Savior Siblings,Parenting and the Moral Valorization of Children

Philosophy has long been concerned with ‘moral status’. Discussions about the moral status of children, however, seem often to promote confusion rather than clarity. Using the creation of ‘savior siblings’ as an example, this paper provides a philosophical critique of the moral status of children and the moral relevance of parenting and the role that formative experience, regret and relational autonomy play in parental decisions. We suggest that parents make moral decisions that are guided by the moral significance they attach to children, to sick children and most importantly, to a specific sick child (theirs). This moral valorization is rarely made explicit and has generally been ignored by both philosophers and clinicians in previous critiques. Recognizing this, however, may transform not only the focus of bioethical discourse but also the policies and practices surrounding the care of children requiring bone marrow or cord blood transplantation by better understanding the values at stake behind parental decision making.     

Preliminary Assessment of the Environmental Benefits of Enzyme Bleaching for Pulp and Paper Making (7 pp)

Goal and Background The LCA methodology is used to compare the potential environmental benefits of an emerging biotechnology, enzyme-bleaching, with those of elemental chlorine free (ECF) bleaching, an existing technology that is widely used in paper making. Through the use of biodegradable enzymes to supplement, or eventually to replace, chemicals in the bleaching process to extract lignin, enzyme bleaching processes are aimed to reduce the use of chlorine based bleaching chemicals and to achieve cost savings by circumventing investment into oxygen delignification or ozone bleaching technology. Scope and Method The assessment is conducted using SimaPro 4.0 and focuses on the processes within the bleach plant stage. For this study, ECF is replaced by enzyme bleaching only in the first stage of the bleaching process. Because this is a comparative study, all upstream and downstream processes are excluded. The impact categories based on Eco-indicator 95 are used to characterize the inventory data in this study. Other methodologies, such as Eco-indicator 99 and CML 2000, have not been chosen as they are more region-specific and are not yet fully applicable to the Canadian environmental condition. A new initiative to develop a Canadian Life Cycle Impact Assessment (LCIA) Method is ongoing at the Interuniversity Reference Center for the Life Cycle Assessment, Interpretation and Management of Products, Processes and Services (CIRAIG), Ecole Polytechnique, Canada. Results and Conclusion The analysis shows that the introduction of enzyme bleaching into the ECF process significantly improves the overall environmental performance in the majority of the impact categories. Extending the substitution of enzyme bleaching for chlorine dioxide is warranted. Of the three impact categories where increased impact was noted, two of these which increased emissions of greenhouse gases and increased incidence of summer smog, would be completely eliminated if the enzyme mediator was manufactured at the point of use. There remains a potential for increased impact from eutrophication, which would need to be managed.Recommendations and Outlook With the only partial substitution of ECF by enzyme bleaching examined here, chlorine dioxide consumption, energy consumption, NaOH consumption, and transportation remain the key hot spots and warrant further research. Anything that can be done to replace or reduce chlorine dioxide consumption will benefit the environment.     

An integrated approach to optimization of Escherichia coli fermentations using historical data

Using a fermentation database for Escherichia coli producing green fluorescent protein (GFP), we have implemented a novel three-step optimization method to identify the process input variables most important in modeling the fermentation, as well as the values of those critical input variables that result in an increase in the desired output. In the first step of this algorithm, we use either decision-tree analysis (DTA) or information theoretic subset selection (ITSS) as a database mining technique to identify which process input variables best classify each of the process outputs (maximum cell concentration, maximum product concentration, and productivity) monitored in the experimental fermentations. The second step of the optimization method is to train an artificial neural network (ANN) model of the process input-output data, using the critical inputs identified in the first step. Finally, a hybrid genetic algorithm (hybrid GA), which includes both gradient and stochastic search methods, is used to identify the maximum output modeled by the ANN and the values of the input conditions that result in that maximum. The results of the database mining techniques are compared, both in terms of the inputs selected and the subsequent ANN performance. For the E. coli process used in this study, we identified 6 inputs from the original 13 that resulted in an ANN that best modeled the GFP fluorescence outputs of an independent test set. Values of the six inputs that resulted in a modeled maximum fluorescence were identified by applying a hybrid GA to the ANN model developed. When these conditions were tested in laboratory fermentors, an actual maximum fluorescence of 2.16E6 AU was obtained. The previous high value of fluorescence that was observed was 1.51E6 AU. Thus, this input condition set that was suggested by implementing the proposed optimization scheme on the available historical database increased the maximum fluorescence by 55%.     

Easy-to-implement Bayesian methods for dose-escalation studies in healthy volunteers

In phase I clinical trials, experimental drugs are administered to healthy volunteers in order to establish their safety and to explore the relationship between the dose taken and the concentration found in plasma. Each volunteer receives a series of increasing single doses. In this paper a Bayesian decision procedure is developed for choosing the doses to give in the next round of the study, taking into account both prior information and the responses observed so far. The procedure seeks the optimal doses for learning about the dose-concentration relationship, subject to a constraint which reduces the risk of administering dangerously high doses. Individual volunteers receive more than one dose, and the pharmacokinetic responses observed are, after logarithmic transformation, treated as approximately normally distributed. Thus data analysis can be achieved by fitting linear mixed models. By expressing prior information as 'pseudo-data', and by maximizing over posterior distributions rather than taking expectations, a procedure which can be implemented using standard mixed model software is derived. Comparisons are made with existing approaches to the conduct of these studies, and the new method is illustrated using real and simulated data.To whom correspondence should be addressed.     

A geography‐based critique of new US biofuels regulations

The new renewable fuels standard (RFS 2) aims to distinguish corn‐ethanol that achieves a 20% reduction in greenhouse gas (GHG) emissions compared with gasoline. Field data from Kim et al. (2009) and from our own study suggest that geographic variability in the GHG emissions arising from corn production casts considerable doubt on the approach used in the RFS 2 to measure compliance with the 20% target. If regulators wish to require compliance of fuels with specific GHG emission reduction thresholds, then data from growing biomass should be disaggregated to a level that captures the level of variability in grain corn production and the application of life cycle assessment to biofuels should be modified to capture this variability.     

Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction

The task of modeling the distribution of a large number of tree species under future climate scenarios presents unique challenges. First, the model must be robust enough to handle climate data outside the current range without producing unacceptable instability in the output. In addition, the technique should have automatic search mechanisms built in to select the most appropriate values for input model parameters for each species so that minimal effort is required when these parameters are fine-tuned for individual tree species. We evaluated four statistical models—Regression Tree Analysis (RTA), Bagging Trees (BT), Random Forests (RF), and Multivariate Adaptive Regression Splines (MARS)—for predictive vegetation mapping under current and future climate scenarios according to the Canadian Climate Centre global circulation model. To test, we applied these techniques to four tree species common in the eastern United States: loblolly pine (Pinus taeda), sugar maple (Acer saccharum), American beech (Fagus grandifolia), and white oak (Quercus alba). When the four techniques were assessed with Kappa and fuzzy Kappa statistics, RF and BT were superior in reproducing current importance value (a measure of basal area in addition to abundance) distributions for the four tree species, as derived from approximately 100,000 USDA Forest Service’s Forest Inventory and Analysis plots. Future estimates of suitable habitat after climate change were visually more reasonable with BT and RF, with slightly better performance by RF as assessed by Kappa statistics, correlation estimates, and spatial distribution of importance values. Although RTA did not perform as well as BT and RF, it provided interpretive models for species whose distributions were captured well by our current set of predictors. MARS was adequate for predicting current distributions but unacceptable for future climate. We consider RTA, BT, and RF modeling approaches, especially when used together to take advantage of their individual strengths, to be robust for predictive mapping and recommend their inclusion in the ecological toolbox.     

Equitable and reasonable freshwater allocation based on a multi-criteria decision making approach with hydrologically constrained bankruptcy rules

Effective water allocation among multiple jurisdictions is a key instrument to improve water use efficiency within the basin scale. To achieve equitable and reasonable water allocation, natural, socio-economic, and ecological conditions within a specific basin need to be systematically considered. Based on the main principles of equitable and reasonable water allocation that were defined by UN Watercourses Convention, an integrated multi-criteria decision making (MCDM) with bankruptcy rules (IMCDM-BR) under multiple hydrological constraints were proposed to allocate trans-jurisdiction water resources in Guanting reservoir basin (GRB), a shared basin between Zhangjiakou and Beijing in China. Projection pursuit (PP), as one of an effective MCDM approach, was employed to synthesize values of the related principles, which were ranked as weights for the corresponding water claims by relevant agents. Then, the weighted bankruptcy rules (BR) with multiple hydrological constraints were applied to allocate water among the related agents of the studying basin. The results of ordinary bankruptcy rules, bankruptcy rules with regular and hydrological constraints, and the proposed methods were compared and discussed. Among them, the proposed IMCDM-BR was recommended as an effective tool to support practical water allocation. Moreover, factors of equitable and reasonable water allocation were comprehensively considered. The results can thus be used for facilitating negotiation in trans-jurisdiction water allocation among agents within basins.     

Effect of species‐counting protocols and the spatial distribution of effort on rarefaction curves in relation to decision making in environmental‐impact assessments

Rarefaction Curves are frequently used in Environmental Impact Assessments to evaluate sampling sufficiency, but without clear guidelines of how to ensure that the assumptions of the methods are met. Infrastructure projects in the Brazilian Amazon and elsewhere often occupy extensive areas in remote locations with difficult access, and random sampling under such conditions is impractical. We tested the influence of sampling unit (sample or individual), and geographic distance between samples on rarefaction curve s, and evaluated the magnitude of errors resulting from the misuse of rarefaction curve in decision making, using frogs from four Amazonian sampling sites. Individual‐based rarefaction curve were steeper than those generated by sample‐based rarefaction curve. Geographic distance influenced the number of exclusive species in a predictable fashion only in one area, and not in the Environmental Impact Assessment site. Misuse of rarefaction curve generated large errors in the identification of vulnerable taxa. Because the rarefaction curve model is sensitive to the assumption of randomness and geographic distance can influence it unpredictably, we suggest that rarefaction curve should generally not be used to estimate sample completeness when making management decisions for environmental licensing purposes.