Good vibrations

The work reported in this thesis addresses the research question of when and how positive psychological states impact positive behavior and positive organizational development. We present two theoretical essays and three empirical studies to find possible answers to this question and we use a multitude of methodologies with different epistemological assumptions, including quantitative correlation analysis, social network analysis and qualitative grounded theory analysis. In the whole, our work shows that positive psychological states are fundamental to promote individual and organizational higher-levels of performance and well-being. It also points that the capability to induce positive psychological states in others (an “alter-positive” approach) is a powerful way to develop outstanding individuals and organizations. In a broader sense, it stresses the need to promote good vibrations as a fundamental route to create a better world.

Cohesion decay: quantitative analysis of partial sister chromatid cohesion

Cell division is a highly dynamic process where sister chromatids remain associated with each other from the moment of DNA replication until the later stages of mitosis, giving rise to two daughter cells with equal genomes. The “molecular glue” that links sister DNA molecules is called cohesin, a tripartite ring-like protein complex composed of two Structural Maintenance of Chromosome proteins (Smc1 and Smc3) bridged by a kleisin subunit Rad21/Scc1, that together prevent precocious sister chromatid separation. Accumulating evidence has suggested that cohesion decay may be the cause of segregation errors that underlie certain human pathologies. However it remains to be determined how much cohesin loss abolishes functional sister chromatid cohesion. To answer these questions, we have developed different experimental conditions aiming to titrate the levels of cohesin on mitotic chromosomes in a precise manner. Using these tools, we will determine the minimal amount of cohesin needed to confer functional cohesion. The approaches described here take advantage of a system in Drosophila melanogaster where the Tobacco Etch Virus (TEV) protease can cleave the Rad21 subunit of cohesin leading to precocious sister chromatid separation. Firstly, we tried to express different levels of TEV protease to obtain partial loss of cohesion. However, this approach has failed to produce systematic different levels of sister chromatid separation. Most of the work was therefore focused on a second strategy, for which we established strains with different levels of cohesin sensitive/cohesin resistant to TEV protease. Strains containing different amounts of functional cohesin (TEV resistant) were tested by in vitro cleavage and by in vivo injections in embryos for their ability to promote sister chromatid cohesion. Our results reveal that removal of half of the cohesin complexes does not impair chromosome segregation, implying that chromosome cohesion is less sensitive to cohesin amounts than previously anticipated.

Automated quantitative analysis of MCC-IMS spectra

Ion Mobility Spectrometry coupled with Multi Capillary Columns (MCC -IMS) is a fast analytical technique working at atmospheric pressure with high sensitivity and selectivity making it suitable for the analysis of complex biological matrices. MCC-IMS analysis generates its information through a 3D spectrum with peaks, corresponding to each of the substances detected, providing quantitative and qualitative information. Sometimes peaks of different substances overlap, making the quantification of substances present in the biological matrices a difficult process. In the present work we use peaks of isoprene and acetone as a model for this problem. These two volatile organic compounds (VOCs) that when detected by MCC-IMS produce two overlapping peaks. In this work it’s proposed an algorithm to identify and quantify these two peaks. This algorithm uses image processing techniques to treat the spectra and to detect the position of the peaks, and then fits the data to a custom model in order to separate the peaks. Once the peaks are separated it calculates the contribution of each peak to the data.