Integrated Disaster Reduction Approach: LforS Learning and Simulation Game

Main objective of the game is to increase the coping capacity of players and familiarise them with the Integrated Disaster Reduction Approach. The game is intended to prepare for and introduce the players to a subsequent Learning for Sustainability capacity building workshop for community leaders. The game represents a typical emergency situation resulting from a natural disaster. Before and after the event, adequate measures help to prevent or minimise potential damages. Once a disaster has occurred, concerted actions and immediate measures need to be taken to rescue as much as possible (human lives, livestock, material) and safeguard the village against further damage and losses. In the course of the game, each playing team can proof its knowledge on adequate measures that have to be taken in order to avoid or reduce losses related to natural disasters. Such measures relate to assessment and monitoring of risks, prevention and mitigation measures, preparedness and response as well as recovery and reconstruction.

Connectionism as metaphor: Towards an integrated, unified conception of self-system and individual difference

Comments on an article by Kashima et al. (see record 2007-10111-001). In their target article Kashima and colleagues try to show how a connectionist model conceptualization of the self is best suited to capture the self's temporal and socio-culturally contextualized nature. They propose a new model and to support this model, the authors conduct computer simulations of psychological phenomena whose importance for the self has long been clear, even if not formally modeled, such as imitation, and learning of sequence and narrative. As explicated when we advocated connectionist models as a metaphor for self in Mischel and Morf (2003), we fully endorse the utility of such a metaphor, as these models have some of the processing characteristics necessary for capturing key aspects and functions of a dynamic cognitive-affective self-system. As elaborated in that chapter, we see as their principal strength that connectionist models can take account of multiple simultaneous processes without invoking a single central control. All outputs reflect a distributed pattern of activation across a large number of simple processing units, the nature of which depends on (and changes with) the connection weights between the links and the satisfaction of mutual constraints across these links (Rummelhart & McClelland, 1986). This allows a simple account for why certain input features will at times predominate, while others take over on other occasions. (PsycINFO Database Record (c) 2008 APA, all rights reserved)

Implicit task sequence learning

Implicit task sequence learning (TSL) can be considered as an extension of implicit sequence learning which is typically tested with the classical serial reaction time task (SRTT). By design, in the SRTT there is a correlation between the sequence of stimuli to which participants must attend and the sequence of motor movements/key presses with which participants must respond. The TSL paradigm allows to disentangle this correlation and to separately manipulate the presences/absence of a sequence of tasks, a sequence of responses, and even other streams of information such as stimulus locations or stimulus-response mappings. Here I review the state of TSL research which seems to point at the critical role of the presence of correlated streams of information in implicit sequence learning. On a more general level, I propose that beyond correlated streams of information, a simple statistical learning mechanism may also be involved in implicit sequence learning, and that the relative contribution of these two explanations differ according to task requirements. With this differentiation, conflicting results can be integrated into a coherent framework.

Tumour genomic and microenvironmental heterogeneity for integrated prediction of 5-year biochemical recurrence of prostate cancer: a retrospective cohort study.

BACKGROUND Clinical prognostic groupings for localised prostate cancers are imprecise, with 30-50% of patients recurring after image-guided radiotherapy or radical prostatectomy. We aimed to test combined genomic and microenvironmental indices in prostate cancer to improve risk stratification and complement clinical prognostic factors. METHODS We used DNA-based indices alone or in combination with intra-prostatic hypoxia measurements to develop four prognostic indices in 126 low-risk to intermediate-risk patients (Toronto cohort) who will receive image-guided radiotherapy. We validated these indices in two independent cohorts of 154 (Memorial Sloan Kettering Cancer Center cohort [MSKCC] cohort) and 117 (Cambridge cohort) radical prostatectomy specimens from low-risk to high-risk patients. We applied unsupervised and supervised machine learning techniques to the copy-number profiles of 126 pre-image-guided radiotherapy diagnostic biopsies to develop prognostic signatures. Our primary endpoint was the development of a set of prognostic measures capable of stratifying patients for risk of biochemical relapse 5 years after primary treatment. FINDINGS Biochemical relapse was associated with indices of tumour hypoxia, genomic instability, and genomic subtypes based on multivariate analyses. We identified four genomic subtypes for prostate cancer, which had different 5-year biochemical relapse-free survival. Genomic instability is prognostic for relapse in both image-guided radiotherapy (multivariate analysis hazard ratio [HR] 4·5 [95% CI 2·1-9·8]; p=0·00013; area under the receiver operator curve [AUC] 0·70 [95% CI 0·65-0·76]) and radical prostatectomy (4·0 [1·6-9·7]; p=0·0024; AUC 0·57 [0·52-0·61]) patients with prostate cancer, and its effect is magnified by intratumoral hypoxia (3·8 [1·2-12]; p=0·019; AUC 0·67 [0·61-0·73]). A novel 100-loci DNA signature accurately classified treatment outcome in the MSKCC low-risk to intermediate-risk cohort (multivariate analysis HR 6·1 [95% CI 2·0-19]; p=0·0015; AUC 0·74 [95% CI 0·65-0·83]). In the independent MSKCC and Cambridge cohorts, this signature identified low-risk to high-risk patients who were most likely to fail treatment within 18 months (combined cohorts multivariate analysis HR 2·9 [95% CI 1·4-6·0]; p=0·0039; AUC 0·68 [95% CI 0·63-0·73]), and was better at predicting biochemical relapse than 23 previously published RNA signatures. INTERPRETATION This is the first study of cancer outcome to integrate DNA-based and microenvironment-based failure indices to predict patient outcome. Patients exhibiting these aggressive features after biopsy should be entered into treatment intensification trials. FUNDING Movember Foundation, Prostate Cancer Canada, Ontario Institute for Cancer Research, Canadian Institute for Health Research, NIHR Cambridge Biomedical Research Centre, The University of Cambridge, Cancer Research UK, Cambridge Cancer Charity, Prostate Cancer UK, Hutchison Whampoa Limited, Terry Fox Research Institute, Princess Margaret Cancer Centre Foundation, PMH-Radiation Medicine Program Academic Enrichment Fund, Motorcycle Ride for Dad (Durham), Canadian Cancer Society.

Linking Applied Ethnobotany to Social Learning: A Participatory Tool for the Promotion of Indigenous Plants Use in Matutuíne, Southern Mozambique

In its search for pathways towards a more sustainable management of natural resources, development oriented research increasingly faces the challenge to develop new concepts and tools based on transdisciplinarity. Transdisciplinarity can, in terms of an idealized goal, be defined as a research approach that identifies and solves problems not only independently of disciplinary boundaries, but also including the knowledge and perceptions of non-scientific actors in a participatory process. In Mozambique, the Centre for Development and Environment (Berne, Switzerland), in partnership with Impacto and Helvetas (Maputo, Mozambique), has elaborated a new transdisciplinary tool to identify indigenous plants with a potential for commercialization. The tool combines methods from applied ethnobotany with participatory research in a social learning process. This approach was devised to support a development project aimed at creating alternative sources of income for rural communities of Matutuíne district, Southern Mozambique, while reducing the pressure on the natural environment. The methodology, which has been applied and tested, is innovative in that it combines important data collection through participatory research with a social learning process involving both local and external actors. This mutual learning process provides a space for complementary forms of knowledge to meet, eventually leading to the adoption of an integrated approach to natural resource management with an understanding of its ecological, socio-economic and cultural aspects; local stakeholders are included in the identification of potentials for sustainable development. Sustainable development itself, as a normative concept, can only be defined through social learning and consensus building between the local and external stakeholders.

Multimodal sequence learning

While sequence learning research models complex phenomena, previous studies have mostly focused on unimodal sequences. The goal of the current experiment is to put implicit sequence learning into a multimodal context: to test whether it can operate across different modalities. We used the Task Sequence Learning paradigm to test whether sequence learning varies across modalities, and whether participants are able to learn multimodal sequences. Our results show that implicit sequence learning is very similar regardless of the source modality. However, the presence of correlated task and response sequences was required for learning to take place. The experiment provides new evidence for implicit sequence learning of abstract conceptual representations. In general, the results suggest that correlated sequences are necessary for implicit sequence learning to occur. Moreover, they show that elements from different modalities can be automatically integrated into one unitary multimodal sequence.