Generation of a vacuum ultraviolet to visible Raman frequency comb in H2-filled kagomé photonic crystal fiber.

We report on the generation of a purely vibrational Raman comb, extending from the vacuum ultraviolet (184 nm) to the visible (478 nm), in hydrogen-filled kagomé-style photonic crystal fiber pumped at 266 nm. Stimulated Raman scattering and molecular modulation processes are enhanced by higher Raman gain in the ultraviolet. Owing to the pressure-tunable normal dispersion landscape of the "fiber + gas" system in the ultraviolet, higher-order anti-Stokes bands are generated preferentially in higher-order fiber modes. The results pave the way toward tunable fiber-based sources of deep and vacuum ultraviolet light for applications in, e.g., spectroscopy and biomedicine.

Dramatic Raman Gain Suppression in the Vicinity of the Zero Dispersion Point in a Gas-Filled Hollow-Core Photonic Crystal Fiber.

In 1964 Bloembergen and Shen predicted that Raman gain could be suppressed if the rates of phonon creation and annihilation (by inelastic scattering) exactly balance. This is only possible if the momentum required for each process is identical, i.e., phonon coherence waves created by pump-to-Stokes scattering are identical to those annihilated in pump-to-anti-Stokes scattering. In bulk gas cells, this can only be achieved over limited interaction lengths at an oblique angle to the pump axis. Here we report a simple system that provides dramatic Raman gain suppression over long collinear path lengths in hydrogen. It consists of a gas-filled hollow-core photonic crystal fiber whose zero dispersion point is pressure adjusted to lie close to the pump laser wavelength. At a certain precise pressure, stimulated generation of Stokes light in the fundamental mode is completely suppressed, allowing other much weaker phenomena such as spontaneous Raman scattering to be explored at high pump powers.

Supercontinuum up-conversion via molecular modulation in gas-filled hollow-core PCF.

We report on the efficient, tunable, and selective frequency up-conversion of a supercontinuum spectrum via molecular modulation in a hydrogen-filled hollow-core photonic crystal fiber. The vibrational Q(1) Raman transition of hydrogen is excited in the fiber by a pump pre-pulse, enabling the excitation of a synchronous, collective oscillation of the molecules. This coherence wave is then used to up-shift the frequency of an arbitrarily weak, delayed probe pulse. Perfect phase-matching for this process is achieved by using higher order fiber modes and adjusting the pressure of the filling gas. Conversion efficiencies of ~50% are obtained within a tuning range of 25 THz.

Arrayed free space continuous-wave terahertz photomixers.

We present free space coherent arrays of continuous-wave terahertz (THz) photomixers and compare the results to on-chip arrays. By altering the relative phases of the exciting laser signals, the relative THz phase between the array elements can be tuned, allowing for beam steering. In addition, the constructive interference of the emission of N elements leads to an increase of the focal intensity by a factor of N2 while reducing the beam width by ∼N(-1), below the diffraction limit of a single source. Such array architectures strongly improve the THz power distribution for stand-off spectroscopy and imaging systems while providing a huge bandwidth at the same time. We demonstrate this by beam profiles generated by a 2×2 and a 4×1 array for a transmission distance of 4.2 m. Spectra between 70 GHz and 1.1 THz have been recorded with these arrays.

Gene expression analysis of precision-cut human liver slices indicates stable expression of ADME-Tox related genes.

In the process of drug development it is of high importance to test the safety of new drugs with predictive value for human toxicity. A promising approach of toxicity testing is based on shifts in gene expression profiling of the liver. Toxicity screening based on animal liver cells cannot be directly extrapolated to humans due to species differences. The aim of this study was to evaluate precision-cut human liver slices as in vitro method for the prediction of human specific toxicity by toxicogenomics. The liver slices contain all cell types of the liver in their natural architecture. This is important since drug-induced toxicity often is a multi-cellular process. Previously we showed that toxicogenomic analysis of rat liver slices is highly predictive for rat in vivo toxicity. In this study we investigated the levels of gene expression during incubation up to 24 h with Affymetrix microarray technology. The analysis was focused on a broad spectrum of genes related to stress and toxicity, and on genes encoding for phase-I, -II and -III metabolizing enzymes and transporters. Observed changes in gene expression were associated with cytoskeleton remodeling, extracellular matrix and cell adhesion, but for the ADME-Tox related genes only minor changes were observed. PCA analysis showed that changes in gene expression were not associated with age, sex or source of the human livers. Slices treated with acetaminophen showed patterns of gene expression related to its toxicity. These results indicate that precision-cut human liver slices are relatively stable during 24h of incubation and represent a valuable model for human in vitro hepatotoxicity testing despite the human inter-individual variability.

Interpretation of ANOVA models for microarray data using PCA.

MOTIVATION: ANOVA is a technique, which is frequently used in the analysis of microarray data, e.g. to assess the significance of treatment effects, and to select interesting genes based on P-values. However, it does not give information about what exactly is causing the effect. Our purpose is to improve the interpretation of the results from ANOVA on large microarray datasets, by applying PCA on the individual variance components. Interaction effects can be visualized by biplots, showing genes and variables in one plot, providing insight in the effect of e.g. treatment or time on gene expression. Because ANOVA has removed uninteresting sources of variance, the results are much more interpretable than without ANOVA. Moreover, the combination of ANOVA and PCA provides a simple way to select genes, based on the interactions of interest. RESULTS: It is shown that the components from an ANOVA model can be summarized and visualized with PCA, which improves the interpretability of the models. The method is applied to a real time-course gene expression dataset of mesenchymal stem cells. The dataset was designed to investigate the effect of different treatments on osteogenesis. The biplots generated with the algorithm give specific information about the effects of specific treatments on genes over time. These results are in agreement with the literature. The biological validation with GO annotation from the genes present in the selections shows that biologically relevant groups of genes are selected. AVAILABILITY: R code with the implementation of the method for this dataset is available from http://www.cac.science.ru.nl under the heading "Software".

Decision support systems for chemical structure representation, reaction modeling, and spectra simulation.

The choice of an appropriate structure coding scheme is the secret to success in QSAR studies. Depending on the problem at hand, 2D or 3D descriptors have to be chosen; the consideration of electronic effects might be crucial, conformational flexibility has to be of special concern. Artificial neural networks, both with unsupervised and with supervised learning schemes, are powerful tools for establishing relationships between structure and physical, chemical, or biological properties. The EROS system for the simulation of chemical reactions is briefly presented and its application to the degradation of s-triazine herbicides is shown. It is further shown how the simulation of chemical reactions can be combined with the simulation of infrared spectra for the efficient identification of the structure of degradation products.

Microarray analysis in rat liver slices correctly predicts in vivo hepatotoxicity.

The microarray technology, developed for the simultaneous analysis of a large number of genes, may be useful for the detection of toxicity in an early stage of the development of new drugs. The effect of different hepatotoxins was analyzed at the gene expression level in the rat liver both in vivo and in vitro. As in vitro model system the precision-cut liver slice model was used, in which all liver cell types are present in their natural architecture. This is important since drug-induced toxicity often is a multi-cellular process involving not only hepatocytes but also other cell types such as Kupffer and stellate cells. As model toxic compounds lipopolysaccharide (LPS, inducing inflammation), paracetamol (necrosis), carbon tetrachloride (CCl(4), fibrosis and necrosis) and gliotoxin (apoptosis) were used. The aim of this study was to validate the rat liver slice system as in vitro model system for drug-induced toxicity studies. The results of the microarray studies show that the in vitro profiles of gene expression cluster per compound and incubation time, and when analyzed in a commercial gene expression database, can predict the toxicity and pathology observed in vivo. Each toxic compound induces a specific pattern of gene expression changes. In addition, some common genes were up- or down-regulated with all toxic compounds. These data show that the rat liver slice system can be an appropriate tool for the prediction of multi-cellular liver toxicity. The same experiments and analyses are currently performed for the prediction of human specific toxicity using human liver slices.