Computing pseudotriangulations via branched coverings

We describe an efficient algorithm to compute a pseudotriangulation of a finite planar family of pairwise disjoint convex bodies presented by its chirotope. The design of the algorithm relies on a deepening of the theory of visibility complexes and on the extension of that theory to the setting of branched coverings. The problem of computing a pseudotriangulation that contains a given set of bitangent line segments is also examined.Comment: 66 pages, 39 figure

Clinical genetic testing in cardiomyopathies

The completion of the Human Genome Project was a landmark achievement that revealed the reference DNA sequence for our own genome. Almost immediately it became clear that there was no single reference DNA sequence, as even the approximately half-dozen human DNA samples used by the Human Genome Project contained tens of thousands of variations [1]. As clinical genetic testing becomes more mainstream, and various projects underway perform full DNA genome sequencing in thousands of individuals, the extent of this genetic variation is increasingly being appreciated. It is widely recognized that most of this variation is probably not relevant for determining health or risk of disease and it has been collectively referred to as genetic noise. As in much of biology, separation of the signal from the noise can be challenging, and as molecular genetic sequencing expands in use and in the total length of DNA that can be sequenced in a single assay, problems in distinguishing a diagnostic genetic change from background genetic variation will remain a difficult task for researchers and clinicians to fulfill. Newer DNA sequencing technology can now complete the sequencing of an entire human genome several times in a matter of days, which is orders of magnitude faster than the nearly 13 years required for the initial first-pass done by the Human Genome Project consortium [2]. This technology, which will shortly be widely used in clinical genetic testing, will undoubtedly add new challenges to the difficulty of distinguishing signal from noise