Parsimoneous Modeling of Yield Curves for U.S. Treasury Bills

A new model is proposed for representinq the term to maturity structure of interest rates at a point in time.The model produces humped, monotonic and S-shaped yield curves using four parameters. Conditional on a time decay parameter, estimates of the other three are obtained by least squares. Yield curves for thirty-seven sets of U.S. Treasury bill yields with maturities up to one year are presented. The median standard deviation of fit is just over seven basis points and the corresponding median R-squared is .96. Study of residuals suggests the existence of specific maturity effects not previously identified. Using the models to predict the price of a long term bond provides a diagnostic check and suggests directions for further research.

Testing Portfolio Efficiency with Conditioning Information

We develop asset pricing models' implications for portfolio efficiency when there is conditioning information in the form of a set of lagged instruments. A model of expected returns identifies a portfolio that should be minimum variance efficient with respect to the conditioning information. Our tests refine previous tests of portfolio efficiency, using the conditioning information optimally. We reject the efficiency of all static or time-varying combinations of the three Fama-French (1996) factors with respect to the conditioning information and also the conditional efficiency of time-varying combinations of the factors, given standard lagged instruments.

Mimicking Portfolios with Conditioning Information

Mimicking portfolios have long been useful in asset pricing research. In most empirical applications, the portfolio weights are assumed to be fixed over time, while in theory they may be functions of the economic state. This paper derives and characterizes mimicking portfolios in the presence of predetermined state variables, or conditioning information. The results generalize and integrate multifactor minimum variance efficiency (Fama, 1996) with conditional and unconditional mean variance efficiency (Hansen and Richard (1987), Ferson and Siegel, 2001). Empirical examples illustrate the potential importance of time-varying mimicking portfolio weights and highlight challenges in their application.

Network motif analysis of a multi-mode genetic-interaction network

Statistical and computational methods for the extraction of biological information from dense multi-mode genetic-interaction networks were developed and implemented in open-source software

A Third Approach to Gene Prediction Suggests Thousands of Additional Human Transcribed Regions

The identification and characterization of the complete ensemble of genes is a main goal of deciphering the digital information stored in the human genome. Many algorithms for computational gene prediction have been described, ultimately derived from two basic concepts: (1) modeling gene structure and (2) recognizing sequence similarity. Successful hybrid methods combining these two concepts have also been developed. We present a third orthogonal approach to gene prediction, based on detecting the genomic signatures of transcription, accumulated over evolutionary time. We discuss four algorithms based on this third concept: Greens and CHOWDER, which quantify mutational strand biases caused by transcription-coupled DNA repair, and ROAST and PASTA, which are based on strand-specific selection against polyadenylation signals. We combined these algorithms into an integrated method called FEAST, which we used to predict the location and orientation of thousands of putative transcription units not overlapping known genes. Many of the newly predicted transcriptional units do not appear to code for proteins. The new algorithms are particularly apt at detecting genes with long introns and lacking sequence conservation. They therefore complement existing gene prediction methods and will help identify functional transcripts within many apparent “genomic deserts.

Differential expression of CD10 in prostate cancer and its clinical implication

BACKGROUND: CD10 is a transmembrane metallo-endopeptidase that cleaves and inactivates a variety of peptide growth factors. Loss of CD10 expression is a common, early event in human prostate cancer; however, CD10 positive cancer cells frequently appear in lymph node metastasis. We hypothesize that prostate tumors expressing high levels of CD10 have a more aggressive biology with an early propensity towards lymph node metastasis. METHODS: Eighty-seven patients, 53 with and 34 without pathologically organ confined prostate cancer at the time of radical prostatectomy (RP), were used for the study. Fourteen patients with lymph node metastasis found at the time of surgery were identified and included in this study. Serial sections from available frozen tumor specimens in OCT were processed for CD10 immunohistochemistry. Cancer glands were graded for the presence and intensity of CD10 staining, and overall percentage of glands staining positive was estimated. Clinical characteristics including pre- and post-operative PSA and Gleason score were obtained. A similar study as a control for the statistical analysis was performed with CD13 staining. For statistical analysis, strong staining was defined as > 20% positivity based on the observed maximum separation of the cumulative distributions. RESULTS: CD10 expression significantly correlated with Gleason grade, tumor stage, and with pre-operative serum PSA. Seventy percent of RP specimens from patients with node metastasis showed strong staining for CD10, compared to 30% in the entire cohort (OR = 3.4, 95% CI: 1.08–10.75, P = 0.019). Increased staining for CD10 was associated with PSA recurrence after RP. CD13 staining did not correlate significantly with any of these same clinical parameters. CONCLUSION: These results suggest that the expression of CD10 by prostate cancer corresponds to a more aggressive phenotype with a higher malignant potential, described histologically by the Gleason score. CD10 offers potential clinical utility for stratifying prostate cancer to predict biological behavior of the tumor

Detours and Paths: BRST Complexes and Worldline Formalism

We construct detour complexes from the BRST quantization of worldline diffeomorphism invariant systems. This yields a method to efficiently extract physical quantum field theories from particle models with first class constraint algebras. As an example, we show how to obtain the Maxwell detour complex by gauging N=2 supersymmetric quantum mechanics in curved space. Then we concentrate on first class algebras belonging to a class of recently introduced orthosymplectic quantum mechanical models and give generating functions for detour complexes describing higher spins of arbitrary symmetry types. The first quantized approach facilitates quantum calculations and we employ it to compute the number of physical degrees of freedom associated to the second quantized, field theoretical actions.Comment: 1+35 pages, 1 figure; typos corrected and references added, published versio

Mapping the stellar structure of the Milky Way thick disk and halo using SEGUE photometry

We map the stellar structure of the Galactic thick disk and halo by applying color-magnitude diagram (CMD) fitting to photometric data from the SEGUE survey, allowing, for the first time, a comprehensive analysis of their structure at both high and low latitudes using uniform SDSS photometry. Incorporating photometry of all relevant stars simultaneously, CMD fitting bypasses the need to choose single tracer populations. Using old stellar populations of differing metallicities as templates we obtain a sparse 3D map of the stellar mass distribution at |Z|>1 kpc. Fitting a smooth Milky Way model comprising exponential thin and thick disks and an axisymmetric power-law halo allows us to constrain the structural parameters of the thick disk and halo. The thick-disk scale height and length are well constrained at 0.75+-0.07 kpc and 4.1+-0.4 kpc, respectively. We find a stellar halo flattening within ~25 kpc of c/a=0.88+-0.03 and a power-law index of 2.75+-0.07 (for 7<R_{GC}<~30 kpc). The model fits yield thick-disk and stellar halo densities at the solar location of rho_{thick,sun}=10^{-2.3+-0.1} M_sun pc^{-3} and rho_{halo,sun}=10^{-4.20+-0.05} M_sun pc^{-3}, averaging over any substructures. Our analysis provides the first clear in situ evidence for a radial metallicity gradient in the Milky Way's stellar halo: within R<~15 kpc the stellar halo has a mean metallicity of [Fe/H]=-1.6, which shifts to [Fe/H]=-2.2 at larger radii. Subtraction of the best-fit smooth and symmetric model from the overall density maps reveals a wealth of substructures at all latitudes, some attributable to known streams and overdensities, and some new. A simple warp cannot account for the low latitude substructure, as overdensities occur simultaneously above and below the Galactic plane. (abridged)Comment: 13 pages, 10 figures, accepted for publication in Astrophysical Journa