The relationship between Tourism and Internet

Sen, M.; Yapici, F. (2011). The relationship between Tourism and Internet. Universitat Politècnica de València. http://hdl.handle.net/10251/12229Archivo delegad

An internal ligand-bound, metastable state of a leukocyte integrin, αXβ2

How is massive conformational change in integrins achieved on a rapid timescale? We report crystal structures of a metastable, putative transition state of integrin αXβ2. The αXβ2 ectodomain is bent; however, a lattice contact stabilizes its ligand-binding αI domain in a high affinity, open conformation. Much of the αI α7 helix unwinds, loses contact with the αI domain, and reshapes to form an internal ligand that binds to the interface between the β propeller and βI domains. Lift-off of the αI domain above this platform enables a range of extensional and rotational motions without precedent in allosteric machines. Movements of secondary structure elements in the β2 βI domain occur in an order different than in β3 integrins, showing that integrin β subunits can be specialized to assume different intermediate states between closed and open. Mutations demonstrate that the structure trapped here is metastable and can enable rapid equilibration between bent and extended-open integrin conformations and up-regulation of leukocyte adhesiveness

Max-margin stacking with group sparse regularization for classifier combination

Multiple classifier systems are shown to be effective in terms of accuracy for multiclass classification problems with the expense of increased complexity. Classifier combination studies deal with the methods of combining the outputs of base classifiers of an ensemble. Stacked generalization, or stacking, is shown to be a strong combination scheme among combination algorithms; and in this thesis, we improve stacking's performance further in terms of both accuracy and complexity. We investigate four main issues for this purpose. First, we show that margin maximizing combiners outperform the conventional least-squares estimation of the weights. Second we incorporate the idea of group sparsity into regularization to facilitate classifier selection. Third, we develop non-linear versions of class-conscious linear combination types by transforming datasets into binary classification datasets; then applying the kernel trick. And finally, we derive a new optimization algorithm based on the majorization-minimization framework for a particular linear combination type, which we show is the most preferable one