Four-Dimensional Planck Scale is Not Universal in Fifth Dimension in Randall-Sundrum Scenario

It has recently been proposed that the hierarchy problem can be solved by considering the warped fifth dimension compactified on $S^{1}/Z_{2}$. Many studies in the context have assumed a particular choice for an integration constant $\sigma_{0}$ that appears when one solves the five-dimensional Einstein equation. Since $\sigma_{0}$ is not determined by the boundary condition of the five-dimensional theory, $\sigma_{0}$ may be regarded as a gauge degree of freedom in a sense. To this time, all indications are that the four-dimensional Planck mass depends on $\sigma_{0}$. In this paper, we carefully investigate the properties of the geometry in the Randall-Sundrum model, and consider in which location $y$ the four-dimensional Planck mass is measured. As a result, we find a $\sigma_{0}$-independent relation between the four-dimensional Planck mass $M_{\rm Pl}$ and five- dimensional fundamental mass scale $M$, and remarkably enough, we can take $M$ to TeV region when we consider models with the Standard Model confined on a distant brane. We also confirm that the physical masses on the distant brane do not depend on $\sigma_{0}$ by considering a bulk scalar field as an illustrative example. The resulting mass scale of the Kaluza-Klein modes is on the order of $M$.Comment: Latex, 12 page

Multiple Ontologies for Integrating Complex Phenotype Datasets

There has been an emergence of multiple large scale phenotyping projects in the rat model organism community as well as renewed interest in the ongoing phenotype data generated by thousands of researchers using hundreds of rat strains worldwide. Unfortunately, this data is scattered and is neither described nor formatted in a standardized manner. A system to integrate complex phenotype data from multiple sources and facilitate data mining and analysis is being developed using multiple ontologies.

*Introduction*
The potential value of integrating phenotype data from multiple sources (different laboratories, varying techniques to measure similar phenotypes, multiple strains) is enormous. Presented here is a data integration system for complex phenotype data from both large-scale and individual experiments and the taxonomy and ontologies that provide the backbone of this format. RGD along with Mouse Genome Informatics (MGI) (Blake et al, 2009) and the Animal QTL Database (Hu and Reecy, 2007) is developing a Vertebrate Trait Ontology to represent morphological states and physiological processes to be used to annotate quantitative trait loci (QTL) and other data. RGD has also used the Mammalian Phenotype Ontology (Smith et al, 2005) for several years to indicate the relationship of genomic elements to abnormal phenotypes. The Vertebrate Trait Ontology represents what is being assessed, and the Mammalian Phenotype Ontology represents the conclusion that was made. The system presented here represents what was done to measure the trait in order to reach the conclusion. Because of the close relationship among these ontologies, care is being taken to ensure compatibility and similarity in structure using the phenotype properties in the Phenotypic Quality Ontology (PATO) for guidance. ("http://www.bioontology.org/wiki/index.php/PATO:Main_Page":http://www.bioontology.org/wiki/index.php/PATO:Main_Page) 

*Data Format and Ontologies*
Standardization of data types and relationships used to define the phenotype experiment and resulting data, and the ontologies to be used to standardize descriptive fields are being developed. For phenotype data, the major informational components include Researcher, Study, Experiment, Sample, Experimental Conditions and Clinical Measurement. A Rat Strain Taxonomy has been developed to standardize this information and provide the relationships among strains to allow investigators to retrieve and analyze phenotype data for strains that are related genetically. Two important aspects of a phenotype measurement include 1) what was measured and 2) how it was measured. The Clinical Measurement Ontology and the Measurement Method Ontology are being developed to standardize this information. In addition an Experimental Conditions ontology is under construction to allow integration of data measured under various conditions.

*Pilot Study Results*
Cardiovascular and biochemistry phenotype data from two major datasets have been integrated using the Rat Strain Taxonomy and the three phenotype related ontologies. A prototype data mining tool ("http://rgd.mcw.edu/rgdweb/":http://rgd.mcw.edu/rgdweb/) has also been developed that provides the user with options to begin a search with strains or any of the ontologies and make subsequent filter choices from the other ontologies. Choices presented to the user are restricted to those for which data is available and query tracking functions are provided to alert the user to the number of results being returned and the query choices made.

*References*
Blake JA, Bult CJ, Eppig JT, Kadin JA, Richardson JE; Mouse Genome Database Group, 2009 _Nucleic Acids Res_. Jan;37:D712-9.

HuZL, Reecy JM, Animal QTLdb: beyond a repository. A public platform for QTL comparisons and integration with diverse types of structural genomic information, 2007, _Mamm Genome_, Jan;18(1):1-4.

Smith CL, Goldsmith CA, Eppig JT. The Mammalian Phenotype Ontology as a tool for annotating, analyzing and comparing phenotypic information, _Genome Biol_. 2005 6(1):R7.
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Manipulators with flexible links: A simple model and experiments

A simple dynamic model proposed for flexible links is briefly reviewed and experimental control results are presented for different flexible systems. A simple dynamic model is useful for rapid prototyping of manipulators and their control systems, for possible application to manipulator design decisions, and for real time computation as might be applied in model based or feedforward control. Such a model is proposed, with the further advantage that clear physical arguments and explanations can be associated with its simplifying features and with its resulting analytical properties. The model is mathematically equivalent to Rayleigh's method. Taking the example of planar bending, the approach originates in its choice of two amplitude variables, typically chosen as the link end rotations referenced to the chord (or the tangent) motion of the link. This particular choice is key in establishing the advantageous features of the model, and it was used to support the series of experiments reported