The ArgoNeuT detector in the NuMI low-energy beam line at Fermilab

The ArgoNeuT liquid argon time projection chamber has collected thousands of neutrino and anti-neutrino events during an extended run period in the NuMI beam-line at Fermilab. This paper focuses on the main aspects of the detector layout and related technical features, including the cryogenic equipment, time projection chamber, read-out electronics, and off-line data treatment. The detector commissioning phase, physics run, and first neutrino event displays are also reported. The characterization of the main working parameters of the detector during data-taking, the ionization electron drift velocity and lifetime in liquid argon, as obtained from through-going muon data complete the present report.

Urinary metabolite profiling reveals CYP1A2-mediated metabolism of NSC686288 (aminoflavone)

NSC686288 [aminoflavone (AF)], a candidate chemotherapeutic agent, possesses a unique antiproliferative profile against tumor cells. Metabolic bioactivation of AF by drug-metabolizing enzymes, especially CYP1A monooxygenases, has been implicated as an underlying mechanism for its selective cytotoxicity in several cell culture-based studies. However, in vivo metabolism of AF has not been investigated in detail. In this study, the structural identities of 13 AF metabolites (12 of which are novel) in mouse urine or from microsomal incubations, including three monohydroxy-AFs, two dihydroxy-AFs and their sulfate and glucuronide conjugates, as well as one N-glucuronide, were determined by accurate mass measurements and liquid chromatography-tandem mass spectrometry fragmentation patterns, and a comprehensive map of the AF metabolic pathways was constructed. Significant differences between wild-type and Cyp1a2-null mice, within the relative composition of urinary metabolites of AF, demonstrated that CYP1A2-mediated regioselective oxidation was a major contributor to the metabolism of AF. Comparisons between wild-type and CYP1A2-humanized mice further revealed interspecies differences in CYP1A2-mediated catalytic activity. Incubation of AF with liver microsomes from all three mouse lines and with pooled human liver microsomes confirmed the observations from urinary metabolite profiling. Results from enzyme kinetic analysis further indicated that in addition to CYP1A P450s, CYP2C P450s may also play some role in the metabolism of AF.

Gene expression profiling reveals consistent differences between clinical samples of human leukaemias and their model cell lines

Microarray gene expression profiles of fresh clinical samples of chronic myeloid leukaemia in chronic phase, acute promyelocytic leukaemia and acute monocytic leukaemia were compared with profiles from cell lines representing the corresponding types of leukaemia (K562, NB4, HL60). In a hierarchical clustering analysis, all clinical samples clustered separately from the cell lines, regardless of leukaemic subtype. Gene ontology analysis showed that cell lines chiefly overexpressed genes related to macromolecular metabolism, whereas in clinical samples genes related to the immune response were abundantly expressed. These findings must be taken into consideration when conclusions from cell line-based studies are extrapolated to patients.

Dental CT imaging as a screening tool for dental profiling: advantages and limitations

The use of dental processing software for computed tomography (CT) data (Dentascan) is described on postmortem (pm) CT data for the purpose of pm identification. The software allows reconstructing reformatted images comparable to conventional panoramic dental radiographs by defining a curved reconstruction line along the teeth on oblique images. Three corpses that have been scanned within the virtopsy project were used to test the software for the purpose of dental identification. In every case, dental panoramic images could be reconstructed and compared to antemortem radiographs. The images showed the basic component of teeth (enamel, dentin, and pulp), the anatomic structure of the alveolar bone, missing or unerupted teeth as well as restorations of the teeth that could be used for identification. When streak artifacts due to metal-containing dental work reduced image quality, it was still necessary to perform pm conventional radiographs for comparison of the detailed shape of the restoration. Dental identification or a dental profiling seems to become possible in a noninvasive manner using the Dentascan software.

A Data-Analytic Strategy for Protein-Biomarker Discovery: Profiling of High-Dimensional Proteomic Data for Cancer Detection

With recent advances in mass spectrometry techniques, it is now possible to investigate proteins over a wide range of molecular weights in small biological specimens. This advance has generated data-analytic challenges in proteomics, similar to those created by microarray technologies in genetics, namely, discovery of "signature" protein profiles specific to each pathologic state (e.g., normal vs. cancer) or differential profiles between experimental conditions (e.g., treated by a drug of interest vs. untreated) from high-dimensional data. We propose a data analytic strategy for discovering protein biomarkers based on such high-dimensional mass-spectrometry data. A real biomarker-discovery project on prostate cancer is taken as a concrete example throughout the paper: the project aims to identify proteins in serum that distinguish cancer, benign hyperplasia, and normal states of prostate using the Surface Enhanced Laser Desorption/Ionization (SELDI) technology, a recently developed mass spectrometry technique. Our data analytic strategy takes properties of the SELDI mass-spectrometer into account: the SELDI output of a specimen contains about 48,000 (x, y) points where x is the protein mass divided by the number of charges introduced by ionization and y is the protein intensity of the corresponding mass per charge value, x, in that specimen. Given high coefficients of variation and other characteristics of protein intensity measures (y values), we reduce the measures of protein intensities to a set of binary variables that indicate peaks in the y-axis direction in the nearest neighborhoods of each mass per charge point in the x-axis direction. We then account for a shifting (measurement error) problem of the x-axis in SELDI output. After these pre-analysis processing of data, we combine the binary predictors to generate classification rules for cancer, benign hyperplasia, and normal states of prostate. Our approach is to apply the boosting algorithm to select binary predictors and construct a summary classifier. We empirically evaluate sensitivity and specificity of the resulting summary classifiers with a test dataset that is independent from the training dataset used to construct the summary classifiers. The proposed method performed nearly perfectly in distinguishing cancer and benign hyperplasia from normal. In the classification of cancer vs. benign hyperplasia, however, an appreciable proportion of the benign specimens were classified incorrectly as cancer. We discuss practical issues associated with our proposed approach to the analysis of SELDI output and its application in cancer biomarker discovery.

Profiling of alterations in platelet proteins during storage of platelet concentrates

BACKGROUND: The quality of platelet concentrates (PCs) is primarily determined in vitro by selective methods (e.g., pH, aggregometry), which provide only limited information on certain platelet (PLT) characteristics. In contrast, proteomic technologies provide a comprehensive overview of the PLT proteome. High interassay variability, however, limits meaningful assessment of samples taken from the same product over time or before and after processing. STUDY DESIGN AND METHODS: Differential in-gel electrophoresis (DIGE) and mass spectrometry were applied to analyze changes in the PLT proteome during storage of PCs. RESULTS: DIGE provides a comprehensive and reproducible overview of the cytoplasmic PLT proteome (median standard deviation of protein spot intensities, 5%-9%). Although 97 percent of cytosolic PLT proteins remained unchanged over a 9-day storage period, septin 2 showed characteristic alterations that preceded by several days more widespread alterations affecting numerous other proteins. Also beta-actin and gelsolin are potential marker proteins for changes in the PLT proteome. Interestingly septin 2 and gelsolin are affected during apoptosis, indicating that apoptosis in PCs may have an impact on PLT storage. CONCLUSION: DIGE is a tool for comprehensively assessing the impact of storage on the global proteome profile of therapeutic PCs. Most of the changes detected are in high-abundance PLT proteins.

Photon-beam subsource sensitivity to the initial electron-beam parameters

One limitation to the widespread implementation of Monte Carlo (MC) patient dose-calculation algorithms for radiotherapy is the lack of a general and accurate source model of the accelerator radiation source. Our aim in this work is to investigate the sensitivity of the photon-beam subsource distributions in a MC source model (with target, primary collimator, and flattening filter photon subsources and an electron subsource) for 6- and 18-MV photon beams when the energy and radial distributions of initial electrons striking a linac target change. For this purpose, phase-space data (PSD) was calculated for various mean electron energies striking the target, various normally distributed electron energy spread, and various normally distributed electron radial intensity distributions. All PSD was analyzed in terms of energy, fluence, and energy fluence distributions, which were compared between the different parameter sets. The energy spread was found to have a negligible influence on the subsource distributions. The mean energy and radial intensity significantly changed the target subsource distribution shapes and intensities. For the primary collimator and flattening filter subsources, the distribution shapes of the fluence and energy fluence changed little for different mean electron energies striking the target, however, their relative intensity compared with the target subsource change, which can be accounted for by a scaling factor. This study indicates that adjustments to MC source models can likely be limited to adjusting the target subsource in conjunction with scaling the relative intensity and energy spectrum of the primary collimator, flattening filter, and electron subsources when the energy and radial distributions of the initial electron-beam change.

Monte Carlo calculation of Varian 2300C/D Linac photon beam characteristics: a comparison between MCNP4C, GEANT3 and measurements

Different codes are used for Monte Carlo (MC) calculations in radiation therapy. In this research, MCNP4C and GEANT3 codes have been compared in calculations of dosimetric characteristics of Varian Clinac 2300C/D. The parameters of influence in the differences seen in dosimetric features were discussed. This study emphasizes that both MCNP4C and GEANT3 MC can be used in radiation therapy computations and their differences in photon spectra calculations have a negligible effect on percentage depth dose computations in radiation therapy.

Monte Carlo source model for photon beam radiotherapy: photon source characteristics

A major barrier to widespread clinical implementation of Monte Carlo dose calculation is the difficulty in characterizing the radiation source within a generalized source model. This work aims to develop a generalized three-component source model (target, primary collimator, flattening filter) for 6- and 18-MV photon beams that match full phase-space data (PSD). Subsource by subsource comparison of dose distributions, using either source PSD or the source model as input, allows accurate source characterization and has the potential to ease the commissioning procedure, since it is possible to obtain information about which subsource needs to be tuned. This source model is unique in that, compared to previous source models, it retains additional correlations among PS variables, which improves accuracy at nonstandard source-to-surface distances (SSDs). In our study, three-dimensional (3D) dose calculations were performed for SSDs ranging from 50 to 200 cm and for field sizes from 1 x 1 to 30 x 30 cm2 as well as a 10 x 10 cm2 field 5 cm off axis in each direction. The 3D dose distributions, using either full PSD or the source model as input, were compared in terms of dose-difference and distance-to-agreement. With this model, over 99% of the voxels agreed within +/-1% or 1 mm for the target, within 2% or 2 mm for the primary collimator, and within +/-2.5% or 2 mm for the flattening filter in all cases studied. For the dose distributions, 99% of the dose voxels agreed within 1% or 1 mm when the combined source model-including a charged particle source and the full PSD as input-was used. The accurate and general characterization of each photon source and knowledge of the subsource dose distributions should facilitate source model commissioning procedures by allowing scaling the histogram distributions representing the subsources to be tuned.