Efficient digital self-calibration of video-rate pipeline ADCs using white gaussian noise

Proceedings of IEEE, ISCAS 2003, Vol.I, pp. 877-880

Natural and amyloid self-assembly of S100 proteins: structural basis of functional diversity

The S100 proteins are 10-12 kDa EF-hand proteins that act as central regulators in a multitude of cellular processes including cell survival, proliferation, differentiation and motility. Consequently, many S100 proteins are implicated and display marked changes in their expression levels in many types of cancer, neurodegenerative disorders, inflammatory and autoimmune diseases. The structure and function of S100 proteins are modulated by metal ions via Ca2+ binding through EF-hand motifs and binding of Zn2+ and Cu2+ at additional sites, usually at the homodimer interfaces. Ca2+ binding modulates S100 conformational opening and thus promotes and affects the interaction with p53, the receptor for advanced glycation endproducts and Toll-like receptor 4, among many others. Structural plasticity also occurs at the quaternary level, where several S100 proteins self-assemble into multiple oligomeric states, many being functionally relevant. Recently, we have found that the S100A8/A9 proteins are involved in amyloidogenic processes in corpora amylacea of prostate cancer patients, and undergo metal-mediated amyloid oligomerization and fibrillation in vitro. Here we review the unique chemical and structural properties of S100 proteins that underlie the conformational changes resulting in their oligomerization upon metal ion binding and ultimately in functional control. The possibility that S100 proteins have intrinsic amyloid-forming capacity is also addressed, as well as the hypothesis that amyloid self-assemblies may, under particular physiological conditions, affect the S100 functions within the cellular milieu.

Self-assessment of writing skills: a reliable and valid tool in an efl classroom

Trabalho de projecto apresentado para cumprimento dos requisitos necessários à obtenção do grau de Mestre em Ensino da Língua Inglesa

Diversity in Teams: Source of Conflict or Source of Creativity? Investigating Hidden Mental Models

The workforce in organizations today is becoming increasingly diverse. Consequently the role of diversity management is heavily discussed with respect to the question how diversity influences the productivity of a group. Empirical studies show that on one hand there is a potential for increasing productivity but on the other hand it might be as well that conflicts arise due to the heterogeneity of the group. Usually according empirical studies are based on interviews, questionnaires and/or observations. These methods imply that answers are highly selective and filtered. In order to make the invisible visible, to have access to mental models of team members the paper will present an empirical study on the self-understanding of groups based on an innovative research method, called “mind-scripting”.

Automatic learning for the classification of chemical reactions and in statistical thermodynamics

This Thesis describes the application of automatic learning methods for a) the classification of organic and metabolic reactions, and b) the mapping of Potential Energy Surfaces(PES). The classification of reactions was approached with two distinct methodologies: a representation of chemical reactions based on NMR data, and a representation of chemical reactions from the reaction equation based on the physico-chemical and topological features of chemical bonds. NMR-based classification of photochemical and enzymatic reactions. Photochemical and metabolic reactions were classified by Kohonen Self-Organizing Maps (Kohonen SOMs) and Random Forests (RFs) taking as input the difference between the 1H NMR spectra of the products and the reactants. The development of such a representation can be applied in automatic analysis of changes in the 1H NMR spectrum of a mixture and their interpretation in terms of the chemical reactions taking place. Examples of possible applications are the monitoring of reaction processes, evaluation of the stability of chemicals, or even the interpretation of metabonomic data. A Kohonen SOM trained with a data set of metabolic reactions catalysed by transferases was able to correctly classify 75% of an independent test set in terms of the EC number subclass. Random Forests improved the correct predictions to 79%. With photochemical reactions classified into 7 groups, an independent test set was classified with 86-93% accuracy. The data set of photochemical reactions was also used to simulate mixtures with two reactions occurring simultaneously. Kohonen SOMs and Feed-Forward Neural Networks (FFNNs) were trained to classify the reactions occurring in a mixture based on the 1H NMR spectra of the products and reactants. Kohonen SOMs allowed the correct assignment of 53-63% of the mixtures (in a test set). Counter-Propagation Neural Networks (CPNNs) gave origin to similar results. The use of supervised learning techniques allowed an improvement in the results. They were improved to 77% of correct assignments when an ensemble of ten FFNNs were used and to 80% when Random Forests were used. This study was performed with NMR data simulated from the molecular structure by the SPINUS program. In the design of one test set, simulated data was combined with experimental data. The results support the proposal of linking databases of chemical reactions to experimental or simulated NMR data for automatic classification of reactions and mixtures of reactions. Genome-scale classification of enzymatic reactions from their reaction equation. The MOLMAP descriptor relies on a Kohonen SOM that defines types of bonds on the basis of their physico-chemical and topological properties. The MOLMAP descriptor of a molecule represents the types of bonds available in that molecule. The MOLMAP descriptor of a reaction is defined as the difference between the MOLMAPs of the products and the reactants, and numerically encodes the pattern of bonds that are broken, changed, and made during a chemical reaction. The automatic perception of chemical similarities between metabolic reactions is required for a variety of applications ranging from the computer validation of classification systems, genome-scale reconstruction (or comparison) of metabolic pathways, to the classification of enzymatic mechanisms. Catalytic functions of proteins are generally described by the EC numbers that are simultaneously employed as identifiers of reactions, enzymes, and enzyme genes, thus linking metabolic and genomic information. Different methods should be available to automatically compare metabolic reactions and for the automatic assignment of EC numbers to reactions still not officially classified. In this study, the genome-scale data set of enzymatic reactions available in the KEGG database was encoded by the MOLMAP descriptors, and was submitted to Kohonen SOMs to compare the resulting map with the official EC number classification, to explore the possibility of predicting EC numbers from the reaction equation, and to assess the internal consistency of the EC classification at the class level. A general agreement with the EC classification was observed, i.e. a relationship between the similarity of MOLMAPs and the similarity of EC numbers. At the same time, MOLMAPs were able to discriminate between EC sub-subclasses. EC numbers could be assigned at the class, subclass, and sub-subclass levels with accuracies up to 92%, 80%, and 70% for independent test sets. The correspondence between chemical similarity of metabolic reactions and their MOLMAP descriptors was applied to the identification of a number of reactions mapped into the same neuron but belonging to different EC classes, which demonstrated the ability of the MOLMAP/SOM approach to verify the internal consistency of classifications in databases of metabolic reactions. RFs were also used to assign the four levels of the EC hierarchy from the reaction equation. EC numbers were correctly assigned in 95%, 90%, 85% and 86% of the cases (for independent test sets) at the class, subclass, sub-subclass and full EC number level,respectively. Experiments for the classification of reactions from the main reactants and products were performed with RFs - EC numbers were assigned at the class, subclass and sub-subclass level with accuracies of 78%, 74% and 63%, respectively. In the course of the experiments with metabolic reactions we suggested that the MOLMAP / SOM concept could be extended to the representation of other levels of metabolic information such as metabolic pathways. Following the MOLMAP idea, the pattern of neurons activated by the reactions of a metabolic pathway is a representation of the reactions involved in that pathway - a descriptor of the metabolic pathway. This reasoning enabled the comparison of different pathways, the automatic classification of pathways, and a classification of organisms based on their biochemical machinery. The three levels of classification (from bonds to metabolic pathways) allowed to map and perceive chemical similarities between metabolic pathways even for pathways of different types of metabolism and pathways that do not share similarities in terms of EC numbers. Mapping of PES by neural networks (NNs). In a first series of experiments, ensembles of Feed-Forward NNs (EnsFFNNs) and Associative Neural Networks (ASNNs) were trained to reproduce PES represented by the Lennard-Jones (LJ) analytical potential function. The accuracy of the method was assessed by comparing the results of molecular dynamics simulations (thermal, structural, and dynamic properties) obtained from the NNs-PES and from the LJ function. The results indicated that for LJ-type potentials, NNs can be trained to generate accurate PES to be used in molecular simulations. EnsFFNNs and ASNNs gave better results than single FFNNs. A remarkable ability of the NNs models to interpolate between distant curves and accurately reproduce potentials to be used in molecular simulations is shown. The purpose of the first study was to systematically analyse the accuracy of different NNs. Our main motivation, however, is reflected in the next study: the mapping of multidimensional PES by NNs to simulate, by Molecular Dynamics or Monte Carlo, the adsorption and self-assembly of solvated organic molecules on noble-metal electrodes. Indeed, for such complex and heterogeneous systems the development of suitable analytical functions that fit quantum mechanical interaction energies is a non-trivial or even impossible task. The data consisted of energy values, from Density Functional Theory (DFT) calculations, at different distances, for several molecular orientations and three electrode adsorption sites. The results indicate that NNs require a data set large enough to cover well the diversity of possible interaction sites, distances, and orientations. NNs trained with such data sets can perform equally well or even better than analytical functions. Therefore, they can be used in molecular simulations, particularly for the ethanol/Au (111) interface which is the case studied in the present Thesis. Once properly trained, the networks are able to produce, as output, any required number of energy points for accurate interpolations.

Positive self-image over time

This paper incorporates egocentric comparisons into a human capital accumulation model and studies the evolution of positive self image over time. The paper shows that the process of human capital accumulation together with egocentric comparisons imply that positive self image of a cohort is first increasing and then decreasing over time. Additionally, the paper finds that positive self image: (1) peaks earlier in activities where skill depreciation is higher, (2) is smaller in activities where the distribution of income is more dispersed, (3) is not a stable characteristic of an individual, and (4) is higher for more patient individuals.

From Consciousness to Knowledge – The Explanatory Power of Revelation

Epistemology in philosophy of mind is a difficult endeavor. Those who believe that our phenomenal life is different from other domains suggest that self-knowledge about phenomenal properties is certain and therefore privileged. Usually, this so called privileged access is explained by the idea that we have direct access to our phenomenal life. This means, in contrast to perceptual knowledge, self-knowledge is non-inferential. It is widely believed that, this kind of directness involves two different senses: an epistemic sense and a metaphysical sense. Proponents of this view often claim that this is due to the fact that we are acquainted with our current experiences. The acquaintance thesis, therefore, is the backbone in justifying privileged access. Unfortunately the whole approach has a profound flaw. For the thesis to work, acquaintance has to be a genuine explanation. Since it is usually assumed that any knowledge relation between judgments and the corresponding objects are merely causal and contingent (e.g. in perception), the proponent of the privileged access view needs to show that acquaintance can do the job. In this thesis, however, I claim that the latter cannot be done. Based on considerations introduced by Levine, I conclude that this approach involves either the introduction of ontologically independent properties or a rather obscure knowledge relation. A proper explanation, however, cannot employ either of the two options. The acquaintance thesis is, therefore, bound to fail. Since the privileged access intuition seems to be vital to epistemology within the philosophy of mind, I will explore alternative justifications. After discussing a number of options, I will focus on the so called revelation thesis. This approach states that by simply having an experience with phenomenal properties, one is in the position to know the essence of those phenomenal properties. I will argue that, after finding a solution for the controversial essence claim, this thesis is a successful replacement explanation which maintains all the virtues of the acquaintance account without necessarily introducing ontologically independent properties or an obscure knowledge relation. The overall solution consists in qualifying the essence claim in the relevant sense, leaving us with an appropriate ontology for phenomenal properties. On the one hand, this avoids employing mysterious independent properties, since this ontological view is physicalist in nature. On the other hand, this approach has the right kind of structure to explain privileged self-knowledge of our phenomenal life. My final conclusion consists in the claim that the privileged access intuition is in fact veridical. It cannot, however, be justified by the popular acquaintance approach, but rather, is explainable by the controversial revelation thesis.

Productivity gains from migration: An analysis of Cape Verdean return migrants

Are return migrants more productive than non-migrants? If so, is it a causal effect or simply self-selection? Existing literature has not reached a consensus on the role of return migration for origin countries. To answer these research questions, an empirical analysis was performed based on household data collected in Cape Verde. One of the most common identification problems in the migration literature is the presence of migrant self-selection. In order to disentangle potential selection bias, we use instrumental variable estimation using variation provided by unemployment rates in migrant destination countries, which is compared with OLS and Nearest Neighbor Matching (NNM) methods. The results using the instrumental variable approach provide evidence of labour income gains due to return migration, while OLS underestimates the coefficient of interest. This bias points towards negative self-selection of return migrants on unobserved characteristics, although the different estimates cannot be distinguished statistically. Interestingly, migration duration and occupational changes after migration do not seem to influence post-migration income. There is weak evidence that return migrants from the United States have higher income gains caused by migration than the ones who returned from Portugal.