Efficient algorithms for online learning over graphs

In this thesis we consider the problem of online learning with labelled graphs, in particular designing algorithms that can perform this problem quickly and with low memory requirements. We consider the tasks of Classification (in which we are asked to predict the labels of vertices) and Similarity Prediction (in which we are asked to predict whether two given vertices have the same label). The first half of the thesis considers non- probabilistic online learning, where there is no probability distribution on the labelling and we bound the number of mistakes of an algorithm by a function of the labelling’s complexity (i.e. its “naturalness"), often the cut- size. The second half of the thesis considers probabilistic machine learning in which we have a known probability distribution on the labelling. Before considering probabilistic online learning we first analyse the junction tree algorithm, on which we base our online algorithms, and design a new ver- sion of it, superior to the otherwise current state of the art. Explicitly, the novel contributions of this thesis are as follows: • A new algorithm for online prediction of the labelling of a graph which has better performance than previous algorithms on certain graph and labelling families. • Two algorithms for online similarity prediction on a graph (a novel problem solved in this thesis). One performs very well whilst the other not so well but which runs exponentially faster. • A new (better than before, in terms of time and space complexity) state of the art junction tree algorithm, as well as an application of it to the problem of online learning in an Ising model. • An algorithm that, in linear time, finds the optimal junction tree for online inference in tree-structured Ising models, the resulting online junction tree algorithm being far superior to the previous state of the art. All claims in this thesis are supported by mathematical proofs

FGD2, a CDC42-specific Exchange Factor Expressed by Antigen-presenting Cells, Localizes to Early Endosomes and Active Membrane Ruffles*S⃞

Members of the Fgd (faciogenital dysplasia) gene family encode a group of critical guanine nucleotide exchange factors (GEFs), which, by specifically activating Cdc42, control cytoskeleton-dependent membrane rearrangements. In its first characterization, we find that FGD2 is expressed in antigen-presenting cells, including B lymphocytes, macrophages, and dendritic cells. In the B lymphocyte lineage, FGD2 levels change with developmental stage. In both mature splenic B cells and immature bone marrow B cells, FGD2 expression is suppressed upon activation through the B cell antigen receptor. FGD2 has a complex intracellular localization, with concentrations found in membrane ruffles and early endosomes. Although endosomal localization of FGD2 is dependent on a conserved FYVE domain, its C-terminal pleckstrin homology domain mediates recruitment to membrane ruffles. FGD2 overexpression promotes the activation of Cdc42 and leads to elevated JNK1 activity in a Cdc42- but not Rac1-dependent fashion. These findings are consistent with a role of FGD2 in leukocyte signaling and vesicle trafficking in cells specialized to present antigen in the immune system

CDC42 and FGD1 Cause Distinct Signaling and Transforming Activities

Activated forms of different Rho family members (CDC42, Rac1, RhoA, RhoB, and RhoG) have been shown to transform NIH 3T3 cells as well as contribute to Ras transformation. Rho family guanine nucleotide exchange factors (GEFs) (also known as Dbl family proteins) that activate CDC42, Rac1, and RhoA also demonstrate oncogenic potential. The faciogenital dysplasia gene product, FGD1, is a Dbl family member that has recently been shown to function as a CDC42-specific GEF. Mutations within the FGD1 locus cosegregate with faciogenital dysplasia, a multisystemic disorder resulting in extensive growth impairments throughout the skeletal and urogenital systems. Here we demonstrate that FGD1 expression is sufficient to cause tumorigenic transformation of NIH 3T3 fibroblasts. Although both FGD1 and constitutively activated CDC42 cooperated with Raf and showed synergistic focus-forming activity, both quantitative and qualitative differences in their functions were seen. FGD1 and CDC42 also activated common nuclear signaling pathways. However, whereas both showed comparable activation of c-Jun, CDC42 showed stronger activation of serum response factor and FGD1 was consistently a better activator of Elk-1. Although coexpression of FGD1 with specific inhibitors of CDC42 function demonstrated the dependence of FGD1 signaling activity on CDC42 function, FGD1 signaling activities were not always consistent with the direct or exclusive stimulation of CDC42 function. In summary, FGD1 and CDC42 signaling and transformation are distinct, thus suggesting that FGD1 may be mediating some of its biological activities through non-CDC42 targets