Tyrosine phosphorylation of cortactin by the FAK-Src complex at focal adhesions regulates cell motility.

BackgroundCell migration plays an important role in many physiological and pathological processes, including immune cell chemotaxis and cancer metastasis. It is a coordinated process that involves dynamic changes in the actin cytoskeleton and its interplay with focal adhesions. At the leading edge of a migrating cell, it is the re-arrangement of actin and its attachment to focal adhesions that generates the driving force necessary for movement. However, the mechanisms involved in the attachment of actin filaments to focal adhesions are still not fully understood.ResultsSignaling by the FAK-Src complex plays a crucial role in regulating the formation of protein complexes at focal adhesions to which the actin filaments are attached. Cortactin, an F-actin associated protein and a substrate of Src kinase, was found to interact with FAK through its SH3 domain and the C-terminal proline-rich regions of FAK. We found that the autophosphorylation of Tyr(397) in FAK, which is necessary for FAK activation, was not required for the interaction with cortactin, but was essential for the tyrosine phosphorylation of the associated cortactin. At focal adhesions, cortactin was phosphorylated at tyrosine residues known to be phosphorylated by Src. The tyrosine phosphorylation of cortactin and its ability to associate with the actin cytoskeleton were required in tandem for the regulation of cell motility. Cell motility could be inhibited by truncating the N-terminal F-actin binding domains of cortactin or by blocking tyrosine phosphorylation (Y421/466/475/482F mutation). In addition, the mutant cortactin phosphorylation mimic (Y421/466/475/482E) had a reduced ability to interact with FAK and promoted cell motility. The promotion of cell motility by the cortactin phosphorylation mimic could also be inhibited by truncating its N-terminal F-actin binding domains.ConclusionsOur results suggest that cortactin acts as a bridging molecule between actin filaments and focal adhesions. The cortactin N-terminus associates with F-actin, while its C-terminus interacts with focal adhesions. The tyrosine phosphorylation of cortactin by the FAK-Src complex modulates its interaction with FAK and increases its turnover at focal adhesions to promote cell motility

Cognitive satellite communications and representation learning for streaming and complex graphs.

This dissertation includes two topics. The first topic studies a promising dynamic spectrum access algorithm (DSA) that improves the throughput of satellite communication (SATCOM) under the uncertainty. The other topic investigates distributed representation learning for streaming and complex networks. DSA allows a secondary user to access the spectrum that are not occupied by primary users. However, uncertainty in SATCOM causes more spectrum sensing errors. In this dissertation, the uncertainty has been addressed by formulating a DSA decision-making process as a Partially Observable Markov Decision Process (POMDP) model to optimally determine which channels to sense and access. Large-scale networks have attracted many attentions to discover the hidden information from big data. Particularly, representation learning embeds the network into a lower vector space while maximally preserving the similarity among nodes. I propose a real-time distributed graph embedding algorithm (RTDGE) which is capable of distributively embedding the streaming graph by combining a novel edge partition approach and an incremental negative sample approach. Furthermore, a platform is prototyped based on Kafka and Storm. Real-time Twitter network data can be retrieved, partitioned and processed for state-of-art tasks. For knowledge graphs, existing works cannot capture the complex connection patterns and never consider the impacts from complicated relations, due to the unquantifiable relationships. A novel embedding algorithm is proposed to hierarchically measure the structural similarity and the impacts from relations by constructing a multi-layer graph. Then, an advanced representation learning model is designed based on an entity\u27s context generated by random walks on the multi-layer content graph

Newsvendor Conditional Value-at-Risk Minimisation with a Non-Parametric Approach

In the classical Newsvendor problem, one must determine the order quantity that maximises the expected profit. Some recent works have proposed an alternative approach, in which the goal is to minimise the conditional value-at-risk (CVaR), a very popular risk measure in financial risk management. Unfortunately, CVaR estimation involves considering observations with extreme values, which poses problems for both parametric and non-parametric methods. Indeed, parametric methods often underestimate the downside risk, which leads to significant losses in extreme cases. The existing non-parametric methods, on the other hand, are extremely computationally expensive for large instances. In this paper, we propose an alternative non-parametric approach to CVaR minimisation that uses only a small proportion of the data. Using both simulation and real-life case studies, we show that the proposed method can be very useful in practice, allowing the decision makers to suffer less downside loss in extreme cases while requiring reasonable computing effort