Relevance of strategic management for universities.

Management; Strategic management; University;

Protein-protein interactions: network analysis and applications in drug discovery

Physical interactions among proteins constitute the backbone of cellular function, making them an attractive source of therapeutic targets. Although the challenges associated with targeting protein-protein interactions (PPIs) -in particular with small molecules are considerable, a growing number of functional PPI modulators is being reported and clinically evaluated. An essential starting point for PPI inhibitor screening or design projects is the generation of a detailed map of the human interactome and the interactions between human and pathogen proteins. Different routes to produce these biological networks are being combined, including literature curation and computational methods. Experimental approaches to map PPIs mainly rely on the yeast two-hybrid (Y2H) technology, which have recently shown to produce reliable protein networks. However, other genetic and biochemical methods will be essential to increase both coverage and resolution of current protein networks in order to increase their utility towards the identification of novel disease-related proteins and PPIs, and their potential use as therapeutic targets

A Hybrid Micromachined High -Q Cavity Resonator at 5.8 GHz

A novel hybrid micromachined resonator with high quality factor and small size at 5.8GHz is presented. The design of the resonator is based on a micromachined cavity filled with a high dielectric constant material. Energy is coupled into the cavity from input and output microstrip lines via slots. It is shown experimentally that the limiting factor in achieving a higher Q with the given dielectric materials is the dielectric loss. This resonator provides a low cost, minimum size and compact solution for the fabrication of planar, narrow-band filters and diplexers in modern wireless communication systems.

Influence of the lung microbiome on antibiotic susceptibility of cystic fibrosis pathogens

The lungs of patients with cystic fibrosis (CF) are colonised by a microbial community comprised of pathogenic species, such as Pseudomonas aeruginosa and Staphylococcus aureus, and microorganisms that are typically not associated with worse clinical outcomes (considered as commensals). Antibiotics directed at CF pathogens are often not effective and a discrepancy is observed between activity of these agents in vitro and in the patient. This review describes how interspecies interactions within the lung microbiome might influence the outcome of antibiotic treatment targeted at common CF pathogens. Protective mechanisms by members of the microbiome such as antibiotic degradation (indirect pathogenicity), alterations of the cell wall, production of matrix components decreasing antibiotic penetration, and changes in metabolism are discussed. Interspecies interactions that increase bacterial susceptibility are also addressed. Furthermore, we discuss how experimental conditions, such as culture media, oxygen levels, incorporation of host-pathogen interactions, and microbial community composition may influence the outcome of microbial interaction studies related to antibiotic activity. Hereby, the importance to create in vitro conditions reflective of the CF lung microenvironment is highlighted. Understanding the role of the CF lung microbiome in antibiotic efficacy may help find novel therapeutic and diagnostic approaches to better tackle chronic lung infections in this patient population

Unbiased protein association study on the public human proteome reveals biological connections between co-occurring protein pairs

Mass-spectrometry-based, high-throughput proteomics experiments produce large amounts of data. While typically acquired to answer specific biological questions, these data can also be reused in orthogonal wayS to reveal new biological knowledge. We here present a novel method for such orthogonal data reuse of public proteomics data. Our method elucidates biological relationships between proteins based on the co-occurrence of these proteins across human experiments in the PRIDE database. The majority of the significantly co-occurring protein pairs that were detected by our method have been successfully mapped to existing biological knowledge. The validity of our novel method is substantiated by the extremely few pairs that can be mapped to existing knowledge based on random associations between the same set of proteins. Moreover, using literature searches and the STRING database, we were able to derive meaningful biological associations for unannotated protein pairs that were detected using our method, further illustrating that as-yet unknown associations present highly interesting targets for follow-up analysis