Why microalgal biofuels won't save the internal combustion machine

Proponents of microalgae biofuel technologies often claim that the world demand of liquid fuels, about 5 trillion liters per year, could be supplied by microalgae cultivated on only a few tens of millions of hectares. This perspective reviews this subject and points out that such projections are greatly exaggerated, because (1) the pro- ductivities achieved in large-scale commercial microalgae production systems, operated year-round, do not surpass those of irrigated tropical crops; (2) cultivating, harvesting and processing microalgae solely for the production of biofuels is simply too expensive using current or prospective technology; and (3) currently available (limited) data suggest that the energy balance of algal biofuels is very poor. Thus, microalgal biofuels are no panacea for depleting oil or global warming, and are unlikely to save the internal combustion machine.

Complementary effects of adenosine and angiotensin II in hypoxemia-induced renal dysfunction in the rabbit.

The acute renal effects of hypoxemia and the ability of the co-administration of an angiotensin converting enzyme inhibitor (perindoprilat) and an adenosine receptor antagonist (theophylline) to prevent these effects were assessed in anesthetized and mechanically-ventilated rabbits. Renal blood flow (RBF) and glomerular filtration rate (GFR) were determined by the clearances of para-aminohippuric acid and inulin, respectively. Each animal acted as its own control. In 8 untreated rabbits, hypoxemia induced a significant drop in mean blood pressure (-12 +/- 2%), GFR (-16 +/- 3%) and RBF (-12 +/- 3%) with a concomitant increase in renal vascular resistance (RVR) (+ 18 +/- 5%), without changes in filtration fraction (FF) (-4 +/- 2%). These results suggest the occurrence of both pre- and postglomerular vasoconstriction during the hypoxemic stress. In 7 rabbits pretreated with intravenous perindoprilat (20 microg/kg), the hypoxemia-induced changes in RBF and RVR were prevented. FF decreased significantly (-18 +/- 2%), while the drop in GFR was partially blunted. These results could be explained by the inhibition of the angiotensin-mediated efferent vasoconstriction by perindoprilat. In 7 additional rabbits, co-administration of perindoprilat and theophylline (1 mg/kg) completely prevented the hypoxemia-induced changes in RBF (+ 11 +/- 3%) and GFR (+ 2 +/- 3%), while RVR decreased significantly (-14 +/- 3%). Since adenosine and angiotensin II were both shown to participate, at least in part, in the renal changes induced by hypoxemia, the beneficial effects of perindoprilat and theophylline in this model could be mediated by complementary actions of angiotensin II and adenosine on the renal vasculature.

Hypocretin (orexin) biology and the pathophysiology of narcolepsy with cataplexy.

The discovery of hypocretins (orexins) and their causal implication in narcolepsy is the most important advance in sleep research and sleep medicine since the discovery of rapid eye movement sleep. Narcolepsy with cataplexy is caused by hypocretin deficiency owing to destruction of most of the hypocretin-producing neurons in the hypothalamus. Ablation of hypocretin or hypocretin receptors also leads to narcolepsy phenotypes in animal models. Although the exact mechanism of hypocretin deficiency is unknown, evidence from the past 20 years strongly favours an immune-mediated or autoimmune attack, targeting specifically hypocretin neurons in genetically predisposed individuals. These neurons form an extensive network of projections throughout the brain and show activity linked to motivational behaviours. The hypothesis that a targeted immune-mediated or autoimmune attack causes the specific degeneration of hypocretin neurons arose mainly through the discovery of genetic associations, first with the HLA-DQB1*06:02 allele and then with the T-cell receptor α locus. Guided by these genetic findings and now awaiting experimental testing are models of the possible immune mechanisms by which a specific and localised brain cell population could become targeted by T-cell subsets. Great hopes for the identification of new targets for therapeutic intervention in narcolepsy also reside in the development of patient-derived induced pluripotent stem cell systems.

Predicting current and future spatial community patterns of plant functional traits

Community-level patterns of functional traits relate to community assembly and ecosystem functioning. By modelling the changes of different indices describing such patterns - trait means, extremes and diversity in communities - as a function of abiotic gradients, we could understand their drivers and build projections of the impact of global change on the functional components of biodiversity. We used five plant functional traits (vegetative height, specific leaf area, leaf dry matter content, leaf nitrogen content and seed mass) and non-woody vegetation plots to model several indices depicting community-level patterns of functional traits from a set of abiotic environmental variables (topographic, climatic and edaphic) over contrasting environmental conditions in a mountainous landscape. We performed a variation partitioning analysis to assess the relative importance of these variables for predicting patterns of functional traits in communities, and projected the best models under several climate change scenarios to examine future potential changes in vegetation functional properties. Not all indices of trait patterns within communities could be modelled with the same level of accuracy: the models for mean and extreme values of functional traits provided substantially better predictive accuracy than the models calibrated for diversity indices. Topographic and climatic factors were more important predictors of functional trait patterns within communities than edaphic predictors. Overall, model projections forecast an increase in mean vegetation height and in mean specific leaf area following climate warming. This trend was important at mid elevation particularly between 1000 and 2000 m asl. With this study we showed that topographic, climatic and edaphic variables can successfully model descriptors of community-level patterns of plant functional traits such as mean and extreme trait values. However, which factors determine the diversity of functional traits in plant communities remains unclear and requires more investigations.

Effects of alternative sets of climatic predictors on species distribution models and associated estimates of extinction risk: a test with plants in an arid environment

Aim To evaluate the effects of using distinct alternative sets of climatic predictor variables on the performance, spatial predictions and future projections of species distribution models (SDMs) for rare plants in an arid environment. . Location Atacama and Peruvian Deserts, South America (18º30'S - 31º30'S, 0 - 3 000 m) Methods We modelled the present and future potential distributions of 13 species of Heliotropium sect. Cochranea, a plant group with a centre of diversity in the Atacama Desert. We developed and applied a sequential procedure, starting from climate monthly variables, to derive six alternative sets of climatic predictor variables. We used them to fit models with eight modelling techniques within an ensemble forecasting framework, and derived climate change projections for each of them. We evaluated the effects of using these alternative sets of predictor variables on performance, spatial predictions and projections of SDMs using Generalised Linear Mixed Models (GLMM). Results The use of distinct sets of climatic predictor variables did not have a significant effect on overall metrics of model performance, but had significant effects on present and future spatial predictions. Main conclusion Using different sets of climatic predictors can yield the same model fits but different spatial predictions of current and future species distributions. This represents a new form of uncertainty in model-based estimates of extinction risk that may need to be better acknowledged and quantified in future SDM studies.

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Past and current climate change has already induced drastic biological changes. We need projections of how future climate change will further impact biological systems. Modeling is one approach to forecast future ecological impacts, but requires data for model parameterization. As collecting new data is costly, an alternative is to use the increasingly available georeferenced species occurrence and natural history databases. Here, we illustrate the use of such databases to assess climate change impacts on mountain flora. We show that these data can be used effectively to derive dynamic impact scenarios, suggesting upward migration of many species and possible extinctions when no suitable habitat is available at higher elevations. Systematically georeferencing all existing natural history collections data in mountain regions could allow a larger assessment of climate change impact on mountain ecosystems in Europe and elsewhere.

Issue review : fiscal year 2011 general fund budget projection.

This issue review provides an overall summary of Iowa's general fund budget. This issue review provides projections for the 2010 fiscal year and fiscal year 2011 budgets based on the October 7, 2009 revenue estimating conference's (REC) revenue estimate. The projection also includes the impact of the Governor's 10 percent across-the-board reduction to fiscal year 2010 general fund appropriations, and the Legislative Services Agency's most recent estimates of built-in and anticipated expenditures for fiscal year 2011.

2006 Cancer in Iowa Report, 2006

In 2006, an estimated 6,300 Iowans will die from cancer, 14 times the number caused by auto fatalities. Cancer is second only to heart disease as a cause of death. These projections are based upon mortality data the State Health Registry of Iowa receives from the Iowa Department of Public Health. The Registry has been recording the occurrence of cancer in Iowa since 1973, and is one of fourteen population-based registries and three supplementary registries nationwide providing data to the National Cancer Institute. In 2006 an estimated 16,000 cancers will be newly diagnosed among Iowa residents. With 2006 Cancer in Iowa the Registry makes a general report to the public on the status of cancer.

2012 Cancer in Iowa Report: A Focus on Lung Cancer, 2012

Cancer is a reportable disease as stated in the Iowa Administrative Code. Cancer data are collected by the State Health Registry of Iowa, located at The University of Iowa in the College of Public Health’s Department of epidemiology. The staff includes more than 50 people. Half of them, situated throughout the state, regularly visit hospitals, clinics, and medical laboratories in Iowa and neighboring states to collect cancer data. A follow-up program tracks more than 99 percent of the cancer survivors diagnosed since 1973. This program provides regular updates for follow-up and survival. The Registry maintains the confidentiality of the patients, physicians, and hospitals providing data. In 2012 data will be collected on a projected 17,500 new cancers among Iowa residents. In situ cases of bladder cancer are included in the projections for bladder cancer, to be in agreement with the definition of reportable cases of the Surveillance, Epidemiology, and End Results (SEER) Program of the NCI. Since 1973 the Iowa Registry has been funded by the SEER Program of the NCI. Iowa represents rural and Midwestern populations and provides data included in many NCI publications. Beginning in 1990 about 5-10 percent of the Registry’s annual operating budget has been provided by the state of Iowa. Beginning in 2003, the University of Iowa has also been providing cost-sharing funds. The Registry also receives funding through grants and contracts with university, state, and national researchers investigating cancer-related topics.

Iowa’s Bridge and Highway Climate Change and Extreme Weather Vulnerability Assessment Pilot, HEPN-707, 2015

The Iowa Department of Transportation (DOT) is responsible for approximately 4,100 bridges and structures that are a part of the state’s primary highway system, which includes the Interstate, US, and Iowa highway routes. A pilot study was conducted for six bridges in two Iowa river basins—the Cedar River Basin and the South Skunk River Basin—to develop a methodology to evaluate their vulnerability to climate change and extreme weather. The six bridges had been either closed or severely stressed by record streamflow within the past seven years. An innovative methodology was developed to generate streamflow scenarios given climate change projections. The methodology selected appropriate rainfall projection data to feed into a streamflow model that generated continuous peak annual streamflow series for 1960 through 2100, which were used as input to PeakFQ to estimate return intervals for floods. The methodology evaluated the plausibility of rainfall projections and credibility of streamflow simulation while remaining consistent with U.S. Geological Survey (USGS) protocol for estimating the return interval for floods. The results were conveyed in an innovative graph that combined historical and scenario-based design metrics for use in bridge vulnerability analysis and engineering design. The pilot results determined the annual peak streamflow response to climate change likely will be basin-size dependent, four of the six pilot study bridges would be exposed to increased frequency of extreme streamflow and would have higher frequency of overtopping, the proposed design for replacing the Interstate 35 bridges over the South Skunk River south of Ames, Iowa is resilient to climate change, and some Iowa DOT bridge design policies could be reviewed to consider incorporating climate change information.

Climate change and plant distribution: local models predict high-elevation persistence

Mountain ecosystems will likely be affected by global warming during the 21st century, with substantial biodiversity loss predicted by species distribution models (SDMs). Depending on the geographic extent, elevation range and spatial resolution of data used in making these models, different rates of habitat loss have been predicted, with associated risk of species extinction. Few coordinated across-scale comparisons have been made using data of different resolution and geographic extent. Here, we assess whether climate-change induced habitat losses predicted at the European scale (10x10' grid cells) are also predicted from local scale data and modeling (25x25m grid cells) in two regions of the Swiss Alps. We show that local-scale models predict persistence of suitable habitats in up to 100% of species that were predicted by a European-scale model to lose all their suitable habitats in the area. Proportion of habitat loss depends on climate change scenario and study area. We find good agreement between the mismatch in predictions between scales and the fine-grain elevation range within 10x10' cells. The greatest prediction discrepancy for alpine species occurs in the area with the largest nival zone. Our results suggest elevation range as the main driver for the observed prediction discrepancies. Local scale projections may better reflect the possibility for species to track their climatic requirement toward higher elevations.

Introduction of snow and geomorphic disturbance variables into predictive models of alpine plant distribution in the Western Swiss Alps

Indirect topographic variables have been used successfully as surrogates for disturbance processes in plant species distribution models (SDM) in mountain environments. However, no SDM studies have directly tested the performance of disturbance variables. In this study, we developed two disturbance variables: a geomorphic index (GEO) and an index of snow redistribution by wind (SNOW). These were developed in order to assess how they improved both the fit and predictive power of presenceabsence SDM based on commonly used topoclimatic (TC) variables for 91 plants in the Western Swiss Alps. The individual contribution of the disturbance variables was compared to TC variables. Maps of models were prepared to spatially test the effect of disturbance variables. On average, disturbance variables significantly improved the fit but not the predictive power of the TC models and their individual contribution was weak (5.6% for GEO and 3.3% for SNOW). However their maximum individual contribution was important (24.7% and 20.7%). Finally, maps including disturbance variables (i) were significantly divergent from TC models in terms of predicted suitable surfaces and connectivity between potential habitats, and (ii) were interpreted as more ecologically relevant. Disturbance variables did not improve the transferability of models at the local scale in a complex mountain system, and the performance and contribution of these variables were highly species-specific. However, improved spatial projections and change in connectivity are important issues when preparing projections under climate change because the future range size of the species will determine the sensitivity to changing conditions.

Statistical mechanics of multi-edge networks

Statistical properties of binary complex networks are well understood and recently many attempts have been made to extend this knowledge to weighted ones. There are, however, subtle yet important considerations to be made regarding the nature of the weights used in this generalization. Weights can be either continuous or discrete magnitudes, and in the latter case, they can additionally have undistinguishable or distinguishable nature. This fact has not been addressed in the literature insofar and has deep implications on the network statistics. In this work we face this problem introducing multiedge networks as graphs where multiple (distinguishable) connections between nodes are considered. We develop a statistical mechanics framework where it is possible to get information about the most relevant observables given a large spectrum of linear and nonlinear constraints including those depending both on the number of multiedges per link and their binary projection. The latter case is particularly interesting as we show that binary projections can be understood from multiedge processes. The implications of these results are important as many real-agent-based problems mapped onto graphs require this treatment for a proper characterization of their collective behavior.

Radiation risks : a way to inform patient : 3.10

Purpose: Diagnostic radiology involving ionizing radiation often leads to crucial information but also involves risk. Estimated cancer risks associated with CT range between 1 in 1000 to 1 in 10 000, depending on age and exposure settings. The aim of this contribution is to provide radiologists a way to inform a patient about these risks on a collective and individual base. Materials and methods: After a brief review of the effects of ionizing radiations, conversion from dose indicators into effective dose will be presented for radiography, fluoroscopy and CT. The Diagnostic Reference Level (DRL) concept will be then introduced to enable the reader to compare the level of exposure of various examinations. Finally, the limit of effective dose will be explained and risk projections after various radiological procedures for adults and children will be presented. Results: From an individual standpoint the benefit of a well justified and optimized CT examination clearly outweigh its risk of inducing a fatal cancer. The uncertainties associated with the effective dose concept should be kept in mind in order to avoid cancer risk projections after an examination on an individual basis. Conclusion: Risk factors or effective dose are not the simplest tools to communicate when dealing with radiological risks. Thus, a set of categories should be preferred as proposed in the ICRP (International Commission on Radiation Protection) report 99.

Plannification hospitalière: Quel scénario démographique pour prédire les besoins en soins hospitaliers?

Contexte: L'évolution démographique est un facteur essentiel pour la planification hospitalière. Le scenario démographique moyen de l'OFS ne répond que partiellement aux exigences de la planification pour le canton du Valais ; il ne permet pas de faire des projections par région hospitalière. De plus, la croissance en 2010 et 2011 selon le scenario OFS est inferieure a celle observée. Nous avons développe un scenario ajuste en tenant compte de l'évolution récente de la population valaisanne, et ce par région hospitalière. Méthode: La population de départ et les taux d'accroissement du au solde naturel et migratoire 2012 ont été déterminés à partir des données réelles 2011. Entre 2002 et 2011, le taux d'accroissement migratoire a fortement varie. Les taux 2011 ont donc été ajustés en fonction des taux moyens d'accroissement migratoire 2002-2011 calcules pour chaque région hospitalière. Pour les années 2013-2025, les écarts de taux d'accroissement naturel et migratoire du scenario OFS ont été repris tel quel. Résultats: Entre 2000 et 2010, la population valaisanne est passée de 276'170 a 312'684 habitants (+13.2%). Entre 2012 et 2025, les deux scenarios prévoient une poursuite de la croissance de la population, mais de manière moins soutenue, particulièrement selon le scenario OFS. Ainsi, en 2020, la population devrait atteindre 348'783 habitants (+11.5%) selon le scenario ajuste et 330'616 (+5.7%) selon le scenario OFS. La part de la population âgée de 65 ans et plus passera de 17.1% en 2010 a 20.5% en 2020 selon le scenario ajuste et a 21.4% selon le scenario OFS. Conclusion: Au vu des limites du scenario moyen de l'OFS, il est pertinent de développer des alternatives tenant compte au mieux des spécificités démographiques cantonales. Le scenario ajuste prédit une croissance plus forte de la population, ce qui a un impact important sur les besoins en soins hospitaliers et la planification hospitalière.