Biotic carbon feedbacks in a materially-closed soil-vegetation-atmosphere system

The magnitude and direction of the coupled feedbacks between the biotic and abiotic components of the terrestrial carbon cycle is a major source of uncertainty in coupled climate–carbon-cycle models1, 2, 3. Materially closed, energetically open biological systems continuously and simultaneously allow the two-way feedback loop between the biotic and abiotic components to take place4, 5, 6, 7, but so far have not been used to their full potential in ecological research, owing to the challenge of achieving sustainable model systems6, 7. We show that using materially closed soil–vegetation–atmosphere systems with pro rata carbon amounts for the main terrestrial carbon pools enables the establishment of conditions that balance plant carbon assimilation, and autotrophic and heterotrophic respiration fluxes over periods suitable to investigate short-term biotic carbon feedbacks. Using this approach, we tested an alternative way of assessing the impact of increased CO2 and temperature on biotic carbon feedbacks. The results show that without nutrient and water limitations, the short-term biotic responses could potentially buffer a temperature increase of 2.3 °C without significant positive feedbacks to atmospheric CO2. We argue that such closed-system research represents an important test-bed platform for model validation and parameterization of plant and soil biotic responses to environmental changes

The effects of dissection-room experiences and related coping strategies among Hungarian medical students

Background: Students get their first experiences of dissecting human cadavers in the practical classes of anatomy and pathology courses, core components of medical education. These experiences form an important part of the process of becoming a doctor, but bring with them a special set of problems. Methods: Quantitative, national survey (n = 733) among medical students, measured reactions to dissection experiences and used a new measuring instrument to determine the possible factors of coping. Results: Fifty per cent of students stated that the dissection experience does not affect them . Negative effects were significantly more frequently reported by women and students in clinical training (years 3,4,5,6). The predominant factor in the various coping strategies for dissection practicals is cognitive coping (rationalisation, intellectualisation). Physical and emotional coping strategies followed, with similar mean scores. Marked gender differences also showed up in the application of coping strategies: there was a clear dominance of emotional-based coping among women. Among female students, there was a characteristic decrease in the physical repulsion factor in reactions to dissection in the later stages of study. Conclusions: The experience of dissection had an emotional impact on about half of the students. In general, students considered these experiences to be an important part of becoming a doctor. Our study found that students chiefly employed cognitive coping strategies to deal with their experiences. Dissection-room sessions are important for learning emotional as well as technical skills. Successful coping is achieved not by repressing emotions but by accepting and understanding the negative emotions caused by the experience and developing effective strategies to deal with them. Medical training could make better use of the learning potential of these experiences

Foreign exchange trading using a learning classifier system

We apply a simple Learning Classifier System to a foreign exchange trading problem. The performance of the Learning Classifier System is compared to that of a Genetic Programming approach from the literature. The simple Learning Classifier System is able to achieve a positive excess return in simulated trading, but results are not yet fully competitive because the Learning Classifier System trades too frequently. However, the Learning Classifier System approach shows potential because returns are obtained with no offline training and the technique is inherently adaptive, unlike many of the machine learning methods currently employed for financial trading. © 2008 Springer-Verlag Berlin Heidelberg