Cooperative Research and Development for Advanced Microturbines Program on Advanced Integrated Microturbine System

The Advanced Integrated Microturbine Systems (AIMS) project was kicked off in October of 2000 to develop the next generation microturbine system. The overall objective of the project was to develop a design for a 40% electrical efficiency microturbine system and demonstrate many of the enabling technologies. The project was initiated as a collaborative effort between several units of GE, Elliott Energy Systems, Turbo Genset, Oak Ridge National Lab and Kyocera. Since the inception of the project the partners have changed but the overall direction of the project has stayed consistent. The project began as a systems study to identify design options to achieve the ultimate goal of 40% electrical efficiency. Once the optimized analytical design was identified for the 40% system, it was determined that a 35% efficient machine would be capable of demonstrating many of the advanced technologies within the given budget and timeframe. The items that would not be experimentally demonstrated were fully produced ceramic parts. However, to understand the requirements of these ceramics, an effort was included in the project to experimentally evaluate candidate materials in representative conditions. The results from this effort would clearly identify the challenges and improvement required of these materials for the full design. Following the analytical effort, the project was dedicated to component development and testing. Each component and subsystem was designed with the overall system requirements in mind and each tested to the fullest extent possible prior to being integrated together. This method of component development and evaluation helps to minimize the technical risk of the project. Once all of the components were completed, they were assembled into the full system and experimentally evaluated

Modelling the impact of liner shipping network perturbations on container cargo routing: Southeast Asia to Europe application

Understanding how container routing stands to be impacted by different scenarios of liner shipping network perturbations such as natural disasters or new major infrastructure developments is of key importance for decision-making in the liner shipping industry. The variety of actors and processes within modern supply chains and the complexity of their relationships have previously led to the development of simulation-based models, whose application has been largely compromised by their dependency on extensive and often confidential sets of data. This study proposes the application of optimisation techniques less dependent on complex data sets in order to develop a quantitative framework to assess the impacts of disruptive events on liner shipping networks. We provide a categorization of liner network perturbations, differentiating between systemic and external and formulate a container assignment model that minimises routing costs extending previous implementations to allow feasible solutions when routing capacity is reduced below transport demand. We develop a base case network for the Southeast Asia to Europe liner shipping trade and review of accidents related to port disruptions for two scenarios of seismic and political conflict hazards. Numerical results identify alternative routing paths and costs in the aftermath of port disruptions scenarios and suggest higher vulnerability of intra-regional connectivity

Improved Functional Prediction of Hypothetical Proteins from \u3ci\u3eListeria monocytogenes\u3c/i\u3e 08-5578

Listeria monocytogenes is a foodborne human pathogen responsible for listerosis. The genomes of several L. monocytogenes strains have been recently sequenced. The genome of L. monocytogenes 08-5578, which was in part responsible for a significant listerosis outbreak in 2008, contains an unexpectedly high percentage of protein-encoding genes (1,927 out of 3,161; 60.96%) autonomously annotated as hypothetical proteins. The aim of this study was to test whether a manual annotation strategy could be used to assign more meaningful functional names to the hypothetical proteins of 08-5578. A holistic, manual gene annotation strategy that utilized sequence homology, cellular localization predictions, structure-based evidence, phylogeny, and proteinprotein interaction data was used to assign potential cellular roles to 79 out of 100 hypothetical proteins randomly selected from the genome of 08-5578. Of significance, 5 of the 79 hypothetical proteins assigned a more meaningful name may contribute to the virulence of L. monocytogenes 08-5578, by contributing to chemotaxis, cell surface protein sorting, cell wall biosynthesis, and cold adaptation. The findings here support the notion that manual annotations, using a combination of diverse bioinformatics tools, can improve the quality of genomic information provided by automated genome annotation methods alone

Sensitivity of genomic selection to using different prior distributions

Genomic selection describes a selection strategy based on genomic estimated breeding values (GEBV) predicted from dense genetic markers such as single nucleotide polymorphism (SNP) data. Different Bayesian models have been suggested to derive the prediction equation, with the main difference centred around the specification of the prior distributions

Hadronic Parity Violation: a New View through the Looking Glass

Studies of the strangeness changing hadronic weak interaction have produced a number of puzzles that have so far evaded a complete explanation within the Standard Model. Their origin may lie either in dynamics peculiar to weak interactions involving strange quarks or in more general aspects of the interplay between strong and weak interactions. In principle, studies of the strangeness conserving hadronic weak interaction using parity violating hadronic and nuclear observables provide a complementary window on this question. However, progress in this direction has been hampered by the lack of a suitable theoretical framework for interpreting hadronic parity violation measurements in a model-independent way. Recent work involving effective field theory ideas has led to the formulation of such a framework while motivating the development of a number of new hadronic parity violation experiments in few-body systems. In this article, we review these recent developments and discuss the prospects and opportunities for further experimental and theoretical progress.Comment: Manuscript submitted to Annual Reviews of Nuclear and Particle Scienc

Genome position specific priors for genomic prediction

<p>Abstract</p> <p>Background</p> <p>The accuracy of genomic prediction is highly dependent on the size of the reference population. For small populations, including information from other populations could improve this accuracy. The usual strategy is to pool data from different populations; however, this has not proven as successful as hoped for with distantly related breeds. BayesRS is a novel approach to share information across populations for genomic predictions. The approach allows information to be captured even where the phase of SNP alleles and casuative mutation alleles are reversed across populations, or the actual casuative mutation is different between the populations but affects the same gene. Proportions of a four-distribution mixture for SNP effects in segments of fixed size along the genome are derived from one population and set as location specific prior proportions of distributions of SNP effects for the target population. The model was tested using dairy cattle populations of different breeds: 540 Australian Jersey bulls, 2297 Australian Holstein bulls and 5214 Nordic Holstein bulls. The traits studied were protein-, fat- and milk yield. Genotypic data was Illumina 777K SNPs, real or imputed.</p> <p>Results</p> <p>Results showed an increase in accuracy of up to 3.5% for the Jersey population when using BayesRS with a prior derived from Australian Holstein compared to a model without location specific priors. The increase in accuracy was however lower than was achieved when reference populations were combined to estimate SNP effects, except in the case of fat yield. The small size of the Jersey validation set meant that these improvements in accuracy were not significant using a Hotelling-Williams t-test at the 5% level. An increase in accuracy of 1-2% for all traits was observed in the Australian Holstein population when using a prior derived from the Nordic Holstein population compared to using no prior information. These improvements were significant (P<0.05) using the Hotelling Williams t-test for protein- and fat yield.</p> <p>Conclusion</p> <p>For some traits the method might be advantageous compared to pooling of reference data for distantly related populations, but further investigation is needed to confirm the results. For closely related populations the method does not perform better than pooling reference data. However, it does give an increased accuracy compared to analysis based on only one reference population, without an increased computational burden. The approach described here provides a general setup for inclusion of location specific priors: the approach could be used to include biological information in genomic predictions.</p

Conformational distributions of isolated myosin motor domains encode their mechanochemical properties

Myosin motor domains perform an extraordinary diversity of biological functions despite sharing a common mechanochemical cycle. Motors are adapted to their function, in part, by tuning the thermodynamics and kinetics of steps in this cycle. However, it remains unclear how sequence encodes these differences, since biochemically distinct motors often have nearly indistinguishable crystal structures. We hypothesized that sequences produce distinct biochemical phenotypes by modulating the relative probabilities of an ensemble of conformations primed for different functional roles. To test this hypothesis, we modeled the distribution of conformations for 12 myosin motor domains by building Markov state models (MSMs) from an unprecedented two milliseconds of all-atom, explicit-solvent molecular dynamics simulations. Comparing motors reveals shifts in the balance between nucleotide-favorable and nucleotide-unfavorable P-loop conformations that predict experimentally measured duty ratios and ADP release rates better than sequence or individual structures. This result demonstrates the power of an ensemble perspective for interrogating sequence-function relationships