A grid broker pricing mechanism for temporal and budget guarantees

We introduce a pricing mechanism for Grid computing, with the aim of showing how a broker can accept the most appropriate jobs to be computed on time and on budget. We analyse the mechanism’s performance via discrete event simulation, and illustrate its viability, the benefits of a new admission policy and to how slack relates to machine heterogeneity

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge, it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Possible involvement of transcriptional activation of nuclear factor erythroid 2-related factor 2 (Nrf2) in the protective effect of caffeic acid on paraquat-induced oxidative damage in Drosophila melanogaster

Paraquat (PQ) is a widely used herbicide with no antidote which is implicated in the pathogenesis of the Parkinson's disease. The present study then investigated the potential of caffeic acid (CA), a known antioxidant, cardioprotective and neuroprotective molecule to counteract oxidative stress mediated by PQ. In addition, molecular docking was performed to understand the mechanism underlying the inhibitory effect of CA against PQ poisoning. The fruit fly, Drosophila melanogaster, was exposed to PQ (0.44 mg/g of diet) in the absence or presence of CA (0.25, 0.5, 1 and 2 mg/g of died) for 7 days. Data showed that PQ-fed flies had higher incidence of mortality which was associated with mitochondrial dysfunction, increased free Fe(II) content and lipid peroxidation when compared to the control. Co-exposure with CA reduced mortality and markedly attenuated biochemical changes induced by PQ. The mechanism investigated using molecular docking revealed a strong interaction (−6.2 Kcal/mol) of CA with D. melanogaster transcriptional activation of nuclear factor erythroid 2-related factor 2 (Nrf2). This was characterized by the binding of CA to keap-1 domain of Nrf2. Taking together these results indicate the protective effect of CA against PQ-induced oxidative damage in D. melanogaster was likely through its coordination which hinders Nrf2-keap-1 binding leading to an increase of the antioxidant defense system