Phosphorylation of survivin at threonine 34 inhibits its mitotic function and enhances its cytoprotective activity

Survivin is an essential chromosomal passenger protein required for mitotic progression. It is also an inhibitor of apoptosis and can prevent caspase-mediated cell death. In addition, survivin levels are elevated in cancer cells where its presence correlates with increased resistance to chemo- and radio-therapy, which makes it an attractive target for novel anti-cancer strategies. Interestingly, survivin is phosphorylated by the mitotic kinase, cdk1, and a non-phosphorylatable form, survivin(T34A), cannot inhibit apoptosis. Here we rigorously test the ability of survivin(T34A) and its corresponding phosphomimetic, survivin(T34E), to promote cell viability through survivin's dual roles. The effects of these mutations are diametrically opposed: survivin(T34A) accelerates cell proliferation and promotes apoptosis, whereas survivin(T34E) retards growth and promotes survival. Thus the phosphorylation status of survivin at T34 is pivotal to a cell's decision to live or die

Shrinkage Function And Its Applications In Matrix Approximation

The shrinkage function is widely used in matrix low-rank approximation, compressive sensing, and statistical estimation. In this article, an elementary derivation of the shrinkage function is given. In addition, applications of the shrinkage function are demonstrated in solving several well-known problems, together with a new result in matrix approximation

Training Big Random Forests with Little Resources

Without access to large compute clusters, building random forests on large datasets is still a challenging problem. This is, in particular, the case if fully-grown trees are desired. We propose a simple yet effective framework that allows to efficiently construct ensembles of huge trees for hundreds of millions or even billions of training instances using a cheap desktop computer with commodity hardware. The basic idea is to consider a multi-level construction scheme, which builds top trees for small random subsets of the available data and which subsequently distributes all training instances to the top trees' leaves for further processing. While being conceptually simple, the overall efficiency crucially depends on the particular implementation of the different phases. The practical merits of our approach are demonstrated using dense datasets with hundreds of millions of training instances.Comment: 9 pages, 9 Figure