A Toll for lupus

Toll-like receptor (TLR)-9 recognizes CpG motifs in microbial DNA. TLR9 signalling stimulates innate antimicrobial immunity and modulates adaptive immune responses including autoimmunity against chromatin, e.g., in systemic lupus erythematosus (SLE). This review summarizes the available data for a role of TLR9 signalling in lupus and discusses the following questions that arise from these observations: 1) Is CpG-DNA/TLR9 interaction involved in infection-induced disease activity of lupus? 2) What are the risks of CpG motifs in vaccine adjuvants for lupus patients? 3) Is TLR9 signalling involved in the pathogenesis of lupus by recognizing self DNA

A Riccati type PDE for light-front higher helicity vertices

This paper is based on a curious observation about an equation related to the tracelessness constraints of higher spin gauge fields. The equation also occurs in the theory of continuous spin representations of the Poincar\'e group. Expressed in an oscillator basis for the higher spin fields, the equation becomes a non-linear partial differential operator of the Riccati type acting on the vertex functions. The consequences of the equation for the cubic vertex is investigated in the light-front formulation of higher spin theory. The classical vertex is completely fixed but there is room for off-shell quantum corrections.Comment: 27 pages. Updated to published versio

Anyons on Higher Genus Surfaces - a Constructive Approach

We reconsider the problem of anyons on higher genus surfaces by embedding them in three dimensional space. From a concrete realization based on three dimensional flux tubes bound to charges moving on the surface, we explicitly derive all the representations of the spinning braid group. The component structure of the wave functions arises from winding the flux tubes around the handles. We also argue that the anyons in our construction must fulfil the generalized spin-statistics relation.Comment: 8 pages, LaTex, 2 figures available on request ([email protected]), USITP-93-1

Optimal Investment Horizons

In stochastic finance, one traditionally considers the return as a competitive measure of an asset, {\it i.e.}, the profit generated by that asset after some fixed time span $\Delta t$, say one week or one year. This measures how well (or how bad) the asset performs over that given period of time. It has been established that the distribution of returns exhibits ``fat tails'' indicating that large returns occur more frequently than what is expected from standard Gaussian stochastic processes (Mandelbrot-1967,Stanley1,Doyne). Instead of estimating this ``fat tail'' distribution of returns, we propose here an alternative approach, which is outlined by addressing the following question: What is the smallest time interval needed for an asset to cross a fixed return level of say 10%? For a particular asset, we refer to this time as the {\it investment horizon} and the corresponding distribution as the {\it investment horizon distribution}. This latter distribution complements that of returns and provides new and possibly crucial information for portfolio design and risk-management, as well as for pricing of more exotic options. By considering historical financial data, exemplified by the Dow Jones Industrial Average, we obtain a novel set of probability distributions for the investment horizons which can be used to estimate the optimal investment horizon for a stock or a future contract.Comment: Latex, 5 pages including 4 figur