An optimization method for nacelle design

A multi-objective optimiZation method is demonstrated using an evolutionary genetic algorithm. The applicability of this method to preliminary nacelle design is demonstrated by coupling it with a response surface model of a wide range of nacelle designs. These designs were modelled using computational fluid dynamics and a Kriging interpolation was carried out on the results. The NSGA-II algorithm was tested and verified on established multi-dimensional problems. Optimisation on the nacelle model provided 3-dimensional Pareto surfaces of optimal designs at both cruise and off-design conditions. In setting up this methodology several adaptations to the basic NSGA-II algorithm were tested including constraint handling, weighted objective functions and initial sample size. The influence of these operators is demonstrated in terms of the hyper volume of the determined Pareto set

An automated approach to nacelle parameterization using intuitive class shape transformation curves

A tool to create parametric aerodynamic shapes using intuitive design variables based on class shape transformation (CST) curves is presented. To enable this, a system has been developed which accepts arbitrary constraints and automatically derives the analytical expressions which describe the corresponding class shape transformation curves. Parametric geometry definitions for fan cowl and intake aero-lines were developed using the generalized method. Computational fluid dynamics (CFD) analysis of the fan cowl shows that despite the simple geometry definition, its performance characteristics are close to what would be expected of a finished design. The intake geometry was generated in a similar way and met the typical performance metrics for conventional intakes. This demonstrates the usefulness of the tool to quickly and robustly produce parametric aero-lines with good aerodynamic properties, using relatively simple intuitive design variables

Reproduction, Lactation, and Growth Studies with Swine

Animal Nutritio

Electronic immunization data collection systems: application of an evaluation framework

BACKGROUND: Evaluating the features and performance of health information systems can serve to strengthen the systems themselves as well as to guide other organizations in the process of designing and implementing surveillance tools. We adapted an evaluation framework in order to assess electronic immunization data collection systems, and applied it in two Ontario public health units. METHODS: The Centers for Disease Control and Prevention’s Guidelines for Evaluating Public Health Surveillance Systems are broad in nature and serve as an organizational tool to guide the development of comprehensive evaluation materials. Based on these Guidelines, and informed by other evaluation resources and input from stakeholders in the public health community, we applied an evaluation framework to two examples of immunization data collection and examined several system attributes: simplicity, flexibility, data quality, timeliness, and acceptability. Data collection approaches included key informant interviews, logic and completeness assessments, client surveys, and on-site observations. RESULTS: Both evaluated systems allow high-quality immunization data to be collected, analyzed, and applied in a rapid fashion. However, neither system is currently able to link to other providers’ immunization data or provincial data sources, limiting the comprehensiveness of coverage assessments. We recommended that both organizations explore possibilities for external data linkage and collaborate with other jurisdictions to promote a provincial immunization repository or data sharing platform. CONCLUSIONS: Electronic systems such as the ones described in this paper allow immunization data to be collected, analyzed, and applied in a rapid fashion, and represent the infostructure required to establish a population-based immunization registry, critical for comprehensively assessing vaccine coverage

The Spectral Image Processing System (SIPS): Software for integrated analysis of AVIRIS data

The Spectral Image Processing System (SIPS) is a software package developed by the Center for the Study of Earth from Space (CSES) at the University of Colorado, Boulder, in response to a perceived need to provide integrated tools for analysis of imaging spectrometer data both spectrally and spatially. SIPS was specifically designed to deal with data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and the High Resolution Imaging Spectrometer (HIRIS), but was tested with other datasets including the Geophysical and Environmental Research Imaging Spectrometer (GERIS), GEOSCAN images, and Landsat TM. SIPS was developed using the 'Interactive Data Language' (IDL). It takes advantage of high speed disk access and fast processors running under the UNIX operating system to provide rapid analysis of entire imaging spectrometer datasets. SIPS allows analysis of single or multiple imaging spectrometer data segments at full spatial and spectral resolution. It also allows visualization and interactive analysis of image cubes derived from quantitative analysis procedures such as absorption band characterization and spectral unmixing. SIPS consists of three modules: SIPS Utilities, SIPS_View, and SIPS Analysis. SIPS version 1.1 is described below

A cost comparison of electronic and hybrid data collection systems in Ontario during pandemic and seasonal influenza vaccination campaigns

<p>Abstract</p> <p>Background</p> <p>During the pandemic (H1N1) 2009 influenza vaccination campaign, health regions in Canada collected client-level immunization data using fully electronic or hybrid systems, with the latter comprising both electronic and paper-based elements. The objective of our evaluation was to compare projected five-year costs associated with implementing these systems in Ontario public health units (PHUs) during pandemic and seasonal influenza vaccination campaigns.</p> <p>Methods</p> <p>Six PHUs provided equipment and staffing costs during the pandemic (H1N1) 2009 influenza vaccination campaign and staffing algorithms for seasonal campaigns. We standardized resources to population sizes 100,000, 500,000 and 1,000,000, assuming equipment lifetime of five years and public health vaccine administration rates of 18% and 2.5% for H1N1 and seasonal campaigns, respectively. Two scenarios were considered: Year 1 pandemic and Year 1 seasonal campaigns, each followed by four regular influenza seasons. Costs were discounted at 5%.</p> <p>Results</p> <p>Assuming a Year 1 pandemic, the five-year costs per capita for the electronic system decrease as PHU population size increases, becoming increasingly less costly than hybrid systems ($4.33 vs.$4.34 [100,000], $4.17 vs.$4.34 [500,000], $4.12 vs.$4.34 [1,000, 000]). The same trend is observed for the scenario reflecting five seasonal campaigns, with the electronic system being less expensive per capita than the hybrid system for all population sizes ($1.93 vs.$1.95 [100,000], $1.91 vs.$1.94 [500,000], $1.87 vs.$1.94 [1,000, 000]). Sensitivity analyses identified factors related to nurse hours as affecting the direction and magnitude of the results.</p> <p>Conclusions</p> <p>Five-year cost projections for electronic systems were comparable or less expensive than for hybrid systems, at all PHU population sizes. An intangible benefit of the electronic system is having data rapidly available for reporting.</p

Minichromosome maintenance protein 6, a proliferation marker superior to Ki-67 and independent predictor of survival in patients with mantle cell lymphoma

Minichromosome maintenance protein 6 (MCM6) is one of six proteins of the MCM family which are involved in the initiation of DNA replication and thus represent a marker of proliferating cells. Since the level of cell proliferation is the most valuable predictor of survival in mantle cell lymphoma (MCL), we investigated lymph node biopsy specimens from 70 patients immunohistochemically with a monoclonal antibody against MCM6. The percentage of MCM6 expressing lymphoma cells ranged from 12.0 to 95.6%, with a mean of 61.0%, and was significantly higher than the percentage of Ki-67-positive cells (P<0.0001). Surprisingly, the ratio of MCM6-positive cells to Ki-67-positive cells was higher than in normal stimulated peripheral blood mononuclear cells, indicating a cell early G1-phase arrest in MCL. A high MCM6 expression level of more than 75% positive cells was associated with a significantly shorter overall survival time (16 months) compared to MCL with a low MCM6 expression level of less than 25% (no median reached, P<0.0001). Multivariate analysis revealed MCM6 to be an independent predictor of survival that is superior to the international prognostic factor and the Ki-67 index. Therefore, aside from gene expression profiling, immunohistochemical detection of MCM6 seems to be the most promising marker for predicting the outcome in MCL

Diversity-Oriented Synthesis Yields a Novel Lead for the Treatment of Malaria

Here, we describe the discovery of a novel antimalarial agent using phenotypic screening of Plasmodium falciparum asexual blood-stage parasites. Screening a novel compound collection created using diversity-oriented synthesis (DOS) led to the initial hit. Structure–activity relationships guided the synthesis of compounds having improved potency and water solubility, yielding a subnanomolar inhibitor of parasite asexual blood-stage growth. Optimized compound 27 has an excellent off-target activity profile in erythrocyte lysis and HepG2 assays and is stable in human plasma. This compound is available via the molecular libraries probe production centers network (MLPCN) and is designated ML238.Chemistry and Chemical Biolog

Target highlights in CASP9: Experimental target structures for the critical assessment of techniques for protein structure prediction

15 pags, 9 figsOne goal of the CASP community wide experiment on the critical assessment of techniques for protein structure prediction is to identify the current state of the art in protein structure prediction and modeling. A fundamental principle of CASP is blind prediction on a set of relevant protein targets, that is, the participating computational methods are tested on a common set of experimental target proteins, for which the experimental structures are not known at the time of modeling. Therefore, the CASP experiment would not have been possible without broad support of the experimental protein structural biology community. In this article, several experimental groups discuss the structures of the proteins which they provided as prediction targets for CASP9, highlighting structural and functional peculiarities of these structures: the long tail fiber protein gp37 from bacteriophage T4, the cyclic GMP-dependent protein kinase Iβ dimerization/docking domain, the ectodomain of the JTB (jumping translocation breakpoint) transmembrane receptor, Autotaxin in complex with an inhibitor, the DNA-binding J-binding protein 1 domain essential for biosynthesis and maintenance of DNA base-J (β-D-glucosyl-hydroxymethyluracil) in Trypanosoma and Leishmania, an so far uncharacterized 73 residue domain from Ruminococcus gnavus with a fold typical for PDZ-like domains, a domain from the phycobilisome core-membrane linker phycobiliprotein ApcE from Synechocystis, the heat shock protein 90 activators PFC0360w and PFC0270w from Plasmodium falciparum, and 2-oxo-3-deoxygalactonate kinase from Klebsiella pneumoniae. © 2011 Wiley-Liss, Inc.Grant sponsor: Spanish Ministry of Education and Science; Grant number: BFU2008-01588; Grant sponsor: European Commission; Grant number: NMP4-CT-2006-033256; Grant sponsor: Spanish Ministry of Education and Science (José Castillejo fellowship); Grant sponsor: Xunta de Galicia (Angeles Alvariño fellowship); Grant sponsor: National Institutes of Health; Grant numbers: K22-CA124517 (D.E.C.); R01-GM090161 (C.K.) GM074942; GM094585; Grant sponsor: U. S. Department of Energy, Office of Biological and Environmental Research; Grant number: DE-AC02-06CH11357 (to A.J.); Grant sponsor: Foundation for Polish Science (to K.M.); Grant sponsor: NSF; Grant number: DBI 0829586