Mechanical features and in vivo imaging of a polymer stent

A polyethylene-terephthalate (PETP, polyester), self-expanding, braided mesh stent has been developed for percutaneous (coronary) arterial implantation. In vitro measurements showed that the radial pressure delivered by this device was similar to a self-expanding, stainless steel stent. Due to hysteresis-like behaviour, it proved necessary to mount the polymer stent on the delivery system immediately before the placement procedure, and to select a diameter in the unconstrained condition, which was 60% larger than the diameter of the target vessel. Eight polyester stents were implanted in peripheral arteries of four pigs. Except for heparin during the implantation procedure, antithrombotic or antiplatelet drugs were not administered. After four weeks repeat angiography revealed that one of the stents was subtotally occluded. At autopsy, two other stents proved to be located in the aortic bifurcation, probably due to failure of the delivery system. Quantitative angiographic assessment showed that the mean luminal diameters at the site of stent placement were 3.3±0.2 mm before, 3.2±0.2 mm immediately after, and 2.7±0.5 mm at four weeks after implantation. Intravascular ultrasound (IVUS) examination after 4 weeks could identify the individual struts of the stents, as well as their length. In addition, a description of the extent of neointimal hyperplasia was feasible. The IVUS assessment was validated by histological examination. In conclusion, polyester stents can be constructed with mechanical properties similar to stainless steel stents. After implantation in porcine peripheral arteries, five of six correctly placed stents were patent at four weeks. Imaging of stents by angiography and IVUS provided complementary information

Learning biophysically-motivated parameters for alpha helix prediction

<p>Abstract</p> <p>Background</p> <p>Our goal is to develop a state-of-the-art protein secondary structure predictor, with an intuitive and biophysically-motivated energy model. We treat structure prediction as an optimization problem, using parameterizable cost functions representing biological "pseudo-energies". Machine learning methods are applied to estimate the values of the parameters to correctly predict known protein structures.</p> <p>Results</p> <p>Focusing on the prediction of alpha helices in proteins, we show that a model with 302 parameters can achieve a Q_{<it>α </it>}value of 77.6% and an SOV_{<it>α </it>}value of 73.4%. Such performance numbers are among the best for techniques that do not rely on external databases (such as multiple sequence alignments). Further, it is easier to extract biological significance from a model with so few parameters.</p> <p>Conclusion</p> <p>The method presented shows promise for the prediction of protein secondary structure. Biophysically-motivated elementary free-energies can be learned using SVM techniques to construct an energy cost function whose predictive performance rivals state-of-the-art. This method is general and can be extended beyond the all-alpha case described here.</p

Sequencing and de novo assembly of 150 genomes from Denmark as a population reference

Hundreds of thousands of human genomes are now being sequenced to characterize genetic variation and use this information to augment association mapping studies of complex disorders and other phenotypic traits. Genetic variation is identified mainly by mapping short reads to the reference genome or by performing local assembly. However, these approaches are biased against discovery of structural variants and variation in the more complex parts of the genome. Hence, large-scale de novo assembly is needed. Here we show that it is possible to construct excellent de novo assemblies from high-coverage sequencing with mate-pair libraries extending up to 20 kilobases. We report de novo assemblies of 150 individuals (50 trios) from the GenomeDenmark project. The quality of these assemblies is similar to those obtained using the more expensive long-read technology. We use the assemblies to identify a rich set of structural variants including many novel insertions and demonstrate how this variant catalogue enables further deciphering of known association mapping signals. We leverage the assemblies to provide 100 completely resolved major histocompatibility complex haplotypes and to resolve major parts of the Y chromosome. Our study provides a regional reference genome that we expect will improve the power of future association mapping studies and hence pave the way for precision medicine initiatives, which now are being launched in many countries including Denmark

vantage6/vantage6: 3.8.1

The main vantage6 repository: code for the central server, nodes, CLI and Python Clien

vantage6/vantage6: 3.7.3

The main vantage6 repository: code for the central server, nodes, CLI and Python Clien

vantage6/vantage6: 3.8.2

The main vantage6 repository: code for the central server, nodes, CLI and Python Clien

vantage6/vantage6: version/4.1.0

The main vantage6 repository: code for the central server, nodes, CLI and Python Clien