Public Health and Economic Consequences of Methyl Mercury Toxicity to the Developing Brain

Methyl mercury is a developmental neurotoxicant. Exposure results principally from consumption by pregnant women of seafood contaminated by mercury from anthropogenic (70%) and natural (30%) sources. Throughout the 1990s, the U.S. Environmental Protection Agency (EPA) made steady progress in reducing mercury emissions from anthropogenic sources, especially from power plants, which account for 41% of anthropogenic emissions. However, the U.S. EPA recently proposed to slow this progress, citing high costs of pollution abatement. To put into perspective the costs of controlling emissions from American power plants, we have estimated the economic costs of methyl mercury toxicity attributable to mercury from these plants. We used an environmentally attributable fraction model and limited our analysis to the neurodevelopmental impacts—specifically loss of intelligence. Using national blood mercury prevalence data from the Centers for Disease Control and Prevention, we found that between 316,588 and 637,233 children each year have cord blood mercury levels > 5.8 μg/L, a level associated with loss of IQ. The resulting loss of intelligence causes diminished economic productivity that persists over the entire lifetime of these children. This lost productivity is the major cost of methyl mercury toxicity, and it amounts to $8.7 billion annually (range,$2.2–43.8 billion; all costs are in 2000 US$). Of this total,$1.3 billion (range, $0.1–6.5 billion) each year is attributable to mercury emissions from American power plants. This significant toll threatens the economic health and security of the United States and should be considered in the debate on mercury pollution controls

Hypernovae and Other Black-Hole-Forming Supernovae

During the last few years, a number of exceptional core-collapse supernovae (SNe) have been discovered. Their kinetic energy of the explosions are larger by more than an order of magnitude than the typical values for this type of SNe, so that these SNe have been called `Hypernovae'. We first describe how the basic properties of hypernovae can be derived from observations and modeling. These hypernovae seem to come from rather massive stars, thus forming black holes. On the other hand, there are some examples of massive SNe with only a small kinetic energy. We suggest that stars with non-rotating black holes are likely to collapse "quietly" ejecting a small amount of heavy elements (Faint supernovae). In contrast, stars with rotating black holes are likely to give rise to very energetic supernovae (Hypernovae). We present distinct nucleosynthesis features of these two types of "black-hole-forming" supernovae. Hypernova nucleosynthesis is characterized by larger abundance ratios (Zn,Co,V,Ti)/Fe and smaller (Mn,Cr)/Fe. Nucleosynthesis in Faint supernovae is characterized by a large amount of fall-back. We show that the abundance pattern of the most Fe deficient star, HE0107-5240, and other extremely metal-poor carbon-rich stars are in good accord with those of black-hole-forming supernovae, but not pair-instability supernovae. This suggests that black-hole-forming supernovae made important contributions to the early Galactic (and cosmic) chemical evolution.Comment: 49 pages, to be published in "Stellar Collapse" (Astrophysics and Space Science; Kluwer) ed. C. L. Fryer (2003

Histone deacetylases as new therapy targets for platinum-resistant epithelial ovarian cancer

Introduction: In developed countries, ovarian cancer is the fourth most common cancer in women. Due to the nonspecific symptomatology associated with the disease many patients with ovarian cancer are diagnosed late, which leads to significantly poorer prognosis. Apart from surgery and radiotherapy, a substantial number of ovarian cancer patients will undergo chemotherapy and platinum based agents are the mainstream first-line therapy for this disease. Despite the initial efficacy of these therapies, many women relapse; therefore, strategies for second-line therapies are required. Regulation of DNA transcription is crucial for tumour progression, metastasis and chemoresistance which offers potential for novel drug targets. Methods: We have reviewed the existing literature on the role of histone deacetylases, nuclear enzymes regulating gene transcription. Results and conclusion: Analysis of available data suggests that a signifant proportion of drug resistance stems from abberant gene expression, therefore HDAC inhibitors are amongst the most promising therapeutic targets for cancer treatment. Together with genetic testing, they may have a potential to serve as base for patient-adapted therapies

Tissue Microenvironments Define and Get Reinforced by Macrophage Phenotypes in Homeostasis or during Inflammation, Repair and Fibrosis

Current macrophage phenotype classifications are based on distinct in vitro culture conditions that do not adequately mirror complex tissue environments. In vivo monocyte progenitors populate all tissues for immune surveillance which supports the maintenance of homeostasis as well as regaining homeostasis after injury. Here we propose to classify macrophage phenotypes according to prototypical tissue environments, e.g. as they occur during homeostasis as well as during the different phases of (dermal) wound healing. In tissue necrosis and/or infection, damage- and/or pathogen-associated molecular patterns induce proinflammatory macrophages by Toll-like receptors or inflammasomes. Such classically activated macrophages contribute to further tissue inflammation and damage. Apoptotic cells and antiinflammatory cytokines dominate in postinflammatory tissues which induce macrophages to produce more antiinflammatory mediators. Similarly, tumor-associated macrophages also confer immunosuppression in tumor stroma. Insufficient parenchymal healing despite abundant growth factors pushes macrophages to gain a profibrotic phenotype and promote fibrocyte recruitment which both enforce tissue scarring. Ischemic scars are largely devoid of cytokines and growth factors so that fibrolytic macrophages that predominantly secrete proteases digest the excess extracellular matrix. Together, macrophages stabilize their surrounding tissue microenvironments by adapting different phenotypes as feed-forward mechanisms to maintain tissue homeostasis or regain it following injury. Furthermore, macrophage heterogeneity in healthy or injured tissues mirrors spatial and temporal differences in microenvironments during the various stages of tissue injury and repair. Copyright (C) 2012 S. Karger AG, Base

Design and optimisation of organic Rankine cycles for waste heat recovery in marine applications using the principles of natural selection

Power cycles using alternative working fluids are currently receiving significant attention. Selection of working fluid among many candidates is a key topic and guidelines have been presented. A general problem is that the selection is based on numerous criteria, such as thermodynamic performance, boundary conditions, hazard levels and environmental concerns. A generally applicable methodology, based on the principles of natural selection, is presented and used to determine the optimum working fluid, boiler pressure and Rankine cycle process layout for scenarios related to marine engine heat recovery. Included in the solution domain are 109 fluids in sub and supercritical processes, and the process is adapted to the properties of the individual fluid. The efficiency losses caused by imposing process constraints are investigated to help propose a suitable process layout. Hydrocarbon dry type fluids in recuperated processes produced the highest efficiencies, while wet and isentropic fluids were superior in non-recuperated processes. The results suggested that at design point, the requirements of process simplicity, low operating pressure and low hazard resulted in cumulative reductions in cycle efficiency. Furthermore, the results indicated that non-flammable fluids were able to produce near optimum efficiency in recuperated high pressure processes

Algorithms to predict cerebral malaria in murine models using the SHIRPA protocol

<p>Abstract</p> <p>Background</p> <p><it>Plasmodium berghei </it>ANKA infection in C57Bl/6 mice induces cerebral malaria (CM), which reproduces, to a large extent, the pathological features of human CM. However, experimental CM incidence is variable (50-100%) and the period of incidence may present a range as wide as 6-12 days post-infection. The poor predictability of which and when infected mice will develop CM can make it difficult to determine the causal relationship of early pathological changes and outcome. With the purpose of contributing to solving these problems, algorithms for CM prediction were built.</p> <p>Methods</p> <p>Seventy-eight <it>P. berghei</it>-infected mice were daily evaluated using the primary SHIRPA protocol. Mice were classified as CM+ or CM- according to development of neurological signs on days 6-12 post-infection. Logistic regression was used to build predictive models for CM based on the results of SHIRPA tests and parasitaemia.</p> <p>Results</p> <p>The overall CM incidence was 54% occurring on days 6-10. Some algorithms had a very good performance in predicting CM, with the area under the receiver operator characteristic (_auROC) curve ≥ 80% and positive predictive values (PV+) ≥ 95, and correctly predicted time of death due to CM between 24 and 72 hours before development of the neurological syndrome (_auROC = 77-93%; PV+ = 100% using high cut off values). Inclusion of parasitaemia data slightly improved algorithm performance.</p> <p>Conclusion</p> <p>These algorithms work with data from a simple, inexpensive, reproducible and fast protocol. Most importantly, they can predict CM development very early, estimate time of death, and might be a valuable tool for research using CM murine models.</p

A Search for Dark Higgs Bosons

Recent astrophysical and terrestrial experiments have motivated the proposal of a dark sector with GeV-scale gauge boson force carriers and new Higgs bosons. We present a search for a dark Higgs boson using 516 fb-1 of data collected with the BABAR detector. We do not observe a significant signal and we set 90% confidence level upper limits on the product of the Standard Model-dark sector mixing angle and the dark sector coupling constant.Comment: 7 pages, 5 postscript figures, published version with improved plots for b/w printin

Performance of CMS muon reconstruction in pp collision events at sqrt(s) = 7 TeV

The performance of muon reconstruction, identification, and triggering in CMS has been studied using 40 inverse picobarns of data collected in pp collisions at sqrt(s) = 7 TeV at the LHC in 2010. A few benchmark sets of selection criteria covering a wide range of physics analysis needs have been examined. For all considered selections, the efficiency to reconstruct and identify a muon with a transverse momentum pT larger than a few GeV is above 95% over the whole region of pseudorapidity covered by the CMS muon system, abs(eta) < 2.4, while the probability to misidentify a hadron as a muon is well below 1%. The efficiency to trigger on single muons with pT above a few GeV is higher than 90% over the full eta range, and typically substantially better. The overall momentum scale is measured to a precision of 0.2% with muons from Z decays. The transverse momentum resolution varies from 1% to 6% depending on pseudorapidity for muons with pT below 100 GeV and, using cosmic rays, it is shown to be better than 10% in the central region up to pT = 1 TeV. Observed distributions of all quantities are well reproduced by the Monte Carlo simulation.Comment: Replaced with published version. Added journal reference and DO