Spontaneous cell polarization: Feedback control of Cdc42 GTPase breaks cellular symmetry.

Spontaneous polarization without spatial cues, or symmetry breaking, is a fundamental problem of spatial organization in biological systems. This question has been extensively studied using yeast models, which revealed the central role of the small GTPase switch Cdc42. Active Cdc42-GTP forms a coherent patch at the cell cortex, thought to result from amplification of a small initial stochastic inhomogeneity through positive feedback mechanisms, which induces cell polarization. Here, I review and discuss the mechanisms of Cdc42 activity self-amplification and dynamic turnover. A robust Cdc42 patch is formed through the combined effects of Cdc42 activity promoting its own activation and active Cdc42-GTP displaying reduced membrane detachment and lateral diffusion compared to inactive Cdc42-GDP. I argue the role of the actin cytoskeleton in symmetry breaking is not primarily to transport Cdc42 to the active site. Finally, negative feedback and competition mechanisms serve to control the number of polarization sites.

Feblesa de tutories: manca de feedback i autoregulació en l'estudiant d'infermeria. Valoració de l'avaluació continuada en l'assignatura Ciències Psicosocials Aplicades a la Salut en el Grau d'Infermeria (EEES), (2009-2010)

Resum: En el nou model derivat de l"Espai Europeu d"Educació Superior (EEES), l"ensenyament aprenentatge (E-A) es centra en l"estudiant, enfront del rol més destacat del professor en l"ensenyament tradicional. El procés d"experimentació al que assistim en el nostre medi, requereix la valoració de les primeres experiències. Objectiu: L"objectiu d"aquest estudi és identificar si la metodologia utilitzada en l"assignatura Ciències Psicosocials Aplicades a la Salut, en el Grau d"Infermeria a l"Escola d" infermeria de la Universitat de Barcelona, està ben enfocada per assolir una avaluació continuada de qualitat. Metodologia: Es presenta a traves d"un estudi descriptiu. La població estudiada mitjançant un qüestionari, ha estat els estudiants dels quatre grups de primer curs, del primer any de desenvolupament del Grau d"Infermeria en el nostre Centre, passades el curs acadèmic 2009-2010, amb una mostra total de 221 alumnes. Conclusions: Els resultats permeten tres franges interpretatives: 1) qualificació de notable que fa referència a la modalitat disciplinar i experiència docent del professorat, 2) qualificació entre notable i aprovat referida a la metodologia didàctica i organitzativa, 3) qualificació d"aprovat que fa referència a les tutories.

Feblesa de tutories: manca de feedback i autoregulació en l'estudiant d'infermeria. Valoració de l'avaluació continuada en l'assignatura Ciències Psicosocials Aplicades a la Salut en el Grau d'Infermeria (EEES), (2009-2010)

Resum: En el nou model derivat de l"Espai Europeu d"Educació Superior (EEES), l"ensenyament aprenentatge (E-A) es centra en l"estudiant, enfront del rol més destacat del professor en l"ensenyament tradicional. El procés d"experimentació al que assistim en el nostre medi, requereix la valoració de les primeres experiències. Objectiu: L"objectiu d"aquest estudi és identificar si la metodologia utilitzada en l"assignatura Ciències Psicosocials Aplicades a la Salut, en el Grau d"Infermeria a l"Escola d" infermeria de la Universitat de Barcelona, està ben enfocada per assolir una avaluació continuada de qualitat. Metodologia: Es presenta a traves d"un estudi descriptiu. La població estudiada mitjançant un qüestionari, ha estat els estudiants dels quatre grups de primer curs, del primer any de desenvolupament del Grau d"Infermeria en el nostre Centre, passades el curs acadèmic 2009-2010, amb una mostra total de 221 alumnes. Conclusions: Els resultats permeten tres franges interpretatives: 1) qualificació de notable que fa referència a la modalitat disciplinar i experiència docent del professorat, 2) qualificació entre notable i aprovat referida a la metodologia didàctica i organitzativa, 3) qualificació d"aprovat que fa referència a les tutories.

Expression of the inducible NO synthase in human monocytic U937 cells allows high output nitric oxide production.

Nitric oxide (NO) produced by inducible NO synthase (iNOS, NOS-2) is an important component of the macrophage-mediated immune defense toward numerous pathogens. Murine macrophages produce NO after cytokine activation, whereas, under similar conditions, human macrophages produce low levels or no NO at all. Although human macrophages can express iNOS mRNA and protein on activation, whether they possess the complete machinery necessary for NO synthesis remains controversial. To define the conditions necessary for human monocytes/macrophages to synthesize NO when expressing a functional iNOS, the human monocytic U937 cell line was engineered to synthesize this enzyme, following infection with a retroviral expression vector containing human hepatic iNOS (DFGiNOS). Northern blot and Western blot analysis confirmed the expression of iNOS in transfected U937 cells both at the RNA and protein levels. NOS enzymatic activity was demonstrated in cell lysates by the conversion of L-[3H]arginine into L-[3H]citrulline and the production of NO by intact cells was measured by nitrite and nitrate accumulation in culture supernatants. When expressing functional iNOS, U937 cells were capable of releasing high levels of NO. NO production was strictly dependent on supplementation of the culture medium with tetrahydrobiopterin (BH4) and was not modified by stimulation of the cells with different cytokines. These observations suggest that (1) human monocytic U937 cells contain all the cofactors necessary for NO synthesis, except BH4 and (2) the failure to detect NO in cytokine-stimulated untransfected U937 cells is not due to the presence of a NO-scavenging molecule within these cells nor to the destabilization of iNOS protein. DFGiNOS U937 cells represent a valuable human model to study the role of NO in immunity toward tumors and pathogens.

Feedback i oportunitats de millora: una avaluació orientada a l"aprenentatge actiu

This paper analyzes an innovative experience of formative assessment aimed at improving the teaching of Statistics, which could be easily extrapolated to other studies. We detail the implementation of the double correction, consisting of correcting students' work twice. With the first correction, carried out by classmates according to a rubric developed by the academic, possible errors or deficiencies are discovered, and students are provided with a feedback that allows them to correct and improve their work before being graded by the teacher; whereas in the second correction of the work, once upgraded, the professor evaluates and grades the work. As a result, there is a significant improvement in the quality of students" works, and an active learning from their own mistakes. Both contents and competencies are reinforced by the experience.

Negative feedback regulation of calcineurin-dependent Prz1 transcription factor by the CaMKK-CaMK1 axis in fission yeast

Calcium signals trigger the translocation of the Prz1 transcription factor from the cytoplasm to the nucleus. The process is regulated by the calciumactivated phosphatase calcineurin, which activates Prz1 thereby maintaining active transcription during calcium signalling. When calcium signalling ceases, Prz1 is inactivated by phosphorylation and exported to the cytoplasm. In budding yeast and mammalian cells, different kinases have been reported to counter calcineurin activity and regulate nuclear export. Here, we show that the Ca2+/calmodulin-dependent kinase Cmk1 is first phosphorylated and activated by the newly identified kinase CaMKK2 homologue, Ckk2, in response to Ca2+. Then, active Cmk1 binds, phosphorylates and inactivates Prz1 transcription activity whilst at the same time cmk1 expression is enhanced by Prz1 in response to Ca2+. Furthermore, Cdc25 phosphatase is also phosphorylated by Cmk1, inducing cell cycle arrest in response to an increase in Ca2+. Moreover, cmk1 deletion shows a high tolerance to chronic exposure to Ca2+, due to the lack of cell cycle inhibition and elevated Prz1 activity. This work reveals that Cmk1 kinase activated by the newly identified Ckk2 counteracts calcineurin function by negatively regulating Prz1 activity which in turn is involved in activating cmk1 gene transcription. These results are the first insights into Cmk1 and Ckk2 function in Schizosaccharomyces pombe.

Identifying strategies for mitigating the global warming impact of the EU-25 economy using a multi-objective input-output approach

Global warming mitigation has recently become a priority worldwide. A large body of literature dealing with energy related problems has focused on reducing greenhouse gases emissions at an engineering scale. In contrast, the minimization of climate change at a wider macroeconomic level has so far received much less attention. We investigate here the issue of how to mitigate global warming by performing changes in an economy. To this end, we make use of a systematic tool that combines three methods: linear programming, environmentally extended input output models, and life cycle assessment principles. The problem of identifying key economic sectors that contribute significantly to global warming is posed in mathematical terms as a bi criteria linear program that seeks to optimize simultaneously the total economic output and the total life cycle CO2 emissions. We have applied this approach to the European Union economy, finding that significant reductions in global warming potential can be attained by regulating specific economic sectors. Our tool is intended to aid policymakers in the design of more effective public policies for achieving the environmental and economic targets sought.

Mutual feedback in e-portfolio assessment : an approach to the netfolio system

Peer-reviewed

Learning Preferences with Kernel-Based Methods

Learning of preference relations has recently received significant attention in machine learning community. It is closely related to the classification and regression analysis and can be reduced to these tasks. However, preference learning involves prediction of ordering of the data points rather than prediction of a single numerical value as in case of regression or a class label as in case of classification. Therefore, studying preference relations within a separate framework facilitates not only better theoretical understanding of the problem, but also motivates development of the efficient algorithms for the task. Preference learning has many applications in domains such as information retrieval, bioinformatics, natural language processing, etc. For example, algorithms that learn to rank are frequently used in search engines for ordering documents retrieved by the query. Preference learning methods have been also applied to collaborative filtering problems for predicting individual customer choices from the vast amount of user generated feedback. In this thesis we propose several algorithms for learning preference relations. These algorithms stem from well founded and robust class of regularized least-squares methods and have many attractive computational properties. In order to improve the performance of our methods, we introduce several non-linear kernel functions. Thus, contribution of this thesis is twofold: kernel functions for structured data that are used to take advantage of various non-vectorial data representations and the preference learning algorithms that are suitable for different tasks, namely efficient learning of preference relations, learning with large amount of training data, and semi-supervised preference learning. Proposed kernel-based algorithms and kernels are applied to the parse ranking task in natural language processing, document ranking in information retrieval, and remote homology detection in bioinformatics domain. Training of kernel-based ranking algorithms can be infeasible when the size of the training set is large. This problem is addressed by proposing a preference learning algorithm whose computation complexity scales linearly with the number of training data points. We also introduce sparse approximation of the algorithm that can be efficiently trained with large amount of data. For situations when small amount of labeled data but a large amount of unlabeled data is available, we propose a co-regularized preference learning algorithm. To conclude, the methods presented in this thesis address not only the problem of the efficient training of the algorithms but also fast regularization parameter selection, multiple output prediction, and cross-validation. Furthermore, proposed algorithms lead to notably better performance in many preference learning tasks considered.

Feedback-related Brain Potential Activity Complies with Basic Assumptions of Associative Learning Theory

Feedback-related negativity (FRN) is an ERP component that distinguishes positive from negative feedback. FRN has been hypothesized to be the product of an error signal that may be used to adjust future behavior. In addition, associative learning models assume that the trial-to-trial learning of cueoutcome mappings involves the minimization of an error term. This study evaluated whether FRN is a possible electrophysiological correlate of this error term in a predictive learning task where human subjects were asked to learn different cueoutcome relationships. Specifically, we evaluated the sensitivity of the FRN to the course of learning when different stimuli interact or compete to become a predictor of certain outcomes. Importantly, some of these cues were blocked by more informative or predictive cues (i.e., the blocking effect). Interestingly, the present results show that both learning and blocking affect the amplitude of the FRN component. Furthermore, independent analyses of positive and negative feedback event-related signals showed that the learning effect was restricted to the ERP component elicited by positive feedback. The blocking test showed differences in the FRN magnitude between a predictive and a blocked cue. Overall, the present results show that ERPs that are related to feedback processing correspond to the main predictions of associative learning models. ■

Promoting Programming Learning. Engagement, Automatic Assessment with Immediate Feedback in Visualizations

The skill of programming is a key asset for every computer science student. Many studies have shown that this is a hard skill to learn and the outcomes of programming courses have often been substandard. Thus, a range of methods and tools have been developed to assist students’ learning processes. One of the biggest fields in computer science education is the use of visualizations as a learning aid and many visualization based tools have been developed to aid the learning process during last few decades. Studies conducted in this thesis focus on two different visualizationbased tools TRAKLA2 and ViLLE. This thesis includes results from multiple empirical studies about what kind of effects the introduction and usage of these tools have on students’ opinions and performance, and what kind of implications there are from a teacher’s point of view. The results from studies in this thesis show that students preferred to do web-based exercises, and felt that those exercises contributed to their learning. The usage of the tool motivated students to work harder during their course, which was shown in overall course performance and drop-out statistics. We have also shown that visualization-based tools can be used to enhance the learning process, and one of the key factors is the higher and active level of engagement (see. Engagement Taxonomy by Naps et al., 2002). The automatic grading accompanied with immediate feedback helps students to overcome obstacles during the learning process, and to grasp the key element in the learning task. These kinds of tools can help us to cope with the fact that many programming courses are overcrowded with limited teaching resources. These tools allows us to tackle this problem by utilizing automatic assessment in exercises that are most suitable to be done in the web (like tracing and simulation) since its supports students’ independent learning regardless of time and place. In summary, we can use our course’s resources more efficiently to increase the quality of the learning experience of the students and the teaching experience of the teacher, and even increase performance of the students. There are also methodological results from this thesis which contribute to developing insight into the conduct of empirical evaluations of new tools or techniques. When we evaluate a new tool, especially one accompanied with visualization, we need to give a proper introduction to it and to the graphical notation used by tool. The standard procedure should also include capturing the screen with audio to confirm that the participants of the experiment are doing what they are supposed to do. By taken such measures in the study of the learning impact of visualization support for learning, we can avoid drawing false conclusion from our experiments. As computer science educators, we face two important challenges. Firstly, we need to start to deliver the message in our own institution and all over the world about the new – scientifically proven – innovations in teaching like TRAKLA2 and ViLLE. Secondly, we have the relevant experience of conducting teaching related experiment, and thus we can support our colleagues to learn essential know-how of the research based improvement of their teaching. This change can transform academic teaching into publications and by utilizing this approach we can significantly increase the adoption of the new tools and techniques, and overall increase the knowledge of best-practices. In future, we need to combine our forces and tackle these universal and common problems together by creating multi-national and multiinstitutional research projects. We need to create a community and a platform in which we can share these best practices and at the same time conduct multi-national research projects easily.

Towards input/output-free modelling of linear infinite-dimensional systems in continuous time

This dissertation describes a networking approach to infinite-dimensional systems theory, where there is a minimal distinction between inputs and outputs. We introduce and study two closely related classes of systems, namely the state/signal systems and the port-Hamiltonian systems, and describe how they relate to each other. Some basic theory for these two classes of systems and the interconnections of such systems is provided. The main emphasis lies on passive and conservative systems, and the theoretical concepts are illustrated using the example of a lossless transfer line. Much remains to be done in this field and we point to some directions for future studies as well.

Feedback como estratégia de aprendizado no ensino médico

Fornecer feedback aos alunos é um importante aspecto da aprendizagem e um papel essencial dos docentes. No contexto da educação médica clínica, feedback se refere às informações que descrevem o desempenho dos alunos em determinada situação ou atividade. A habilidade de dar e receber feedback melhora os resultados da aprendizagem, uma vez que fornece a base para a aprendizagem autodirecionada e para a reflexão crítica, auxilia os alunos a corrigirem seus erros, reforça comportamentos desejáveis e mostra como o aluno pode melhorar. Apesar da evidente falta de feedback durante o curso médico, os alunos desejam e valorizam essa ferramenta construtiva, considerando-a um aspecto importante do ensino de qualidade. O feedback eficaz deve ser: assertivo, respeitoso, descritivo, oportuno e específico. Docentes e alunos deveriam ser preparados para dar e receber feedback. Coordenadores e diretores deveriam reconhecer o papel do feedback como uma importante estratégia de ensino- aprendizagem na graduação.