Feasibility of predicting performance degradation of airfoils in heavy rain

The heavy rain aerodynamic performance penalty program is detailed. This effort supported the design of a fullscale test program as well as examined the feasibility of estimating the degradation of performance of airfoils from first principles. The analytic efforts were supplemented by a droplet splashback test program in an attempt to observe the physics of impact and generation of ejecta. These tests demonstrated that the interaction of rain with an airfoil is a highly complex phenomenon and this interaction is not likely to be analyzed analytically with existing tools

Testing and evaluation of a stall-flutter-suppression system for helicopter rotors using individual-blade-control

The development and testing of a feedback system designed to alleviate the violent blade first torsion mode oscillations associated with stall flutter are described. The system, based on previously developed M.I.T. Individual-Blade-Control hardware, employs blade-mounted accelerometers to sense torsional oscillations and feeds back rate informaton to increase the damping of the first torsion mode. A linear model of the blade and control system dynamics is developed and is used to give qualitative and quantitative guidance in the design process as well as to aid in analysis of experimental results. System performance in wind tunnel tests, both in hover and forward flight, is described, and evidence is given of the system's ability to provide substantial additional damping to stall-induced blade oscillations

Performance optimization for rotors in hover and axial flight

Performance optimization for rotors in hover and axial flight is a topic of continuing importance to rotorcraft designers. The aim of this Phase 1 effort has been to demonstrate that a linear optimization algorithm could be coupled to an existing influence coefficient hover performance code. This code, dubbed EHPIC (Evaluation of Hover Performance using Influence Coefficients), uses a quasi-linear wake relaxation to solve for the rotor performance. The coupling was accomplished by expanding of the matrix of linearized influence coefficients in EHPIC to accommodate design variables and deriving new coefficients for linearized equations governing perturbations in power and thrust. These coefficients formed the input to a linear optimization analysis, which used the flow tangency conditions on the blade and in the wake to impose equality constraints on the expanded system of equations; user-specified inequality contraints were also employed to bound the changes in the design. It was found that this locally linearized analysis could be invoked to predict a design change that would produce a reduction in the power required by the rotor at constant thrust. Thus, an efficient search for improved versions of the baseline design can be carried out while retaining the accuracy inherent in a free wake/lifting surface performance analysis

Reconstructing a Z' Lagrangian using the LHC and low-energy data

We study the potential of the LHC and future low-energy experiments to precisely measure the underlying model parameters of a new Z' boson. We emphasize the complimentary information obtained from both on- and off-peak LHC dilepton data, from the future Q-weak measurement of the weak charge of the proton, and from a proposed measurement of parity violation in low-energy Moller scattering. We demonstrate the importance of off-peak LHC data and Q-weak for removing sign degeneracies between Z' couplings that occur if only on-peak LHC data is studied. A future precision measurement of low-energy Moller scattering can resolve a scaling degeneracy between quark and lepton couplings that remains after analyzing LHC dilepton data, permitting an extraction of the individual Z' couplings rather than combinations of them. We study how precisely Z' properties can be extracted for LHC integrated luminosities ranging from a few inverse femtobarns to super-LHC values of an inverse attobarn. For the several example cases studied with M_Z'=1.5 TeV, we find that coupling combinations can be determined with relative uncertainties reaching 30% with 30 fb^-1 of integrated luminosity, while 50% is possible with 10 fb^-1. With SLHC luminosities of 1 ab^-1, we find that products of quark and lepton couplings can be probed to 10%.Comment: 36 pages, 17 figure

Interpreting microarray experiments via co-expressed gene groups analysis

International audienceMicroarray technology produces vast amounts of data by measuring simultaneously the expression levels of thousands of genes under hundreds of biological conditions. Nowadays, one of the principal challenges in bioinformatics is the interpretation of huge data using different sources of information. We propose a novel data analysis method named CGGA (Co-expressed Gene Groups Analysis) that automatically finds groups of genes that are functionally enriched, i.e. have the same functional annotations, and are co- expressed. CGGA automatically integrates the information of microarrays, i.e. gene expression profiles, with the functional annotations of the genes obtained by the genome-wide information sources such as Gene Ontology (GO)1. By applying CGGA to well-known microarray experiments, we have identified the principal functionally enriched and co-expressed gene groups, and we have shown that this approach enhances and accelerates the interpretation of DNA microarray experiments

A Novel Endothelial L-Selectin Ligand Activity in Lymph Node Medulla That Is Regulated by α(1,3)-Fucosyltransferase-IV

Lymphocytes home to peripheral lymph nodes (PLNs) via high endothelial venules (HEVs) in the subcortex and incrementally larger collecting venules in the medulla. HEVs express ligands for L-selectin, which mediates lymphocyte rolling. L-selectin counterreceptors in HEVs are recognized by mAb MECA-79, a surrogate marker for molecularly heterogeneous glycans termed peripheral node addressin. By contrast, we find that medullary venules express L-selectin ligands not recognized by MECA-79. Both L-selectin ligands must be fucosylated by α(1,3)-fucosyltransferase (FucT)-IV or FucT-VII as rolling is absent in FucT-IV+VII−/− mice. Intravital microscopy experiments revealed that MECA-79–reactive ligands depend primarily on FucT-VII, whereas MECA-79–independent medullary L-selectin ligands are regulated by FucT-IV. Expression levels of both enzymes paralleled these anatomical distinctions. The relative mRNA level of FucT-IV was higher in medullary venules than in HEVs, whereas FucT-VII was most prominent in HEVs and weak in medullary venules. Thus, two distinct L-selectin ligands are segmentally confined to contiguous microvascular domains in PLNs. Although MECA-79–reactive species predominate in HEVs, medullary venules express another ligand that is spatially, antigenically, and biosynthetically unique. Physiologic relevance for this novel activity in medullary microvessels is suggested by the finding that L-selectin–dependent T cell homing to PLNs was partly insensitive to MECA-79 inhibition

Hierarchical information clustering by means of topologically embedded graphs

We introduce a graph-theoretic approach to extract clusters and hierarchies in complex data-sets in an unsupervised and deterministic manner, without the use of any prior information. This is achieved by building topologically embedded networks containing the subset of most significant links and analyzing the network structure. For a planar embedding, this method provides both the intra-cluster hierarchy, which describes the way clusters are composed, and the inter-cluster hierarchy which describes how clusters gather together. We discuss performance, robustness and reliability of this method by first investigating several artificial data-sets, finding that it can outperform significantly other established approaches. Then we show that our method can successfully differentiate meaningful clusters and hierarchies in a variety of real data-sets. In particular, we find that the application to gene expression patterns of lymphoma samples uncovers biologically significant groups of genes which play key-roles in diagnosis, prognosis and treatment of some of the most relevant human lymphoid malignancies.Comment: 33 Pages, 18 Figures, 5 Table

Systems biology driven software design for the research enterprise

<p>Abstract</p> <p>Background</p> <p>In systems biology, and many other areas of research, there is a need for the interoperability of tools and data sources that were not originally designed to be integrated. Due to the interdisciplinary nature of systems biology, and its association with high throughput experimental platforms, there is an additional need to continually integrate new technologies. As scientists work in isolated groups, integration with other groups is rarely a consideration when building the required software tools.</p> <p>Results</p> <p>We illustrate an approach, through the discussion of a purpose built software architecture, which allows disparate groups to reuse tools and access data sources in a common manner. The architecture allows for: the rapid development of distributed applications; interoperability, so it can be used by a wide variety of developers and computational biologists; development using standard tools, so that it is easy to maintain and does not require a large development effort; extensibility, so that new technologies and data types can be incorporated; and non intrusive development, insofar as researchers need not to adhere to a pre-existing object model.</p> <p>Conclusion</p> <p>By using a relatively simple integration strategy, based upon a common identity system and dynamically discovered interoperable services, a light-weight software architecture can become the focal point through which scientists can both get access to and analyse the plethora of experimentally derived data.</p

Evaluation of clustering algorithms for gene expression data

BACKGROUND: Cluster analysis is an integral part of high dimensional data analysis. In the context of large scale gene expression data, a filtered set of genes are grouped together according to their expression profiles using one of numerous clustering algorithms that exist in the statistics and machine learning literature. A closely related problem is that of selecting a clustering algorithm that is "optimal" in some sense from a rather impressive list of clustering algorithms that currently exist. RESULTS: In this paper, we propose two validation measures each with two parts: one measuring the statistical consistency (stability) of the clusters produced and the other representing their biological functional congruence. Smaller values of these indices indicate better performance for a clustering algorithm. We illustrate this approach using two case studies with publicly available gene expression data sets: one involving a SAGE data of breast cancer patients and the other involving a time course cDNA microarray data on yeast. Six well known clustering algorithms UPGMA, K-Means, Diana, Fanny, Model-Based and SOM were evaluated. CONCLUSION: No single clustering algorithm may be best suited for clustering genes into functional groups via expression profiles for all data sets. The validation measures introduced in this paper can aid in the selection of an optimal algorithm, for a given data set, from a collection of available clustering algorithms