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

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

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

NeXtQSM -- A complete deep learning pipeline for data-consistent quantitative susceptibility mapping trained with hybrid data

Deep learning based Quantitative Susceptibility Mapping (QSM) has shown great potential in recent years, obtaining similar results to established non-learning approaches. Many current deep learning approaches are not data consistent, require in vivo training data or solve the QSM problem in consecutive steps resulting in the propagation of errors. Here we aim to overcome these limitations and developed a framework to solve the QSM processing steps jointly. We developed a new hybrid training data generation method that enables the end-to-end training for solving background field correction and dipole inversion in a data-consistent fashion using a variational network that combines the QSM model term and a learned regularizer. We demonstrate that NeXtQSM overcomes the limitations of previous deep learning methods. NeXtQSM offers a new deep learning based pipeline for computing quantitative susceptibility maps that integrates each processing step into the training and provides results that are robust and fast

UNILAC Upgrades for Coulomb Barrier Energy Experiments

The GSI linear accelerator UNILAC provides heavy ion beams at Coulomb barrier energies for search and study of super heavy elements. Typical cross-sections of 55 fb require beam doses of 1.4·10¹⁹ according to a beam time of 117 days. Several upgrades will reduce the beam time to only 16 days. A second injection branch with a 28GHz-MS-ECRIS anticipates a factor of 10 in particle intensity. By a new cw rfq-structure all accelerator tanks are suitable for a duty cycle of at least 50% instead of 25% presently. Due to this, thermal power increase of 19 rf-amplifiers eased by higher ion charge states of the ECRIS is necessary. Finally the UNILAC timing system controlling 50Hz pulse-to-pulse operation of up to six beams differing in ion species and energy has to be modified considering beam diagnostics electronics and pulsable magnets. The front end comprising ECRIS, rfq- and IH-structure is cw suitable and will serve as injector for a new future sc-cw-linac