Local and diffusive dynamics of LiCl aqueous solutions in pristine and modified silica nanopores.

We use 1H, 2H, and 7Li nuclear magnetic resonance to investigate local and diffusive dynamics of LiCl-7H2O and LiCl-7D2O solutions in pristine and functionalized silica nanopores in a component-selective manner. Recently, we showed that the solution dynamics become slower when the diameter of the pristine pores is reduced. Here, we determine the effects of (aminopropyl)triethoxysilane and dye surface functionalizations on the motions of the water molecules and lithium ions from ambient temperatures down to the glass transition. The local and diffusive solution dynamics are similar in both functionalized pores but, on average, slower than in pristine pores with comparable diameters. When the exchange between different confinement regions is sufficiently slow at reduced temperatures, bimodal water and lithium dynamics may be observed. We attribute this bimodality to bulk-like motion in the pore centers and slowed-down motion at the pore walls. For the lithium ions, a bimodality observed in the pristine pores is absent in the functionalized ones. We conjecture that the steric hindrance and electrostatic interactions associated with the grafted functional groups interfere with the formation of a defined electric double layer, while the enhanced surface roughness and unequal charge distribution result in overall slower dynamics. Thus, the nature of the walls is an important parameter for the solution dynamics. Thereby, in situ measurements of the pH value inside the silica pores using the grafted dye molecules reveal that observed changes in the pH value in response to the surface functionalization are of limited relevance for the water reorientation.

Learning metal artifact reduction in cardiac CT images with moving pacemakers.

Metal objects in the human heart such as implanted pacemakers frequently lead to heavy artifacts in reconstructed CT image volumes. Due to cardiac motion, common metal artifact reduction methods which assume a static object during CT acquisition are not applicable. We propose a fully automatic Dynamic Pacemaker Artifact Reduction (DyPAR+) pipeline which is built of three convolutional neural network (CNN) ensembles. In a first step, pacemaker metal shadows are segmented directly in the raw projection data by the SegmentationNets. Second, resulting metal shadow masks are passed to the InpaintingNets which replace metal-affected line integrals in the sinogram for subsequent reconstruction of a metal-free image volume. Third, the metal locations in a pre-selected motion state are predicted by the ReinsertionNets based on a stack of partial angle back-projections generated from the segmented metal shadow mask. We generate the data required for the supervised learning processes by introducing synthetic, moving pacemaker leads into 14 clinical cases without pacemakers. The SegmentationNets and the ReinsertionNets achieve average Dice coefficients of 94.16% ± 2.01% and 55.60% ± 4.79% during testing on clinical data with synthetic metal leads. With a mean absolute reconstruction error of 11.54 HU ± 2.49 HU in the image domain, the InpaintingNets outperform the hand-crafted approaches PatchMatch and inverse distance weighting. Application of the proposed DyPAR+ pipeline to nine clinical test cases with real pacemakers leads to significant reduction of metal artifacts and demonstrates the transferability to clinical practice. Especially the SegmentationNets and InpaintingNets generalize well to unseen acquisition modes and contrast protocols.

Motion estimation and correction in cardiac CT angiography images using convolutional neural networks.

Cardiac motion artifacts frequently reduce the interpretability of coronary computed tomography angiography (CCTA) images and potentially lead to misinterpretations or preclude the diagnosis of coronary artery disease (CAD). In this paper, a novel motion compensation approach dealing with Coronary Motion estimation by Patch Analysis in CT data (CoMPACT) is presented. First, the required data for supervised learning is generated by the Coronary Motion Forward Artifact model for CT data (CoMoFACT) which introduces simulated motion to 19 artifact-free clinical CT cases with step-and-shoot acquisition protocol. Second, convolutional neural networks (CNNs) are trained to estimate underlying 2D motion vectors from 2.5D image patches based on the coronary artifact appearance. In a phantom study with computer-simulated vessels, CNNs predict the motion direction and the motion magnitude with average test accuracies of 13.37°±1.21° and 0.77 ±â€¯0.09 mm, respectively. On clinical data with simulated motion, average test accuracies of 34.85°±2.09° and 1.86 ±â€¯0.11 mm are achieved, whereby the precision of the motion direction prediction increases with the motion magnitude. The trained CNNs are integrated into an iterative motion compensation pipeline which includes distance-weighted motion vector extrapolation. Alternating motion estimation and compensation in twelve clinical cases with real cardiac motion artifacts leads to significantly reduced artifact levels, especially in image data with severe artifacts. In four observer studies, mean artifact levels of 3.08 ±â€¯0.24 without MC and 2.28 ±â€¯0.29 with CoMPACT MC are rated in a five point Likert scale.

Motion artifact recognition and quantification in coronary CT angiography using convolutional neural networks.

Excellent image quality is a primary prerequisite for diagnostic non-invasive coronary CT angiography. Artifacts due to cardiac motion may interfere with detection and diagnosis of coronary artery disease and render subsequent treatment decisions more difficult. We propose deep-learning-based measures for coronary motion artifact recognition and quantification in order to assess the diagnostic reliability and image quality of coronary CT angiography images. More specifically, the application, steering and evaluation of motion compensation algorithms can be triggered by these measures. A Coronary Motion Forward Artifact model for CT data (CoMoFACT) is developed and applied to clinical cases with excellent image quality to introduce motion artifacts using simulated motion vector fields. The data required for supervised learning is generated by the CoMoFACT from 17 prospectively ECG-triggered clinical cases with controlled motion levels on a scale of 0-10. Convolutional neural networks achieve an accuracy of 93.3% ±â€¯1.8% for the classification task of separating motion-free from motion-perturbed coronary cross-sectional image patches. The target motion level is predicted by a corresponding regression network with a mean absolute error of 1.12 ±â€¯0.07. Transferability and generalization capabilities are demonstrated by motion artifact measurements on eight additional CCTA cases with real motion artifacts.

Multivariate respiratory motion prediction.

In extracranial robotic radiotherapy, tumour motion is compensated by tracking external and internal surrogates. To compensate system specific time delays, time series prediction of the external optical surrogates is used. We investigate whether the prediction accuracy can be increased by expanding the current clinical setup by an accelerometer, a strain belt and a flow sensor. Four previously published prediction algorithms are adapted to multivariate inputs-normalized least mean squares (nLMS), wavelet-based least mean squares (wLMS), support vector regression (SVR) and relevance vector machines (RVM)-and evaluated for three different prediction horizons. The measurement involves 18 subjects and consists of two phases, focusing on long term trends (M1) and breathing artefacts (M2). To select the most relevant and least redundant sensors, a sequential forward selection (SFS) method is proposed. Using a multivariate setting, the results show that the clinically used nLMS algorithm is susceptible to large outliers. In the case of irregular breathing (M2), the mean root mean square error (RMSE) of a univariate nLMS algorithm is 0.66 mm and can be decreased to 0.46 mm by a multivariate RVM model (best algorithm on average). To investigate the full potential of this approach, the optimal sensor combination was also estimated on the complete test set. The results indicate that a further decrease in RMSE is possible for RVM (to 0.42 mm). This motivates further research about sensor selection methods. Besides the optical surrogates, the sensors most frequently selected by the algorithms are the accelerometer and the strain belt. These sensors could be easily integrated in the current clinical setup and would allow a more precise motion compensation.

Long- and short-term in vitro D-dimer stability measured with INNOVANCE D-Dimer.

In vitro D-dimer stability in plasma is widely assumed, but has not yet been documented by systematic studies using samples covering a wide range of D-dimer. We investigated the short- and long-term stability of D-dimer in clinical citrated plasma samples with normal and pathological levels. The short-term stability was analysed by measuring D-dimer fresh, after storage of plasma for 4 hours at room temperature (RT) and after an additional 24 h storage at +2 to +8 degrees C (n=40). Long-term stability samples (n=40) were measured fresh and after storage for 19, 25 and 36 months at < or =-60 degrees C. The effect of repeated freezing was analysed by measuring samples (n=50) fresh and after four consecutive freeze-thaw cycles. D-dimer was measured on the BCS System using the INNOVANCE D-Dimer assay (Siemens Healthcare Diagnostics Products GmbH, Marburg, Germany). D-dimer values at baseline ranged from 0.23-22.2 mg/l FEU. The mean percentage change after storage for 4 hours at RT and additional 24 hours at +2 to +8 degrees C was +3.8% and +2.7%, respectively. The mean percentage change after frozen storage for 19, 25 and 36 months at < or =-60 degrees C was -11.7%, -4.8% and -9.3%, respectively. The small decrease of D-dimer values after frozen storage was not time-dependent. Repeated freezing did not significantly alter D-dimer values (mean change < or =5%). The data demonstrate stability of D-dimer in plasma prior to freezing for up to 4 hours at RT and for up to 24 hours at +2 to +8 degrees C as well as in plasma stored for up to three years at < or =-60 degrees C.

Expression of fatty acid-binding protein from bovine heart in Escherichia coli.

The coding part of the cDNA of cardiac fatty acid-binding protein (cFABP) from bovine heart was cloned into the vector pKK233-2. After induction with isopropyl-beta-D-thiogalactopyranoside cFABP was found in a soluble form in the cytosol of plasmid transformed E. coli amounting up to 5.7% of the soluble protein. cFABP was detected after SDS-polyacrylamide gelelectrophoresis and/or isoelectric focusing and Western blot by immuno-staining and was determined quantitatively by a solid phase enzyme-linked immuno sorbent assay. The cFABP produced by bacteria binds oleic acid with high affinity as shown by comigration of protein and ligand in both gelfiltration and isoelectric focusing. cFABP was purified from bacterial lysates to near homogeneity and resolved into four isoproteins.

Cloning of a full-length complementary DNA for fatty-acid-binding protein from bovine heart.

A full-length cDNA for bovine heart fatty-acid-binding protein (H-FABP) was cloned from a lambda gt11 cDNA library established from bovine heart muscle. The cDNA sequence shows an open reading frame coding for a protein with 133 amino acids. Colinearity with the amino acid sequences of four tryptic peptides was asserted. H-FABP isolated from bovine heart begins with an N-acetylated valine residue, however, as derived from analysis of the tryptic, amino-terminal-blocked peptide and the molecular mass of the peptide obtained via secondary-ion mass spectrometry. The molecular mass of the total protein is 14673 Da. Bovine H-FABP is 89% homologous to rat H-FABP and 97% homologous to the bovine mammary-derived growth-inhibition factor described recently by Böhmer et al. [J. Biol. Chem. 262, 15137-15143 (1987)]. Significant homologies were also found with bovine myelin protein P2 and murine adipocyte protein p422. Secondary-structure predictions were proposed for these proteins, based on computer analysis, which reveal striking similarities.

The entire nucleotide sequence of the genome of human hepatitis A virus (isolate MBB).

Hepatitis A virus (HAV) is an important human pathogen causing hepatitis, with high incidence in developed as well as in developing countries. No vaccines are available. In order to determine the primary structure of the HAV genome, we have prepared cDNAs from viral RNA and cloned these into plasmid pBR322. These clones were used to determine the entire nucleotide sequence of the HAV RNA by rapid sequencing methods. We have compared this sequence of 7470 bases to known partial sequences, and one complete sequence of HAV RNA which were obtained recently from different strains of HAV. It is hoped that a comparison of sequence data from different isolates will help in the elucidation of the unusual growth pattern of HAV. In addition, it might provide helpful information about the immunological determinants that elicit the antibody response to infection.