The role of valuation and bargaining in optimising transboundary watercourse treaty regimes

In the face of water scarcity, growing water demands, population increase, ecosystem degradation, climate change, and so on transboundary watercourse states inevitably have to make difficult decisions on how finite quantities of water are distributed. Such waters, and their associated ecosystem services, offer multiple benefits. Valuation and bargaining can play a key role in the sharing of these ecosystems services and their associated benefits across sovereign borders. Ecosystem services in transboundary watercourses essentially constitute a portfolio of assets. Whilst challenging, their commodification, which creates property rights, supports trading. Such trading offers a means by which to resolve conflicts over competing uses and allows states to optimise their ‘portfolios’. However, despite this potential, adoption of appropriate treaty frameworks that might facilitate a market-based approach to the discovery and allocation of water-related ecosystem services at the transboundary level remains both a challenge, and a topic worthy of further study. Drawing upon concepts in law and economics, this paper therefore seeks to advance the study of how treaty frameworks might be developed in a way that supports such a market-based approach to ecosystem services and transboundary waters

Multidimentional proteomics for cell biology

The proteome is a dynamic system in which each protein has interconnected properties — dimensions — that together contribute to the phenotype of a cell. Measuring these properties has proved challenging owing to their diversity and dynamic nature. Advances in mass spectrometry-based proteomics now enable the measurement of multiple properties for thousands of proteins, including their abundance, isoform expression, turnover rate, subcellular localization, post-translational modifications and interactions. Complementing these experimental developments are new data analysis, integration and visualization tools as well as data-sharing resources. Together, these advances in the multidimensional analysis of the proteome are transforming our understanding of various cellular and physiological processes

Growth and Mass Spectrometry Profile of Alternaria Alternata Pigment Grown in Maize Grain extract

Alternaria species are common saprophytes found in a variety of habitats as ubiquitous agents of decay. Alternaria spp. produces about sixty different secondary metabolites. In the present investigation, growth and production of pigment from Alternaria alternata was studied in maize grain extract at pH 4-9. The reddish brown pigment was extracted, estimated and partially purified by fractionation. Through mass spectrometry, major constituents of pigment from Alternaria alternata such as Tenuazoic acid (m/z 198), Stemphyperylenol (m/z 253), Alterperylenol (m/z 351), Alternariol (m/z 259.200), Altenuene (m/z 292), Alternarienoic acid (m/z 279.35) and Alternariol 5 methyl ether (m/z 273.20) were identified. The bio-prospecting of these secondary metabolites in industrial applications is also discussed

Comorbidity, age, race and stage at diagnosis in colorectal cancer: a retrospective, parallel analysis of two health systems

© 2008 Zafar et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Background : Stage at diagnosis plays a significant role in colorectal cancer (CRC) survival. Understanding which factors contribute to a more advanced stage at diagnosis is vital to improving overall survival. Comorbidity, race, and age are known to impact receipt of cancer therapy and survival, but the relationship of these factors to stage at diagnosis of CRC is less clear. The objective of this study is to investigate how comorbidity, race and age influence stage of CRC diagnosis. Methods : Two distinct healthcare populations in the United States (US) were retrospectively studied. Using the Cancer Care Outcomes Research and Surveillance Consortium database, we identified CRC patients treated at 15 Veterans Administration (VA) hospitals from 2003–2007. We assessed metastatic CRC patients treated from 2003–2006 at 10 non-VA, fee-for-service (FFS) practices. Stage at diagnosis was dichotomized (non-metastatic, metastatic). Race was dichotomized (white, non-white). Charlson comorbidity index and age at diagnosis were calculated. Associations between stage, comorbidity, race, and age were determined by logistic regression. Results : 342 VA and 340 FFS patients were included. Populations differed by the proportion of patients with metastatic CRC at diagnosis (VA 27% and FFS 77%) reflecting differences in eligibility criteria for inclusion. VA patients were mean (standard deviation; SD) age 67 (11), Charlson index 2.0 (1.0), and were 63% white. FFS patients were mean age 61 (13), Charlson index 1.6 (1.0), and were 73% white. In the VA cohort, higher comorbidity was associated with earlier stage at diagnosis after adjusting for age and race (odds ratio (OR) 0.76, 95% confidence interval (CI) 0.58–1.00; p = 0.045); no such significant relationship was identified in the FFS cohort (OR 1.09, 95% CI 0.82–1.44; p = 0.57). In both cohorts, no association was found between stage at diagnosis and either age or race. Conclusion : Higher comorbidity may lead to earlier stage of CRC diagnosis. Multiple factors, perhaps including increased interactions with the healthcare system due to comorbidity, might contribute to this finding. Such increased interactions are seen among patients within a healthcare system like the VA system in the US versus sporadic interactions which may be seen with FFS healthcare

Knockdown of MBP-1 in Human Foreskin Fibroblasts Induces p53-p21 Dependent Senescence

MBP-1 acts as a general transcriptional repressor. Overexpression of MBP-1 induces cell death in a number of cancer cells and regresses tumor growth. However, the function of endogenous MBP-1 in normal cell growth regulation remains unknown. To unravel the role of endogenous MBP-1, we knocked down MBP-1 expression in primary human foreskin fibroblasts (HFF) by RNA interference. Knockdown of MBP-1 in HFF (HFF-MBPsi-4) resulted in an induction of premature senescence, displayed flattened cell morphology, and increased senescence-associated beta-galactosidase activity. FACS analysis of HFF-MBPsi-4 revealed accumulation of a high number of cells in the G1-phase. A significant upregulation of cyclin D1 and reduction of cyclin A was detected in HFF-MBPsi-4 as compared to control HFF. Senescent fibroblasts exhibited enhanced expression of phosphorylated and acetylated p53, and cyclin-dependent kinase inhibitor, p21. Further analysis suggested that promyolocytic leukemia protein (PML) bodies are dramatically increased in HFF-MBPsi-4. Together, these results demonstrated that knockdown of endogenous MBP-1 is involved in cellular senescence of HFF through p53-p21 pathway

Computational solutions for omics data

High-throughput experimental technologies are generating increasingly massive and complex genomic data sets. The sheer enormity and heterogeneity of these data threaten to make the arising problems computationally infeasible. Fortunately, powerful algorithmic techniques lead to software that can answer important biomedical questions in practice. In this Review, we sample the algorithmic landscape, focusing on state-of-the-art techniques, the understanding of which will aid the bench biologist in analysing omics data. We spotlight specific examples that have facilitated and enriched analyses of sequence, transcriptomic and network data sets.National Institutes of Health (U.S.) (Grant GM081871

Integrating Factor Analysis and a Transgenic Mouse Model to Reveal a Peripheral Blood Predictor of Breast Tumors

Abstract Background Transgenic mouse tumor models have the advantage of facilitating controlled in vivo oncogenic perturbations in a common genetic background. This provides an idealized context for generating transcriptome-based diagnostic models while minimizing the inherent noisiness of high-throughput technologies. However, the question remains whether models developed in such a setting are suitable prototypes for useful human diagnostics. We show that latent factor modeling of the peripheral blood transcriptome in a mouse model of breast cancer provides the basis for using computational methods to link a mouse model to a prototype human diagnostic based on a common underlying biological response to the presence of a tumor. Methods We used gene expression data from mouse peripheral blood cell (PBC) samples to identify significantly differentially expressed genes using supervised classification and sparse ANOVA. We employed these transcriptome data as the starting point for developing a breast tumor predictor from human peripheral blood mononuclear cells (PBMCs) by using a factor modeling approach. Results The predictor distinguished breast cancer patients from healthy individuals in a cohort of patients independent from that used to build the factors and train the model with 89% sensitivity, 100% specificity and an area under the curve (AUC) of 0.97 using Youden's J-statistic to objectively select the model's classification threshold. Both permutation testing of the model and evaluating the model strategy by swapping the training and validation sets highlight its stability. Conclusions We describe a human breast tumor predictor based on the gene expression of mouse PBCs. This strategy overcomes many of the limitations of earlier studies by using the model system to reduce noise and identify transcripts associated with the presence of a breast tumor over other potentially confounding factors. Our results serve as a proof-of-concept for using an animal model to develop a blood-based diagnostic, and it establishes an experimental framework for identifying predictors of solid tumors, not only in the context of breast cancer, but also in other types of cancer.</p

Paniya Voices: A Participatory Poverty and Health Assessment among a marginalized South Indian tribal population

<p>Abstract</p> <p>Background</p> <p>In India, indigenous populations, known as <it>Adivasi </it>or Scheduled Tribes (STs), are among the poorest and most marginalized groups. 'Deprived' ST groups tend to display high levels of resignation and to lack the capacity to aspire; consequently their health perceptions often do not adequately correspond to their real health needs. Moreover, similar to indigenous populations elsewhere, STs often have little opportunity to voice perspectives framed within their own cultural worldviews. We undertook a study to gather policy-relevant data on the views, experiences, and priorities of a marginalized and previously enslaved tribal group in South India, the Paniyas, who have little 'voice' or power over their own situation.</p> <p>Methods/design</p> <p>We implemented a Participatory Poverty and Health Assessment (PPHA). We adopted guiding principles and an ethical code that promote respect for Paniya culture and values. The PPHA, informed by a vulnerability framework, addressed five key themes (health and illness, well-being, institutions, education, gender) using participatory approaches and qualitative methods. We implemented the PPHA in five Paniya colonies (clusters of houses in a small geographical area) in a <it>gram panchayat </it>(lowest level decentralized territorial unit) to generate data that can be quickly disseminated to decision-makers through interactive workshops and public forums.</p> <p>Preliminary findings</p> <p>Findings indicated that the Paniyas are caught in multiple 'vulnerability traps', that is, they view their situation as vicious cycles from which it is difficult to break free.</p> <p>Conclusion</p> <p>The PPHA is a potentially useful approach for global health researchers working with marginalized communities to implement research initiatives that will address those communities' health needs in an ethical and culturally appropriate manner.</p

Nutritional Indices of the Cotton Bollworm, Helicoverpa armigera, on 13 Soybean Varieties

The effects of 13 soybean varieties (356, M4, M7, M9, Clark, Sahar, JK, BP, Williams, L17, Zane, Gorgan3, and DPX) on nutritional indices of the cotton bollworm, Helicoverpa armigera (Hübner) (Lepidoptera: Noctuidae), were determined at 25 ± 1° C, 65 ± 5% RH and a photoperiod of 16:8 L:D. Fourth instar larvae reared on Zane showed the highest efficiency of conversion of digested food (ECD) and approximate digestibility (AD) values (0.299 and 0.867, respectively) compared with other varieties. The lowest value of ECD and food consumed (FC) was on 356 (0.133 and 53.82 mg, respectively). The highest and lowest efficiency of conversion of ingested food (ECI) of fifth instar larvae (0.235 and 0.156, respectively) were on Zane and M4, respectively. The ECI and ECD values of whole larval instars were the highest on M7 (0.524 and 0.820, respectively) and lowest on Sahar (0.279 and 0.353, respectively). However, the highest and lowest value of consumption index (CI) was on M7 (7.351) and BP (3.462). Among the different varieties of soybean, the highest AD value was on M9 (0.858), and the lowest was on Zane (0.597). The results indicated that M4, Sahar, and JK were partially resistant to H. armigera

Hypoxia induces differential translation of enolase/MBP-1

<p>Abstract</p> <p>Background</p> <p>Hypoxic microenvironments in tumors contribute to transformation, which may alter metabolism, growth, and therapeutic responsiveness. The α-enolase gene encodes both a glycolytic enzyme (α-enolase) and a DNA-binding tumor suppressor protein, c-myc binding protein (MBP-1). These divergent α-enolase gene products play central roles in glucose metabolism and growth regulation and their differential regulation may be critical for tumor adaptation to hypoxia. We have previously shown that MBP-1 and its binding to the c-myc P₂promoter regulates the metabolic and cellular growth changes that occur in response to altered exogenous glucose concentrations.</p> <p>Results</p> <p>To examine the regulation of α-enolase and MBP-1 by a hypoxic microenvironment in breast cancer, MCF-7 cells were grown in low, physiologic, or high glucose under 1% oxygen. Our results demonstrate that adaptation to hypoxia involves attenuation of MBP-1 translation and loss of MBP-1-mediated regulation of c-myc transcription, evidenced by decreased MBP-1 binding to the c-myc P₂promoter. This allows for a robust increase in c-myc expression, "early c-myc response", which stimulates aerobic glycolysis resulting in tumor acclimation to oxidative stress. Increased α-enolase mRNA and preferential translation/post-translational modification may also allow for acclimatization to low oxygen, particularly under low glucose concentrations.</p> <p>Conclusions</p> <p>These results demonstrate that malignant cells adapt to hypoxia by modulating α-enolase/MBP-1 levels and suggest a mechanism for tumor cell induction of the hyperglycolytic state. This important "feedback" mechanism may help transformed cells to escape the apoptotic cascade, allowing for survival during limited glucose and oxygen availability.</p