Taxol: A complex diterpenoid natural product with an evolutionarily obscure origin

Taxol, a diterpenoid natural product first isolated from Taxus brevifolia, is one of today’s better known anticancer drugs. Despite its clinical efficacy, the difficulty of establishing a secure and cost-effectivesupply of taxol has limited its use. However, its unique mode of action and efficacy against multiple forms of cancer has ensured continual efforts to achieve total and semisynthesis, as well as biotechnological production methods. Total synthesis is now possible but inefficient, so the production of taxol and related taxoids remains completely dependent on biomass derived from Taxus sp, with cell suspensions and collected plant materials as sources. The key to improving the supply of taxol and other clinically useful taxoids is the detailed elucidation of the taxoid biosynthesis pathway, which has been the subject of intense research. Many genes and enzymes in the Taxus pathway for taxoidbiosynthesis have now been identified, although gaps remain. In addition to Taxus sp, taxoids are also synthesized by various endophytic fungi, which often live in association with Taxus trees, thus raising questions about the evolutionary origin of this complex diterpenoid pathway. In the future, it may be possible to improve taxoid synthesis through the genetic modification of Taxus cell cultures, byculturing endophytic fungi or by transferring the entire pathway into a heterologous expression host, such as Saccharomyces cerevisiae

Two-dimensional shear modulus of a Langmuir foam

We deform a two-dimensional (2D) foam, created in a Langmuir monolayer, by applying a mechanical perturbation, and simultaneously image it by Brewster angle microscopy. We determine the foam stress tensor (through a determination of the 2D gas-liquid line tension, 2.35 $\pm$ 0.4 pJ$\cdot$m$^{-1}$) and the statistical strain tensor, by analyzing the images of the deformed structure. We deduce the 2D shear modulus of the foam, $\mu= 38 \pm 3 \mathrm{nN}\cdot \mathrm{m}^{-1}$. The foam effective rigidity is predicted to be $35 \pm 3 \mathrm {nN}\cdot \mathrm {m}^{- 1}$, which agrees with the value $37.5 \pm 0.8 \mathrm {nN}\cdot \mathrm {m}^{-1}$ obtained in an independent mechanical measurement.Comment: submitted May 12, 2003 ; resubmitted Sept 9, 200

MACAT—microarray chromosome analysis tool

By linking differential gene expression to the chromosomal localization of genes, one can investigate microarray data for characteristic patterns of expression phenomena involving sizeable parts of specific chromosomes. We have implemented a statistical approach for identifying significantly differentially expressed chromosome regions. We demonstrate the applicability of the approach on a publicly available data set on acute lymphocytic leukemia

Dispersive estimates for Schr\"odinger operators with point interactions in R3\mathbb{R}^3

The study of dispersive properties of Schr\"odinger operators with point interactions is a fundamental tool for understanding the behavior of many body quantum systems interacting with very short range potential, whose dynamics can be approximated by non linear Schr\"odinger equations with singular interactions. In this work we proved that, in the case of one point interaction in $\mathbb{R}^3$, the perturbed Laplacian satisfies the same $L^p-L^q$ estimates of the free Laplacian in the smaller regime $q\in[2,3)$. These estimates are implied by a recent result concerning the $L^p$ boundedness of the wave operators for the perturbed Laplacian. Our approach, however, is more direct and relatively simple, and could potentially be useful to prove optimal weighted estimates also in the regime $q\geq 3$.Comment: To appear on: "Advances in Quantum Mechanics: Contemporary Trends and Open Problems", G. Dell'Antonio and A. Michelangeli eds., Springer-INdAM series 201

Supervised Learning Algorithms to Extract Market Sentiment: An Application in the UK Commercial Real Estate Market

Sentiment analysis has become a key area of research in economics and finance with methods evolving from traditional survey-based analysis to computational linguistic techniques. New developments in data handling and analysis have allowed extracting sentiment from vast amounts of written documents. However, these methods depend heavily on the existence of training and test data sets. The choice of training data is critical in such applications. We show a novel application from a unique market – commercial real estate. There are several unique attributes of the real estate market that makes such analysis critical for insightful market intelligence. In the absence of training data sets for the UK commercial real estate (CRE) market, we propose the use of Amazon book reviews for real estate related products. Our analysis has shown, that the use of more than 200,000 book reviews, can train different supervised learning algorithms, which in turn, can capture the sentiment and more importantly, it can help predict the direct commercial real estate market trends