregressive (AR)
process and then removed via pre-whitening. The required noise model for the
pre-whitening depends on a number of parameters, particularly the repetition
time (TR). Here we investigate how the sub-second temporal resolution provided
by simultaneous multislice (SMS) imaging changes the noise structure in fMRI
time series. We fit a higher-order AR model and then estimate the optimal AR
model order for a sequence with a TR of less than 600 ms providing whole brain
coverage. We show that physiological noise modelling successfully reduces the
required AR model order, but remaining serial correlations necessitate an
advanced noise model. We conclude that commonly used noise models, such as the
AR(1) model, are inadequate for modelling serial correlations in fMRI using
sub-second TRs. Rather, physiological noise modelling in combination with
advanced pre-whitening schemes enable valid inference in single-subject
analysis using fast fMRI sequences
Mass flow, energy flow and costs of the German building stock
On behalf of a committee of the German parliament, a study of
the mass, energy and monetary flows of the German building stock
has been initated. The approach was macro-economic (top-down)
for the calculation of the overall flows and process oriented
(bottom-up)for the detailed flows created by new construction,
refurbishment, demolition and utilisation of buildings. The
building stock has been modelled with a stochastic replacement
model for a reference population of 160 building materials.
Specific data sets were used for the upstream and downstream
parts. A scenario for the development of the building stock and
the induced mass, energy and monetary flows was established
Accelerated mapping of magnetic susceptibility using 3D planes-on-a-paddlewheel (POP) EPI at ultra-high field strength
With the advent of ultra-high field MRI scanners in clinical research, susceptibility based MRI has recently gained increasing interest because of its potential to assess subtle tissue changes underlying neurological pathologies/disorders. Conventional, but rather slow, three-dimensional (3D) spoiled gradient-echo (GRE) sequences are typically employed to assess the susceptibility of tissue. 3D echo-planar imaging (EPI) represents a fast alternative but generally comes with echo-time restrictions, geometrical distortions and signal dropouts that can become severe at ultra-high fields. In this work we assess quantitative susceptibility mapping (QSM) at 7T using non-Cartesian 3D EPI with a planes-on-a-paddlewheel (POP) trajectory, which is created by rotating a standard EPI readout train around its own phase encoding axis. We show that the threefold accelerated non-Cartesian 3D POP EPI sequence enables very fast, whole brain susceptibility mapping at an isotropic resolution of 1mm and that the high image quality has sufficient signal-to-noise ratio in the phase data for reliable QSM processing. The susceptibility maps obtained were comparable with regard to QSM values and geometric distortions to those calculated from a conventional 4min 3D GRE scan using the same QSM processing pipeline
Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario
A variety of methods is available to quantify uncertainties arising with\-in
the modeling of flow and transport in carbon dioxide storage, but there is a
lack of thorough comparisons. Usually, raw data from such storage sites can
hardly be described by theoretical statistical distributions since only very
limited data is available. Hence, exact information on distribution shapes for
all uncertain parameters is very rare in realistic applications. We discuss and
compare four different methods tested for data-driven uncertainty
quantification based on a benchmark scenario of carbon dioxide storage. In the
benchmark, for which we provide data and code, carbon dioxide is injected into
a saline aquifer modeled by the nonlinear capillarity-free fractional flow
formulation for two incompressible fluid phases, namely carbon dioxide and
brine. To cover different aspects of uncertainty quantification, we incorporate
various sources of uncertainty such as uncertainty of boundary conditions, of
conceptual model definitions and of material properties. We consider recent
versions of the following non-intrusive and intrusive uncertainty
quantification methods: arbitary polynomial chaos, spatially adaptive sparse
grids, kernel-based greedy interpolation and hybrid stochastic Galerkin. The
performance of each approach is demonstrated assessing expectation value and
standard deviation of the carbon dioxide saturation against a reference
statistic based on Monte Carlo sampling. We compare the convergence of all
methods reporting on accuracy with respect to the number of model runs and
resolution. Finally we offer suggestions about the methods' advantages and
disadvantages that can guide the modeler for uncertainty quantification in
carbon dioxide storage and beyond
Akustische Tomographie zur gleichzeitigen Bestimmung von Temperatur- und Strömungsfeldern in Innenräumen
Das Verfahren der akustischen Laufzeittomographie nutzt die Abhängigkeit der Schallgeschwindigkeit von den Parametern Temperatur und Strömung entlang des Ausbreitungsweges akustischer Signale, um diese Parameter zu bestimmen. Es wird ein Algorithmus vorgestellt, der eine tomographische Rekonstruktion der 2-dimensionalen Strömungsfelder innerhalb eines Messgebietes erlaubt, wobei die räumliche Auflösung des Vektorfeldes der Auflösung des Temperaturfeldes entspricht. Neben Ergebnissen von Simulationen verschiedener Strömungssituationen wird eine Anwendung vorgestellt, welches die Anwendbarkeit des Verfahrens zur Detektion von Strömungs- und Temperaturverteilung in einem abgeschlossenen Raum demonstriert.Acoustic travel time tomography uses the dependency of sound speed from temperature and flow properties along the propagation path to measure these parameters. An algorithm is introduced which is capable of resolving the two-dimensional flow field within a certain measuring area comparable to the resolution of the temperature field. Different flow fields have been simulated in order to show the reconstruction properties of the algorithm. Furthermore an experiment has been carried out, which demonstrates the applicability of the acoustic tomographic
method to detect temperature and flow fields indoor
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Croconaine-Based Polymer Particles as Contrast Agents for Photoacoustic Imaging
In the development and optimization of imaging methods, photoacoustic imaging (PAI) has become a powerful tool for preclinical biomedical diagnosis and detection of cancer. PAI probes can improve contrast and help identify pathogenic tissue. Such contrast agents must meet several requirements: they need to be biocompatible, and absorb strongly in the near-infrared (NIR) range, while relaxing the photoexcited state thermally and not radiatively. In this work, polymer nanoparticles are produced with croconaine as a monomer unit. Small molecular croconaine dyes are known to act as efficient pigments, which do not show photoluminescence. Here, for the first time croconaine copolymer nanoparticles are produced from croconic acid and a range of aromatic diamines. Following a dispersion polymerization protocol, this approach yields monodisperse particles of adjustable size. All synthesized polymers exhibit broad absorption within the NIR spectrum and therefore represent suitable candidates as contrast agents for PAI. The optical properties of these polymer particles are discussed with respect to the relation between particle size and outstanding photoacoustic performance. Biocompatibility of the polymer particles is demonstrated in cell viability experiments. © The Authors. Published by Wiley-VCH Gmb
Aerosol microphysical impact on summertime convective precipitation in the Rocky Mountain region
We present an aerosol-cloud-precipitation modeling study of convective clouds using the Weather Research and Forecasting model fully coupled with Chemistry (WRF-Chem) version 3.1.1. Comparison of the model output with measurements from a research site in the Rocky Mountains in Colorado revealed that the fraction of organics in the model is underpredicted. This is most likely due to missing processes in the aerosol module in the model version used, such as new particle formation and growth of secondary organic aerosols. When boundary conditions and domain-wide initial conditions of aerosol loading are changed in the model (factors of 0.1, 0.2, and 10 of initial aerosol mass of SO4-2, NH4+, and NO3-), the domain-wide precipitation changes by about 5%. Analysis of the model results reveals that the Rocky Mountain region and Front Range environment is not conducive for convective invigoration to play a major role, in increasing precipitation, as seen in some other studies. When localized organic aerosol emission are increased to mimic new particle formation, the resulting increased aerosol loading leads to increases in domain-wide precipitation, opposite to what is seen in the model simulations with changed boundary and initial conditions
A disulfide bridge in the calcium binding site of a polyester hydrolase increases its thermal stability and activity against polyethylene terephthalate
Elevated reaction temperatures are crucial for the efficient enzymatic
degradation of polyethylene terephthalate (PET). A disulfide bridge was
introduced to the polyester hydrolase TfCut2 to substitute its calcium binding site. The melting point of the resulting variant increased to 94.7°C (wild-type TfCut2: 69.8 °C) and its half-inactivation temperature to 84.6 °C (TfCut2: 67.3 °C). The variant D204C-E253C-D174R obtained by introducing further mutations at vicinal residues showed a temperature optimum between 75 and 80 °C compared to 65 and 70 °C of the wild-type enzyme. The variant caused a weight loss of PET films of 25.0 +/- 0.8% (TfCut2: 0.3 +/-0.1%) at 70 °C after a reaction time of 48 h. The results demonstrate that a highly efficient and calcium-independent thermostable polyester hydrolase can be obtained by replacing its calcium binding site with a disulfide bridge
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