Development and validation of AMANDA, a new algorithm for selecting highly relevant regions in Molecular Interaction Fields

Descriptors based on Molecular Interaction Fields (MIF) are highly suitable for drug discovery, but their size (thousands of variables) often limits their application in practice. Here we describe a simple and fast computational method that extracts from a MIF a handful of highly informative points (hot spots) which summarize the most relevant information. The method was specifically developed for drug discovery, is fast, and does not require human supervision, being suitable for its application on very large series of compounds. The quality of the results has been tested by running the method on the ligand structure of a large number of ligand-receptor complexes and then comparing the position of the selected hot spots with actual atoms of the receptor. As an additional test, the hot spots obtained with the novel method were used to obtain GRIND-like molecular descriptors which were compared with the original GRIND. In both cases the results show that the novel method is highly suitable for describing ligand-receptor interactions and compares favorably with other state-of-the-art methods.

Human disease and drug pharmacology, complex as real life

In the past decades drug discovery practice has escaped from the complexity of the formerly used phenotypic screening in animals to focus on assessing drug effects on isolated protein targets in the search for drugs that exclusively and potently hit one selected target, thought to be critical for a given disease, while not affecting at all any other target to avoid the occurrence of side-effects. However, reality does not conform to these expectations, and, conversely, this approach has been concurrent with increased attrition figures in late-stage clinical trials, precisely due to lack of efficacy and safety. In this context, a network biology perspective of human disease and treatment has burst into the drug discovery scenario to bring it back to the consideration of the complexity of living organisms and particularly of the (patho)physiological environment where protein targets are (mal)functioning and where drugs have to exert their restoring action. Under this perspective, it has been found that usually there is not one but several disease-causing genes and, therefore, not one but several relevant protein targets to be hit, which do not work on isolation but in a highly interconnected manner, and that most known drugs are inherently promiscuous. In this light, the rationale behind the currently prevailing single-target-based drug discovery approach might even seem a Utopia, while, conversely, the notion that the complexity of human disease must be tackled with complex polypharmacological therapeutic interventions constitutes a difficult-torefuse argument that is spurring the development of multitarget therapies.

Human disease and drug pharmacology, complex as real life

In the past decades drug discovery practice has escaped from the complexity of the formerly used phenotypic screening in animals to focus on assessing drug effects on isolated protein targets in the search for drugs that exclusively and potently hit one selected target, thought to be critical for a given disease, while not affecting at all any other target to avoid the occurrence of side-effects. However, reality does not conform to these expectations, and, conversely, this approach has been concurrent with increased attrition figures in late-stage clinical trials, precisely due to lack of efficacy and safety. In this context, a network biology perspective of human disease and treatment has burst into the drug discovery scenario to bring it back to the consideration of the complexity of living organisms and particularly of the (patho)physiological environment where protein targets are (mal)functioning and where drugs have to exert their restoring action. Under this perspective, it has been found that usually there is not one but several disease-causing genes and, therefore, not one but several relevant protein targets to be hit, which do not work on isolation but in a highly interconnected manner, and that most known drugs are inherently promiscuous. In this light, the rationale behind the currently prevailing single-target-based drug discovery approach might even seem a Utopia, while, conversely, the notion that the complexity of human disease must be tackled with complex polypharmacological therapeutic interventions constitutes a difficult-torefuse argument that is spurring the development of multitarget therapies.

Stability and asymptotic analysis of a fluid-particle interaction model

We are interested in coupled microscopic/macroscopic models describing the evolution of particles dispersed in a fluid. The system consists in a Vlasov-Fokker-Planck equation to describe the microscopic motion of the particles coupled to the Euler equations for a compressible fluid. We investigate dissipative quantities, equilibria and their stability properties and the role of external forces. We also study some asymptotic problems, their equilibria and stability and the derivation of macroscopic two-phase models.

External sources of innovation and industry-university interaction:evidence from Spanish firms

This paper is about the firm innovation process and the cooperation of the innovative firms with other firms and public institutions. A special attention is paid to the cooperation with universities. We use the Technological Innovation Survey (TIS) from the Instituto Nacional de Estadística (Spain) in order to obtain data for 4,159 innovative firms. Our results show that firm's cooperation activities are closely linked to the characteristics of the industry and the firm as well as to the origin of public funds for R&D activities. Key words: Innovation, universities, Spanish economy. JEL code: O31, I20, L60

Spillovers and growth in a local interaction model

In this paper we aim at studying to what extent spillovers between firms may foster economic growth. The attention is addressed to the spillovers connected with the R&D activity that improves the quality of the goods firms supply. Our model develops a growth theory framework and we assume that firms spread around a circle. Our study assesses that spillovers between neighbors affect the probability of successful research for each of them. In particular, spillovers are the forces fuelling growth when, on the whole, firms turn out to be net receivers with respect to their neighbors.

Stable stationary states of non-local interaction equations

"Vegeu el resum a l'inici del document del fitxer adjunt."

A glimpse into classroom interaction

How does classroom interaction contribute to language learning? This study aims at identifying and interpreting some patterns of teacher-student interaction within an EFL classroom. Different interactional patterns and strategies are examined through the self-observation of the teacher's own performance as a student-teacher during her practicum period in a secondary school

Teacher strategies to elicit knowledge from students in the CLIL classroom: the purpose of questions in controlled patterns of teacher-student interaction

This Master Dissertation comprises two parts: a personal reflection and an empirical study. The personal reflection reviews the process of professionalization undergone by its author throughout the Master. The empirical study tackles teacher strategies to elicit knowledge from students in the CLIL classroom and more specifically the purpose of questions in controlled patterns of teacher-student interaction. The theories of relevant authors such as Vigotsky, Mercer and Tsui are used as a framework to analyze the data presented. The analysis shows the different strategies to elicit knowledge used by the teacher and the appropriateness of her questions in the analyzed interaction

How to avoid gender bias in classroom interaction? A cooperative learning experience

This project aims to analyse the kind of questions the teacher asks students in order to encourage them to participate in her classes. Consequently, the researcher has read relevant literature and has analysed a short excerpt of a video recorded during her first practicum. She has also analysed a number of activities carried out during her second practicum in order to find out if she had improved her questioning skills in the classroom

Repeated Interaction and the Revelation of the Monitor's Type: A Principal-Monitor-Agent Problem

This paper studies a dynamic principal-monitor-agent relation where a strategic principal delegates the task of monitoring the effort of a strategic agent to a third party. The latter we call the monitor, whose type is initially unknown. Through repeated interaction the agent might learn his type. We show that this process damages the principal's payoffs. Compensation is assumed exogenous, limiting to a great extent the provision of incentives. We go around this difficulty by introducing costly replacement strategies, i.e. the principal replaces the monitor, thus disrupting the agent's learning. We found that even when replacement costs are null, if the revealed monitor is strictly preferred by both parties, there is a loss in efficiency due to the impossibility of bene…tting from it. Nonetheless, these strategies can partially recover the principal's losses. Additionally, we establish upper and lower bounds on the payoffs that the principal and the agent can achieve. Finally we characterize the equilibrium strategies under public and private monitoring (with communication) for different cost and impatience levels.