Technological trampolines for new venture creation in Catalonia: the case of the University of Girona

Recent trends in technology transfer show an intensification of spin-off creation as a modality of university research commercialisation, complementary to the conventional ones, contract research and licensing. In this paper we analyse the evolution, objectives, resources and activities of a specialised unit –Technological Trampoline (TT) - in charge of new venture creation at the University of Girona (Catalonia-Spain). Based on two theoretical frameworks, Resource-based-view and Institutional Theory, we adopt a multi-dimensional approach to study the strategy of spinning-off new ventures at the University of Girona in terms of resources and activities, how this process is organised and if the outputs fit with this UdG’s objectives and the local environment. Our main contribution is an in-depth analysis of the spin-off creation unit with special emphasis on its variety of resources and activities. The results have a series of implications and recommendations at both university and TT level.

Revisiting the compositional data. Some fundamental questions and new prospects in Archaeometry and Archaeology

In this paper we examine the problem of compositional data from a different startingpoint. Chemical compositional data, as used in provenance studies on archaeologicalmaterials, will be approached from the measurement theory. The results will show, in avery intuitive way that chemical data can only be treated by using the approachdeveloped for compositional data. It will be shown that compositional data analysis is aparticular case in projective geometry, when the projective coordinates are in thepositive orthant, and they have the properties of logarithmic interval metrics. Moreover,it will be shown that this approach can be extended to a very large number ofapplications, including shape analysis. This will be exemplified with a case study inarchitecture of Early Christian churches dated back to the 5th-7th centuries AD

Two-sided learning in new keynesian models: dynamics, (lack of) convergence and the value of information

This paper investigates the role of learning by private agents and the central bank (two-sided learning) in a New Keynesian framework in which both sides of the economy have asymmetric and imperfect knowledge about the true data generating process. We assume that all agents employ the data that they observe (which may be distinct for different sets of agents) to form beliefs about unknown aspects of the true model of the economy, use their beliefs to decide on actions, and revise these beliefs through a statistical learning algorithm as new information becomes available. We study the short-run dynamics of our model and derive its policy recommendations, particularly with respect to central bank communications. We demonstrate that two-sided learning can generate substantial increases in volatility and persistence, and alter the behavior of the variables in the model in a signifficant way. Our simulations do not converge to a symmetric rational expectations equilibrium and we highlight one source that invalidates the convergence results of Marcet and Sargent (1989). Finally, we identify a novel aspect of central bank communication in models of learning: communication can be harmful if the central bank's model is substantially mis-specified

Synthesis, binding affinity and SAR of new benzolactam derivatives as dopamine D3 receptor ligands

A series of new benzolactam derivatives was synthesized and the derivatives were evaluated for theiraffinities at the dopamine D1, D2, and D3 receptors. Some of these compounds showed high D2 and/orD3 affinity and selectivity over the D1 receptor. The SAR study of these compounds revealed structuralcharacteristics that decisively influenced their D2 and D3 affinities. Structural models of the complexesbetween some of the most representative compounds of this series and the D2 and D3 receptors wereobtained with the aim of rationalizing the observed experimental results. Moreover, selected compoundsshowed moderate binding affinity on 5-HT2A which could contribute to reducing the occurrence of extrapyramidalside effects as potential antipsychotics.

Synthesis, binding affinity, and molecular docking analysis of new benzofuranone derivative as pontential antipsychotics

The complex etiology of schizophrenia has prompted researchers to develop clozapine-related multitargetstrategies to combat its symptoms. Here we describe a series of new 6-aminomethylbenzofuranones in aneffort to find new chemical structures with balanced affinities for 5-HT2 and dopamine receptors. Throughbiological and computational studies of 5-HT2A and D2 receptors, we identified the receptor serine residuesS3.36 and S5.46 as the molecular keys to explaining the differences in affinity and selectivity betweenthese new compounds for this group of receptors. Specifically, the ability of these compounds to establishone or two H-bonds with these key residues appears to explain their difference in affinity. In addition, wedescribe compound 2 (QF1004B) as a tool to elucidate the role of 5-HT2C receptors in mediating antipsychoticeffects and metabolic adverse events. The compound 16a (QF1018B) showed moderate to high affinitiesfor D2 and 5-HT2A receptors, and a 5-HT2A/D2 ratio was predictive of an atypical antipsychotic profile.

Two-sided learning in New Keynesian models: Dynamics, (lack of) convergence and the value of information

This paper investigates the role of learning by private agents and the central bank(two-sided learning) in a New Keynesian framework in which both sides of the economyhave asymmetric and imperfect knowledge about the true data generating process. Weassume that all agents employ the data that they observe (which may be distinct fordifferent sets of agents) to form beliefs about unknown aspects of the true model ofthe economy, use their beliefs to decide on actions, and revise these beliefs througha statistical learning algorithm as new information becomes available. We study theshort-run dynamics of our model and derive its policy recommendations, particularlywith respect to central bank communications. We demonstrate that two-sided learningcan generate substantial increases in volatility and persistence, and alter the behaviorof the variables in the model in a significant way. Our simulations do not convergeto a symmetric rational expectations equilibrium and we highlight one source thatinvalidates the convergence results of Marcet and Sargent (1989). Finally, we identifya novel aspect of central bank communication in models of learning: communicationcan be harmful if the central bank's model is substantially mis-specified.