Mathematical modeling for improved greenhouse gas balances, agro-ecosystems, and policy development: Lessons from the Australian experience

If the land sector is to make significant contributions to mitigating anthropogenic greenhouse gas (GHG) emissions in coming decades, it must do so while concurrently expanding production of food and fiber. In our view, mathematical modeling will be required to provide scientific guidance to meet this challenge. In order to be useful in GHG mitigation policy measures, models must simultaneously meet scientific, software engineering, and human capacity requirements. They can be used to understand GHG fluxes, to evaluate proposed GHG mitigation actions, and to predict and monitor the effects of specific actions; the latter applications require a change in mindset that has parallels with the shift from research modeling to decision support. We compare and contrast 6 agro-ecosystem models (FullCAM, DayCent, DNDC, APSIM, WNMM, and AgMod), chosen because they are used in Australian agriculture and forestry. Underlying structural similarities in the representations of carbon flows though plants and soils in these models are complemented by a diverse range of emphases and approaches to the subprocesses within the agro-ecosystem. None of these agro-ecosystem models handles all land sector GHG fluxes, and considerable model-based uncertainty exists for soil C fluxes and enteric methane emissions. The models also show diverse approaches to the initialisation of model simulations, software implementation, distribution, licensing, and software quality assurance; each of these will differentially affect their usefulness for policy-driven GHG mitigation prediction and monitoring. Specific requirements imposed on the use of models by Australian mitigation policy settings are discussed, and areas for further scientific development of agro-ecosystem models for use in GHG mitigation policy are proposed.

Analysis of the distribution of the number of bidders in construction contract auctions

The number of bidders, N, involved in a construction procurement auction is known to have an important effect on the value of the lowest bid and the mark up applied by bidders. In practice, for example, it is important for a bidder to have a good estimate of N when bidding for a current contract. One approach, instigated by Friedman in 1956, is to make such an estimate by statistical analysis and modelling. Since then, however, finding a suitable model for N has been an enduring problem for researchers and, despite intensive research activity in the subsequent thirty years little progress has been made - due principally to the absence of new ideas and perspectives. This paper resumes the debate by checking old assumptions, providing new evidence relating to concomitant variables and proposing a new model. In doing this and in order to assure universality, a novel approach is developed and tested by using a unique set of twelve construction tender databases from four continents. This shows the new model provides a significant advancement on previous versions. Several new research questions are also posed and other approaches identified for future study.

Abnormal levels of heterogeneous nuclear ribonucleoprotein A2B1 (hnRNPA2B1) in tumour tissue and blood samples from patients diagnosed with lung cancer

Lung cancer is the second most common type of cancer in the world and is the most common cause of cancer-related death in both men and women. Research into causes, prevention and treatment of lung cancer is ongoing and much progress has been made recently in these areas, however survival rates have not significantly improved. Therefore, it is essential to develop biomarkers for early diagnosis of lung cancer, prediction of metastasis and evaluation of treatment efficiency, as well as using these molecules to provide some understanding about tumour biology and translate highly promising findings in basic science research to clinical application. In this investigation, two-dimensional difference gel electrophoresis and mass spectrometry were initially used to analyse conditioned media from a panel of lung cancer and normal bronchial epithelial cell lines. Significant proteins were identified with heterogeneous nuclear ribonucleoprotein A2B1 (hnRNPA2B1), pyruvate kinase M2 isoform (PKM2), Hsc-70 interacting protein and lactate dehydrogenase A (LDHA) selected for analysis in serum from healthy individuals and lung cancer patients. hnRNPA2B1, PKM2 and LDHA were found to be statistically significant in all comparisons. Tissue analysis and knockdown of hnRNPA2B1 using siRNA subsequently demonstrated both the overexpression and potential role for this molecule in lung tumorigenesis. The data presented highlights a number of in vitro derived candidate biomarkers subsequently verified in patient samples and also provides some insight into their roles in the complex intracellular mechanisms associated with tumour progression.