Mapping NGO Competency to Reduce Human Vulnerability in Post-disaster Communities: Comparing Strategies in Sri Lanka and Bangladesh

Natural hazards trigger disasters, the scale of which is largely determined by vulnerability. Developing countries suffer the most from disasters due to various conditions of vulnerability which exist and there is an opportunity after disasters to take mitigative action. NGOs implementing post-disaster rehabilitation projects must be able to address the issues causing communities to live at risk of disaster and therefore must build dynamic capacity, capabilities and competencies, enabling them to operate in unstable environments. This research is built upon a theoretical framework of dynamic competency established by combining elements of disaster management, strategic management and project management theory. A number of NGOs which have implemented reconstruction and rehabilitation projects both in Sri Lanka following the Asian Tsunami and Bangladesh following Cyclone Sidr are being investigated in great depth using a causal mapping procedure. ‘Event’ specific maps have been developed for each organization in each disaster. This data will be analysed with a view to discovering the strategies which lead to vulnerability reduction in post-disaster communities and the competencies that NGOs must possess in order to achieve favourable outcomes. It is hypothesized that by building organizational capacity, capabilities and competencies to be dynamic in nature, while focusing on a more emergent strategic approach, with emphasis on adaptive capability and innovation, NGOs will be better equipped to contribute to sustainable community development through reconstruction. We believe that through this study it will be possible to glean a new understanding of social processes that emerge within community rehabilitation projects.

Novel breast cancer biomarkers identified by integrative proteomic and gene expression mapping

Proteomic and transcriptomic platforms both play important roles in cancer research, with differing strengths and limitations. Here, we describe a proteo-transcriptomic integrative strategy for discovering novel cancer biomarkers, combining the direct visualization of differentially expressed proteins with the high-throughput scale of gene expression profiling. Using breast cancer as a case example, we generated comprehensive two-dimensional electrophoresis (2DE)/mass spectrometry (MS) proteomic maps of cancer (MCF-7 and HCC-38) and control (CCD-1059Sk) cell lines, identifying 1724 expressed protein spots representing 484 different protein species. The differentially expressed cell-line proteins were then mapped to mRNA transcript databases of cancer cell lines and primary breast tumors to identify candidate biomarkers that were concordantly expressed at the gene expression level. Of the top nine selected biomarker candidates, we reidentified ANX1, a protein previously reported to be differentially expressed in breast cancers and normal tissues, and validated three other novel candidates, CRAB, 6PGL, and CAZ2, as differentially expressed proteins by immunohistochemistry on breast tissue microarrays. In total, close to half (4/9) of our protein biomarker candidates were successfully validated. Our study thus illustrates how the systematic integration of proteomic and transcriptomic data from both cell line and primary tissue samples can prove advantageous for accelerating cancer biomarker discovery.

Business representation and the UK regions: mapping institutional change

Political devolution in Scotland, Wales and Northern Ireland and the developing regional agenda in England are prompting changes in the organization of business interest representation within the devolved and decentralized territories. In this paper we seek to describe the realignment of business interest representation at the 'regional' scale, first through a detailed review of changes underway across specific business associations and representative fora, and secondly through an initial attempt to compare and 'map' the patterns of institutional change recorded in the various territories. In broad terms the overall scale, operation and degree of formalization of the new political arrangements for business representation tend broadly to reflect the established institutional and political contexts of the respective nations and regions and the level of devolution ceded to the territories. However, there are important variations in a complex process of uneven development. In the concluding section we present some initial thoughts on the nature of the changes observed in the institutional framework for business representation. A key argument is that to date such changes suggest a reconfiguration of business political activity rather than a step-change in the institutional foundation for sub-national business interest representation in the UK. (C) 2003 Elsevier Ltd. All rights reserved.

Mapping biological to clinical phenotypes during the development (21 days) and resolution (21 days) of experimental gingivitis

Aim: To characterize and map temporal changes in the biological and clinical phenotype during a 21-day experimental gingivitis study. Materials and Methods: Experimental gingivitis was induced over 21 days in healthy human volunteers (n = 56), after which normal brushing was resumed (resolution phase). Gingival and plaque indices were assessed. Gingival crevicular fluid was collected from four paired test and contra-lateral control sites in each volunteer during induction (Days 0, 7, 14 and 21) and resolution (Days 28 and 42) of experimental gingivitis. Fluid volumes were measured and a single analyte was quantified from each site-specific, 30s sample. Data were evaluated by analysis of repeated measurements and paired sample tests. Results: Clinical indices and gingival crevicular fluid volumes at test sites increased from Day 0, peaking at Day 21 (test/control differences all p

Increasing the accuracy of Nitrogen Dioxide (NO2) pollution mapping using geographically weighted regression (GWR) and geostatistics.

Nitrogen Dioxide (NO2) is known to act as an environmental trigger for many respiratory illnesses. As a pollutant it is difficult to map accurately, as concentrations can vary greatly over small distances. In this study three geostatistical techniques were compared, producing maps of NO2 concentrations in the United Kingdom (UK). The primary data source for each technique was NO2 point data, generated from background automatic monitoring and background diffusion tubes, which are analysed by different laboratories on behalf of local councils and authorities in the UK. The techniques used were simple kriging (SK), ordinary kriging (OK) and simple kriging with a locally varying mean (SKlm). SK and OK make use of the primary variable only. SKlm differs in that it utilises additional data to inform prediction, and hence potentially reduces uncertainty. The secondary data source was Oxides of Nitrogen (NOx) derived from dispersion modelling outputs, at 1km x 1km resolution for the UK. These data were used to define the locally varying mean in SKlm, using two regression approaches: (i) global regression (GR) and (ii) geographically weighted regression (GWR). Based upon summary statistics and cross-validation prediction errors, SKlm using GWR derived local means produced the most accurate predictions. Therefore, using GWR to inform SKlm was beneficial in this study.