Adaptive simulation using mode identification

Adaptive simulation using modal clustering and method of potential function

A discrete slug population model determined by egg production

Slugs are significant pests in agriculture (as well as a nuisance to gardeners), and it is therefore important to understand their population dynamics for the construction of efficient and effective control measures. Differential equation models of slug populations require the inclusion of large (variable) temporal delays, and strong seasonal forcing results in a non-autonomous system. This renders such models open to only a limited amount of rigorous analysis. In this paper, we derive a novel batch model based purely upon the quantity of eggs produced at different times of the year. This model is open to considerable reduction; from the resulting two variable discrete-time system it is possible to reconstruct the dynamics of the full population across the year and give conditions for extinction or global stability and persistence. Furthermore, the steady state temporal population distribution displays qualitatively different behavior with only small changes in the survival probability of slugs. The model demonstrates how small variations in the favorability of different years may result in widely different slug population fluctuations between consecutive years, and is in good agreement with field data

Dynamics of a structured slug population model in the absence of seasonal variation

We develop a novel, nonlinear structured population model for the slug Deroceras reticulatum, a highly significant agricultural pest of great economic impact, in both organic and non-organic settings. In the absence of seasonal variations, we numerically explore the effect of life history traits that are dependent on an individual's size and measures of population biomass. We conduct a systematic exploration of parameter space and highlight the main mechanisms and implications of model design. A major conclusion of this work is that strong size dependent predation significantly adjusts the competitive balance, leading to non-monotonic steady state solutions and slowly decaying transients consisting of distinct generational cycles. Furthermore, we demonstrate how a simple ratio of adult to juvenile biomass can act as a useful diagnostic to distinguish between predated and non-predated environments, and may be useful in agricultural settings

Micro-Hall Magnetometry Studies of Thermally Assisted and Pure Quantum Tunneling in Single Molecule Magnet Mn12-Acetate

We have studied the crossover between thermally assisted and pure quantum tunneling in single crystals of high spin (S=10) uniaxial single molecule magnet Mn12-acetate using micro-Hall effect magnetometry. Magnetic hysteresis experiments have been used toinvestigate the energy levels that determine the magnetization reversal as a function of magnetic field and temperature. These experiments demonstrate that the crossover occurs in a narrow (~0.1 K) or broad (~1 K) temperature interval depending on the magnitude and direction of the applied field. For low external fields applied parallel to the easy axis, the energy levels that dominate the tunneling shift abruptly with temperature. In the presence of a transverse field and/or large longitudinal field these energy levels change with temperature more gradually. A comparison of our experimental results with model calculations of this crossover suggest that there are additional mechanisms that enhance the tunneling rate of low lying energy levels and broaden the crossover for small transverse fields.Comment: 5 pages, 5 figure

Comparison of caffeine-induced changes in cerebral blood flow and middle cerebral artery blood velocity shows that caffeine reduces middle cerebral artery diameter

Changes in cerebral blood flow (CBF) can be assessed directly with xenon clearance (XeC) or indirectly by measuring changes in middle cerebral artery blood velocity (Vmca) with transcranial Doppler (TCD). The aim of this study was to compare the changes in CBF and Vmca following caffeine ingestion. Nineteen patients (age 48–86, recovering from an acute stroke) and ten controls (age 52–85) were each studied twice. Bilateral measurements of CBF and Vmca were made before and after ingestion of 250 mg caffeine or matched placebo. The percentage change in CBF and Vmca after caffeine was calculated. Full results (CBF and Vmca) were obtained from 14 patients and 9 controls. There was no significant difference between patients and controls, so results were combined. Caffeine reduced CBF by 22% (95% confidence interval (CI) = 17% to 28%) and reduced Vmca by 13% (95% CI = 10% to 17%). The fall in Vmca was significantly less than that in CBF (p = 0.0016), showing that caffeine reduces mca diameter. Analysis based on Poiseuille flow in the arterioles suggests that caffeine reduced arteriole diameter by 5.9% (95% CI = 4.6% to 7.3%) and mca diameter by 4.3% (95% CI = 2.0% to 6.6%). TCD is being used as an alternative to XeC for assessing the effect of vasoconstrictors and vasodilators on CBF. This study has demonstrated that mca diameter can be changed by the vasoactive agents, and that changes in Vmca do not necessarily reflect changes in CBF

Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomics

<p>Abstract</p> <p>Background</p> <p>High-throughput peptide and protein identification technologies have benefited tremendously from strategies based on tandem mass spectrometry (MS/MS) in combination with database searching algorithms. A major problem with existing methods lies within the significant number of false positive and false negative annotations. So far, standard algorithms for protein identification do not use the information gained from separation processes usually involved in peptide analysis, such as retention time information, which are readily available from chromatographic separation of the sample. Identification can thus be improved by comparing measured retention times to predicted retention times. Current prediction models are derived from a set of measured test analytes but they usually require large amounts of training data.</p> <p>Results</p> <p>We introduce a new kernel function which can be applied in combination with support vector machines to a wide range of computational proteomics problems. We show the performance of this new approach by applying it to the prediction of peptide adsorption/elution behavior in strong anion-exchange solid-phase extraction (SAX-SPE) and ion-pair reversed-phase high-performance liquid chromatography (IP-RP-HPLC). Furthermore, the predicted retention times are used to improve spectrum identifications by a <it>p</it>-value-based filtering approach. The approach was tested on a number of different datasets and shows excellent performance while requiring only very small training sets (about 40 peptides instead of thousands). Using the retention time predictor in our retention time filter improves the fraction of correctly identified peptide mass spectra significantly.</p> <p>Conclusion</p> <p>The proposed kernel function is well-suited for the prediction of chromatographic separation in computational proteomics and requires only a limited amount of training data. The performance of this new method is demonstrated by applying it to peptide retention time prediction in IP-RP-HPLC and prediction of peptide sample fractionation in SAX-SPE. Finally, we incorporate the predicted chromatographic behavior in a <it>p</it>-value based filter to improve peptide identifications based on liquid chromatography-tandem mass spectrometry.</p

Age-Related Toxoplasma gondii Seroprevalence in Dutch Wild Boar Inconsistent with Lifelong Persistence of Antibodies

Toxoplasma gondii is an important zoonotic pathogen that is best known as a cause of abortion or abnormalities in the newborn after primary infection during pregnancy. Our aim was to determine the prevalence of T. gondii in wild boar to investigate the possible role of their meat in human infection and to get an indication of the environmental contamination with T. gondii. The presence of anti-T. gondii antibodies was determined by in-house ELISA in 509 wild boar shot in 2002/2003 and 464 wild boar shot in 2007. Most of the boar originated from the “Roerstreek” (n = 673) or the “Veluwe” (n = 241). A binormal mixture model was fitted to the log-transformed optical density values for wild boar up to 20 months old to estimate the optimal cut-off value (−0.685) and accompanying sensitivity (90.6%) and specificity (93.6%). The overall seroprevalence was estimated at 24.4% (95% CI: 21.1–27.7%). The prevalence did not show variation between sampling years or regions, indicating a stable and homogeneous infection pressure from the environment. The relation between age and seroprevalence was studied in two stages. Firstly, seroprevalence by age group was determined by fitting the binary mixture model to 200 animals per age category. The prevalence showed a steep increase until approximately 10 months of age but stabilized at approximately 35% thereafter. Secondly, we fitted the age-dependent seroprevalence data to several SIR-type models, with seropositives as infected (I) and seronegatives as either susceptible (S) or resistant (R). A model with a recovery rate (SIS) was superior to a model without a recovery rate (SI). This finding is not consistent with the traditional view of lifelong persistence of T. gondii infections. The high seroprevalence suggests that eating undercooked wild boar meat may pose a risk of infection with T. gondii

Potential of poly(styrene-co-divinylbenzene) monolithic columns for the LC-MS analysis of protein digests

Two polystyrene-based capillary monolithic columns of different length (50 and 250 mm) were used to evaluate the effects of column length on gradient separation of protein digests. A tryptic digest of a 9-protein mixture was used as a test sample. Peak capacities were determined from selected extracted ion chromatograms, and tandem mass spectrometry data were used for database matching using the MASCOT search engine. Peak capacities and protein identification scores were higher for the long column with all gradients. Peak capacities appear to approach a plateau for longer gradient times; maximum peak capacity was estimated to be 294 for the short column and 370 for the long column. Analyses with similar gradient slope produced a ratio of the peak capacities of 3.36 for the long and the short column, which is slightly higher than the expected value of the square root of the column length ratio. The use of a longer monolith improves peptide separation, as reflected by higher peak capacity, and also increases protein identification, as observed from higher identification scores and a larger number of identified peptides. Attention has also been paid to the peak production rate (PPR, peak capacity per unit time). For short analysis times, the short column produces a higher PPR, while for analysis times longer than 40 min, the PPR of the 250-mm column is higher