Effect of lipopolysaccharide from periodontal pathogens on the production of tissue plasminogen activator and plasminogen activator inhibitor 2 by human gingival fibroblasts

Both tissue plasminogen activator (t-PA) and plasminogen activator inhibitor 2 (PAI-2) are important proteolysis factors present in inflamed human periodontal tissues. The aim of the present study was to investigate the effect of lipopolysaccharide (LPS) on the synthesis: of t-PA and PAI-2 by human gingival fibroblasts (HGF). LPS from different periodontal pathogens including Actinobacillus actinomycetemcomitans, Porphyromonas gingivalis and Fusobacterium nucleatum were extracted by the hot phenol water method. The levels of t-PA and PAI-2 secreted into the cell culture media were measured by enzyme-linked immunosorbent assays (ELISA). The mRNA for t-PA and PAI-2 were measured by RT-PCR. The results showed t-PA synthesis was increased in response to all types of LPS studied and PAI-2 level was increased by LPS from A. actinomycetemcomitans and F. nucleatum, but not P. gingivalis. When comparing the effects of LPS from non-periodontal bacteria (Escherichia coli and Salmonella enteritidis) with the LPS from periodontal pathogens, we found that the ratio of t-PA to PAI-2 was greater following exposure of the cells to LPS from periodontal pathogens. The highest ratio of t-PA to PAI-2 was found in those cells exposed to LPS from P. gingivalis. These results indicate that LPS derived from periodontal pathogens may cause unbalanced regulation of plasminogen activator and plasminogen activator inhibitor by HGF and such an effect may, in part, contribute to the destruction of periodontal connective tissue through dysregulated pericellular proteolysis.

Increasing profits and reducing risks in crop production using participatory systems simulation approaches

The development of cropping systems simulation capabilities world-wide combined with easy access to powerful computing has resulted in a plethora of agricultural models and consequently, model applications. Nonetheless, the scientific credibility of such applications and their relevance to farming practice is still being questioned. Our objective in this paper is to highlight some of the model applications from which benefits for farmers were or could be obtained via changed agricultural practice or policy. Changed on-farm practice due to the direct contribution of modelling, while keenly sought after, may in some cases be less achievable than a contribution via agricultural policies. This paper is intended to give some guidance for future model applications. It is not a comprehensive review of model applications, nor is it intended to discuss modelling in the context of social science or extension policy. Rather, we take snapshots around the globe to 'take stock' and to demonstrate that well-defined financial and environmental benefits can be obtained on-farm from the use of models. We highlight the importance of 'relevance' and hence the importance of true partnerships between all stakeholders (farmer, scientists, advisers) for the successful development and adoption of simulation approaches. Specifically, we address some key points that are essential for successful model applications such as: (1) issues to be addressed must be neither trivial nor obvious; (2) a modelling approach must reduce complexity rather than proliferate choices in order to aid the decision-making process (3) the cropping systems must be sufficiently flexible to allow management interventions based on insights gained from models. The pro and cons of normative approaches (e.g. decision support software that can reach a wide audience quickly but are often poorly contextualized for any individual client) versus model applications within the context of an individual client's situation will also be discussed. We suggest that a tandem approach is necessary whereby the latter is used in the early stages of model application for confidence building amongst client groups. This paper focuses on five specific regions that differ fundamentally in terms of environment and socio-economic structure and hence in their requirements for successful model applications. Specifically, we will give examples from Australia and South America (high climatic variability, large areas, low input, technologically advanced); Africa (high climatic variability, small areas, low input, subsistence agriculture); India (high climatic variability, small areas, medium level inputs, technologically progressing; and Europe (relatively low climatic variability, small areas, high input, technologically advanced). The contrast between Australia and Europe will further demonstrate how successful model applications are strongly influenced by the policy framework within which producers operate. We suggest that this might eventually lead to better adoption of fully integrated systems approaches and result in the development of resilient farming systems that are in tune with current climatic conditions and are adaptable to biophysical and socioeconomic variability and change. (C) 2001 Elsevier Science Ltd. All rights reserved.

Quantifying stresses and support requirements in the undercut and production level drifts of block and panel caving mines

A numerical modelling strategy has been developed in order to quantify the magnitude of induced stresses at the boundaries of production level and undercut level drifts for various in situ stress environments and undercut scenarios. The results of the stress modelling were in line with qualitative experiential guidelines and a limited number of induced stress measurements documented from caving sites. A number of stress charts were developed which quantify the maximum boundary stresses in drift roofs for varying in situ stress regimes, depths and undercut scenarios. This enabled many of the experiential guidelines to be quantified and bounded. A limited number of case histories of support and support performance in cave mine drifts were compared to support recommendations using the NGI classification system, The stress charts were used to estimate the Stress Reduction Factor for this system. The back-analyses suggested that the NGI classification system might be able to give preliminary estimates of support requirements in caving mines with modifications relating to rock bolt length and the support of production level intersections. (C) 2002 Elsevier Science Ltd. All rights reserved.

Nutrient dynamics in Queensland savannas: Implications for the sustainability of land clearing for pasture production

Eucalyptus savannas on low nutrient soils are being extensively cleared in Queensland. In this paper we provide background information relevant to understanding nutrient (particularly nitrogen) dynamics in sub/tropical savanna, and review the available evidence relevant to understanding the potential impact of clearing Eucalyptus savanna on nutrient relations. The limited evidence presently available can be used to argue for the extreme positions that: (i) woody vegetation competes with grasses Cor resources. and tree/shrub clearing improves pasture production, (ii) woody vegetation benefits pasture production. At present, the lack of fundamental knowledge about Australian savanna nutrient relations makes accurate predictions about medium- and long-term effects of clearing on nutrient relations in low nutrient savannas difficult. The future of cleared savannas will differ if herbaceous species maintain all functions that woody vegetation has previously held, or if woody species have functions distinct from those of herbaceous vegetation. Research suggests that savanna soils are susceptible to nitrate leaching, and that trees improve the nutrient status of savanna soils in some situations. The nitrogen capital of cleared savanna is at risk if mobile ions are not captured efficiently by the vegetation. and nitrogen input via N-2 fixation from vegetation and microbiotic crusts is reduced. In order to predict clearing effects on savanna nutrient relations, research should be directed to answering (i) how open or closed nutrient cycles are in natural and cleared savanna, (ii) which functions are performed by savanna constituents such as woody and herbaceous vegetation, native and exotic plant species. termites, and microbiotic 7 crusts in relation to nutrient cycles. In the absence of detailed knowledge about savanna functioning, clearing carries the risk of promoting continuous nutrient depiction.