Interaction of ultrarelativistic electron and proton bunches with dense plasmas

Here we discuss the possibility of employment of ultrarelativistic electron and proton bunches for generation of high plasma wakefields in dense plasmas due to the Cherenkov resonance plasma-bunch interaction. We estimate the maximum amplitude of such a wake and minimum system length at which the maximum amplitude can be generated at the given bunch parameters.Comment: 8 page

Agonist-induced formation of FGFR1 homodimers and signaling differ among members of the FGF family.

Fibroblast growth factor receptor 1 (FGFR1) is known to be activated by homodimerization in the presence of both the FGF agonist ligand and heparan sulfate glycosaminoglycan. FGFR1 homodimers in turn trigger a variety of downstream signaling cascades via autophosphorylation of tyrosine residues in the cytoplasmic domain of FGFR1. By means of Bioluminescence Energy Resonance Transfer (BRET) as a sign of FGFR1 homodimerization, we evaluated in HEK293T cells the effects of all known FGF agonist ligands on homodimer formation. A significant correlation between BRET(2) signaling and ERK1/2 phosphorylation was observed, leading to a further characterization of the binding and signaling properties of the FGF subfamilies. FGF agonist ligand-FGFR1 binding interactions appear as the main mechanism for the control of FGFR1 homodimerization and MAPK signaling which demonstrated a high correlation. The bioinformatic analysis demonstrates the interface of the two pro-triplets SSS (Ser-Ser-Ser) and YGS (Tyr-Gly-Ser) located in the extracellular and intracellular domain of the FGFR1. These pro-triplets are postulated participate in the FGFR1 homodimerization interface interaction. The findings also reveal that FGF agonist ligands within the same subfamily of the FGF gene family produced similar increases in FGFR1 homodimer formation and MAPK signaling. Thus, the evolutionary relationship within this gene family appears to have a distinct functional relevance

Estimation of social network user's influence in a given area of expertise

Nowadays social networks are frequently used to express personal opinion on a topic of interest. Some users' opinion has more informational influence than others do. These users are called influential users. There are services that allow evaluating how popular and influential users are; however, any information on evaluation methods is proprietary and represents know-how of such software services. Furthermore, most services could not provide extensive data on the influential users within the specified area of knowledge. This article proposes the method of evaluating a user influence index within a social network in a given area of expertise