A new variant of Actinobacillus pleuropneumoniae serotype 3 lacking the entire apxII operon

Actinobacillus pleuropneumoniae is an important respiratory pathogen causing pleuropneumonia in pig. The species is genetically characterized by the presence of 4 RTX (Repeats in the Structural ToXin) toxin genes: apxI, apxII, and apxIII genes are differentially present in various combinations among the different serotypes, thereby defining pathogenicity; the apxIV gene is present in all serotypes. Polymerase chain reaction (PCR)-based apx gene typing is done in many veterinary diagnostic laboratories, especially reference laboratories. The present report describes the isolation of atypical A. pleuropneumoniae from 4 independent cases from 2 countries. All isolates were beta-nicotinamide adenine dinucleotide (beta-NAD) dependent and nonhemolytic but showed strong co-hemolysis with the sphingomyelinase of Staphylococcus aureus on sheep blood agar. Classical biochemical tests as well as Matrix-assisted laser desorption ionization time-of-flight mass spectrometry and sequence-based analysis (16S ribosomal RNA [rRNA] and rpoB genes) identified them as A. pleuropneumoniae. Apx-toxin gene typing using 2 different PCR systems showed the presence of apxIV and only the apxIII operon (apxIIICABD). None of the apxI or apxII genes were present as confirmed by Southern blot analysis. The 16S rRNA and rpoB gene analyses as well as serotype-specific PCR indicate that the isolates are variants of serotype 3. Strains harboring only apxIV and the apxIII operon are possibly emerging types of A. pleuropneumoniae and should therefore be carefully monitored for epidemiological reasons.

Proteomic and peptidomic study of proteolysis in quarter milk after infusion with lipoteichoic acid from Staphylococcus aureus

Mastitic milk is associated with increased bovine protease activity, such as that from plasmin and somatic cell enzymes, which cause proteolysis of the caseins and may reduce cheese yield and quality. The aim of this work was to characterize the peptide profile resulting from proteolysis in a model mastitis system and to identify the proteases responsible. One quarter of each of 2 cows (A and B) was infused with lipoteichoic acid from Staphylococcus aureus. The somatic cell counts of the infused quarters reached a peak 6h after infusion, whereas plasmin activity of those quarters also increased, reaching a peak after 48 and 12h for cow A and B, respectively. Urea-polyacrylamide gel electrophoretograms of milk samples of cow A and B obtained at different time points after infusion and incubated for up to 7 d showed almost full hydrolysis of beta- and alpha(S1)-casein during incubation of milk samples at peak somatic cell counts, with that of beta-casein being faster than that of alpha(S1)-casein. Two-dimensional gel electrophoretograms of milk 6h after infusion with the toxin confirmed hydrolysis of beta- and alpha(S1)-casein and the appearance of lower-molecular-weight products. Peptides were subsequently separated by reversed-phase HPLC and handmade nanoscale C(18) columns, and identified by matrix-assisted laser desorption/ionization time-of-flight tandem mass spectrometry. Twenty different peptides were identified and shown to originate from alpha(s1)- and beta-casein. Plasmin, cathepsin B and D, elastase, and amino- and carboxypeptidases were suggested as possible responsible proteases based on the peptide cleavage sites. The presumptive activity of amino- and carboxypeptidases is surprising and may indicate the activity of cathepsin H, which has not been reported in milk previously.

Macro Monte Carlo for dose calculation of proton beams

Although the Monte Carlo (MC) method allows accurate dose calculation for proton radiotherapy, its usage is limited due to long computing time. In order to gain efficiency, a new macro MC (MMC) technique for proton dose calculations has been developed. The basic principle of the MMC transport is a local to global MC approach. The local simulations using GEANT4 consist of mono-energetic proton pencil beams impinging perpendicularly on slabs of different thicknesses and different materials (water, air, lung, adipose, muscle, spongiosa, cortical bone). During the local simulation multiple scattering, ionization as well as elastic and inelastic interactions have been taken into account and the physical characteristics such as lateral displacement, direction distributions and energy loss have been scored for primary and secondary particles. The scored data from appropriate slabs is then used for the stepwise transport of the protons in the MMC simulation while calculating the energy loss along the path between entrance and exit position. Additionally, based on local simulations the radiation transport of neutrons and the generated ions are included into the MMC simulations for the dose calculations. In order to validate the MMC transport, calculated dose distributions using the MMC transport and GEANT4 have been compared for different mono-energetic proton pencil beams impinging on different phantoms including homogeneous and inhomogeneous situations as well as on a patient CT scan. The agreement of calculated integral depth dose curves is better than 1% or 1 mm for all pencil beams and phantoms considered. For the dose profiles the agreement is within 1% or 1 mm in all phantoms for all energies and depths. The comparison of the dose distribution calculated using either GEANT4 or MMC in the patient also shows an agreement of within 1% or 1 mm. The efficiency of MMC is up to 200 times higher than for GEANT4. The very good level of agreement in the dose comparisons demonstrate that the newly developed MMC transport results in very accurate and efficient dose calculations for proton beams.

Radiation metabolomics. 5. Identification of urinary biomarkers of ionizing radiation exposure in nonhuman primates by mass spectrometry-based metabolomics

Mass spectrometry-based metabolomics has previously demonstrated utility for identifying biomarkers of ionizing radiation exposure in cellular, mouse and rat in vivo radiation models. To provide a valuable link from small laboratory rodents to humans, γ-radiation-induced urinary biomarkers were investigated using a nonhuman primate total-body-irradiation model. Mass spectrometry-based metabolomics approaches were applied to determine whether biomarkers could be identified, as well as the previously discovered rodent biomarkers of γ radiation. Ultra-performance liquid chromatography-electrospray ionization quadrupole time-of-flight mass spectrometry analysis was carried out on a time course of clean-catch urine samples collected from nonhuman primates (n = 6 per cohort) exposed to sham, 1.0, 3.5, 6.5 or 8.5 Gy doses of (60)Co γ ray (∼0.55 Gy/min) ionizing radiation. By multivariate data analysis, 13 biomarkers of radiation were discovered: N-acetyltaurine, isethionic acid, taurine, xanthine, hypoxanthine, uric acid, creatine, creatinine, tyrosol sulfate, 3-hydroxytyrosol sulfate, tyramine sulfate, N-acetylserotonin sulfate, and adipic acid. N-Acetyltaurine, isethionic acid, and taurine had previously been identified in rats, and taurine and xanthine in mice after ionizing radiation exposure. Mass spectrometry-based metabolomics has thus successfully revealed and verified urinary biomarkers of ionizing radiation exposure in the nonhuman primate for the first time, which indicates possible mechanisms for ionizing radiation injury.

Metabolomics reveals the metabolic map of procainamide in humans and mice

Procainamide, a type I antiarrhythmic agent, is used to treat a variety of atrial and ventricular dysrhythmias. It was reported that long-term therapy with procainamide may cause lupus erythematosus in 25-30% of patients. Interestingly, procainamide does not induce lupus erythematosus in mouse models. To explore the differences in this side-effect of procainamide between humans and mouse models, metabolomic analysis using ultra-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC-ESI-QTOFMS) was conducted on urine samples from procainamide-treated humans, CYP2D6-humanized mice, and wild-type mice. Thirteen urinary procainamide metabolites, including nine novel metabolites, derived from P450-dependent, FMO-dependent oxidations and acylation reactions, were identified and structurally elucidated. In vivo metabolism of procainamide in CYP2D6-humanized mice as well as in vitro incubations with microsomes and recombinant P450s suggested that human CYP2D6 plays a major role in procainamide metabolism. Significant differences in N-acylation and N-oxidation of the drug between humans and mice largely account for the interspecies differences in procainamide metabolism. Significant levels of the novel N-oxide metabolites produced by FMO1 and FMO3 in humans might be associated with the development of procainamide-induced systemic lupus erythematosus. Observations based on this metabolomic study offer clues to understanding procainamide-induced lupus in humans and the effect of P450s and FMOs on procainamide N-oxidation.

A mass spectrometric multicenter study supports classification of preeclampsia as heterogeneous disorder

The diagnostic value of affinity-based matrix-assisted laser desorption/ionization time-of-flight mass spectrometry analysis to distinguish preeclampsia (PE) from matched controls was tested in a multicenter setting.

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.

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.

Labeling the human skeleton with 41Ca to assess changes in bone calcium metabolism

Bone research is limited by the methods available for detecting changes in bone metabolism. While dual X-ray absorptiometry is rather insensitive, biochemical markers are subject to significant intra-individual variation. In the study presented here, we evaluated the isotopic labeling of bone using 41Ca, a long-lived radiotracer, as an alternative approach. After successful labeling of the skeleton, changes in the systematics of urinary 41Ca excretion are expected to directly reflect changes in bone Ca metabolism. A minute amount of 41Ca (100 nCi) was administered orally to 22 postmenopausal women. Kinetics of tracer excretion were assessed by monitoring changes in urinary 41Ca/40Ca isotope ratios up to 700 days post-dosing using accelerator mass spectrometry and resonance ionization mass spectrometry. Isotopic labeling of the skeleton was evaluated by two different approaches: (i) urinary 41Ca data were fitted to an established function consisting of an exponential term and a power law term for each individual; (ii) 41Ca data were analyzed by population pharmacokinetic (NONMEM) analysis to identify a compartmental model that describes urinary 41Ca tracer kinetics. A linear three-compartment model with a central compartment and two sequential peripheral compartments was found to best fit the 41Ca data. Fits based on the use of the combined exponential/power law function describing urinary tracer excretion showed substantially higher deviations between predicted and measured values than fits based on the compartmental modeling approach. By establishing the urinary 41Ca excretion pattern using data points up to day 500 and extrapolating these curves up to day 700, it was found that the calculated 41Ca/40Ca isotope ratios in urine were significantly lower than the observed 41Ca/40Ca isotope ratios for both techniques. Compartmental analysis can overcome this limitation. By identifying relative changes in transfer rates between compartments in response to an intervention, inaccuracies in the underlying model cancel out. Changes in tracer distribution between compartments were modeled based on identified kinetic parameters. While changes in bone formation and resorption can, in principle, be assessed by monitoring urinary 41Ca excretion over the first few weeks post-dosing, assessment of an intervention effect is more reliable approximately 150 days post-dosing when excreted tracer originates mainly from bone.

Theoretical considerations to the verification of dynamic multileaf collimated fields with a SLIC-type portal image detector

The verification possibilities of dynamically collimated treatment beams with a scanning liquid ionization chamber electronic portal image device (SLIC-EPID) are investigated. The ion concentration in the liquid of a SLIC-EPID and therefore the read-out signal is determined by two parameters of a differential equation describing the creation and recombination of the ions. Due to the form of this equation, the portal image detector describes a nonlinear dynamic system with memory. In this work, the parameters of the differential equation were experimentally determined for the particular chamber in use and for an incident open 6 MV photon beam. The mathematical description of the ion concentration was then used to predict portal images of intensity-modulated photon beams produced by a dynamic delivery technique, the sliding window approach. Due to the nature of the differential equation, a mathematical condition for 'reliable leaf motion verification' in the sliding window technique can be formulated. It is shown that the time constants for both formation and decay of the equilibrium concentration in the chamber is in the order of seconds. In order to guarantee reliable leaf motion verification, these time constants impose a constraint on the rapidity of the image-read out for a given maximum leaf speed. For a leaf speed of 2 cm s(-1), a minimum image acquisition frequency of about 2 Hz is required. Current SLIC-EPID systems are usually too slow since they need about a second to acquire a portal image. However, if the condition is fulfilled, the memory property of the system can be used to reconstruct the leaf motion. It is shown that a simple edge detecting algorithm can be employed to determine the leaf positions. The method is also very robust against image noise.