559 research outputs found
Unified covariant treatment of hyperfine splitting for heavy and light mesons
This paper aims at proving the fundamental role of a relativistic formulation
for quarkonia models.
We present a completely covariant description of a two-quark system
interacting by the Cornell potential with a Breit term describing the hyperfine
splitting. Using an appropriate procedure to calculate the Breit correction, we
find heavy meson masses in excellent agreement with experimental data.
Moreover, also when applied to light quarks and even taking average values of
the running coupling constant, we prove that covariance properties and
hyperfine splitting are sufficient to explain the light mesons spectrum and to
give a very good agreement with the data.Comment: 4 page
Exponential mapping for non semisimple quantum groups
The concept of universal T matrix, recently introduced by Fronsdal and
Galindo in the framework of quantum groups, is here discussed as a
generalization of the exponential mapping. New examples related to
inhomogeneous quantum groups of physical interest are developed, the duality
calculations are explicitly presented and it is found that in some cases the
universal T matrix, like for Lie groups, is expressed in terms of usual
exponential series.Comment: 12 page
Patterning molecular scale paramagnets at Au Surface: A root to Magneto-Molecular-Electronics
Few examples of the exploitation of molecular magnetic properties in
molecular electronics are known to date. Here we propose the realization of
Self assembled monolayers (SAM) of a particular stable organic radical. This
radical is meant to be used as a standard molecule on which to prove the
validity of a single spin reading procedure known as ESR-STM. We also discuss a
range of possible applications, further than ESR-STM, of magnetic monolayers of
simple purely organic magnetic molecule.Comment: This preprint is currently partially under revisio
A data review and re-assessment of ovarian cancer serum proteomic profiling
BACKGROUND: The early detection of ovarian cancer has the potential to dramatically reduce mortality. Recently, the use of mass spectrometry to develop profiles of patient serum proteins, combined with advanced data mining algorithms has been reported as a promising method to achieve this goal. In this report, we analyze the Ovarian Dataset 8-7-02 downloaded from the Clinical Proteomics Program Databank website, using nonparametric statistics and stepwise discriminant analysis to develop rules to diagnose patients, as well as to understand general patterns in the data that may guide future research. RESULTS: The mass spectrometry serum profiles derived from cancer and controls exhibited numerous statistical differences. For example, use of the Wilcoxon test in comparing the intensity at each of the 15,154 mass to charge (M/Z) values between the cancer and controls, resulted in the detection of 3,591 M/Z values whose intensities differed by a p-value of 10(-6 )or less. The region containing the M/Z values of greatest statistical difference between cancer and controls occurred at M/Z values less than 500. For example the M/Z values of 2.7921478 and 245.53704 could be used to significantly separate the cancer from control groups. Three other sets of M/Z values were developed using a training set that could distinguish between cancer and control subjects in a test set with 100% sensitivity and specificity. CONCLUSION: The ability to discriminate between cancer and control subjects based on the M/Z values of 2.7921478 and 245.53704 reveals the existence of a significant non-biologic experimental bias between these two groups. This bias may invalidate attempts to use this dataset to find patterns of reproducible diagnostic value. To minimize false discovery, results using mass spectrometry and data mining algorithms should be carefully reviewed and benchmarked with routine statistical methods
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