The Experts below are selected from a list of 256728 Experts worldwide ranked by ideXlab platform

Robert Mckibbin - One of the best experts on this subject based on the ideXlab platform.

  • a cfd modeling system for airflow and heat transfer in ventilated packaging for fresh foods ii Computational Solution software development and model testing
    Journal of Food Engineering, 2006
    Co-Authors: Qian Zou, Linus U Opara, Robert Mckibbin
    Abstract:

    Abstract In the previous paper (Part I) of this series, mathematical models of airflow and heat transfer inside ventilated packaging systems were developed and presented based on the porous media approach. In the present paper (Part II), we describe the CFD methods used to solve the mathematical models for both layered and bulk packaging systems, and the structure of a user-friendly software package (called ‘CoolSimu’) that integrates the modeling system. We also present the results of several simulation tests for both layered cartons and bulk bins of fruit undergoing forced-air cooling. Overall, the CFD modeling system gave satisfactory predictions of temperature profiles of fruit. When model predictions and experimental product center temperature data were compared, good agreements were obtained. The lack of fit at certain locations inside the packaging was attributed to inaccurate temperature measurement and uncertainties in model input data.

Aiqian Zhang - One of the best experts on this subject based on the ideXlab platform.

  • a novel Computational Solution to the health risk assessment of air pollution via joint toxicity prediction a case study on selected pah binary mixtures in particulate matters
    Ecotoxicology and Environmental Safety, 2019
    Co-Authors: Huazhou Zhang, Jianjie Fu, Minghui Zheng, Aiqian Zhang
    Abstract:

    Abstract Regional haze episode has already caused overwhelming public concern. Unraveling the health effects of the representative composition mixtures of atmospheric fine particulate matters (PM2.5) becomes a top priority. In this study, a novel Computational Solution integrating chemical-induced genomic residual effect prediction with in vitro-based risk assessment is proposed to obtain the cumulative health risk of typical chemical mixtures of particulate matters (PM). The joint toxicity of binary mixtures is estimated by analyzing both genomic similarity and dose-response curve of relevant pollutants for the chemical-induced genomic residual effect. Specifically, the modified relative potency factor (mRPF) of mixtures is introduced for this purpose, and the ratio of activation (RA) value is defined to assess the corresponding health risks of the mixtures. As a methodology demonstration, the health risk of typical binary polycyclic aromatic hydrocarbon (PAH) mixtures in PM, containing Benzo[a]pyrene (BaP) as a component, is assessed using the proposed Solution. Our results indicate that the combined effect of pairwise PAHs of BaP with Benzo[b]fluoranthene (BbF) and Benz[a]anthracene (BaA) is synergistic on p53 pathway, and that the health risk of the such mixtures increases compared to that of the individual ones. Obviously, the cumulative health risk of environmental mixtures will be underestimated when the synergistic effect is wrongly assumed to be additive. To our knowledge, this is the first study ever report on a Computational Solution to the health risk assessment of environmental pollution via joint toxicity prediction. The novel methodology proposed here makes full use of the open-access in vitro assay data and transcriptomic information in literatures and provides a successful demonstration of the concept of systems biology and translational science.

Miklós Erdélyi - One of the best experts on this subject based on the ideXlab platform.

  • TestSTORM: Versatile simulator software for multimodal super-reSolution localization fluorescence microscopy
    Scientific Reports, 2017
    Co-Authors: Tibor Novák, Tamás Gajdos, József Sinkó, Gábor Szabó, Miklós Erdélyi
    Abstract:

    Optimization of sample, imaging and data processing parameters is an essential task in localization based super-reSolution microscopy, where the final image quality strongly depends on the imaging of single isolated fluorescent molecules. A Computational Solution that uses a simulator software for the generation of test data stacks was proposed, developed and tested. The implemented advanced physical models such as scalar and vector based point spread functions, polarization sensitive detection, drift, spectral crosstalk, structured background etc., made the simulation results more realistic and helped us interpret the final super-resolved images and distinguish between real structures and imaging artefacts.

M. Hosseininejad - One of the best experts on this subject based on the ideXlab platform.

  • Plasma Parameters Measurement in IR-T1 Tokamak with Langmuir Probe and the Simulation of the Lower Hybrid Waves in IR-T1, JET and NSTX Tokamaks
    Journal of Fusion Energy, 2012
    Co-Authors: A. Molavi Choobini, M. Hosseininejad
    Abstract:

    Ohmic heating is not enough for ignition mode in plasma and fusion reactions. Therefore additional methods, has been used, such as wave injection into plasma. Radio frequency wave injection into fusion plasma is more considered. Interaction between waves and plasma is described by Fokker–Planck equation with an added quasi-linear term. This paper is composed of three sections. At first, required experimental parameters for LSC program such as temperature and density measured by a Movable Langmuir Probe in IR-T1, and then we presented Computational Solution of Fokker–Planck equation with Adjoint method and Rosenbluth potentials to achieve distribution function in velocity space and at least, we simulated Lower Hybrid Waves and quasi-linear term for NSTX, JET and IR-T1 tokamak. The results of this simulation showed higher efficiency of NSTX, in comparison with JET and IR-T1.

  • Plasma Parameters Measurement in IR-T1 Tokamak with Langmuir Probe and the Simulation of the Lower Hybrid Waves in IR-T1, JET and NSTX Tokamaks
    Journal of Fusion Energy, 2012
    Co-Authors: A. Molavi Choobini, M. Hosseininejad
    Abstract:

    Ohmic heating is not enough for ignition mode in plasma and fusion reactions. Therefore additional methods, has been used, such as wave injection into plasma. Radio frequency wave injection into fusion plasma is more considered. Interaction between waves and plasma is described by Fokker–Planck equation with an added quasi-linear term. This paper is composed of three sections. At first, required experimental parameters for LSC program such as temperature and density measured by a Movable Langmuir Probe in IR-T1, and then we presented Computational Solution of Fokker–Planck equation with Adjoint method and Rosenbluth potentials to achieve distribution function in velocity space and at least, we simulated Lower Hybrid Waves and quasi-linear term for NSTX, JET and IR-T1 tokamak. The results of this simulation showed higher efficiency of NSTX, in comparison with JET and IR-T1.

Yibing Wei - One of the best experts on this subject based on the ideXlab platform.

  • a Computational Solution to improve biomarker reproducibility during long term projects
    PLOS ONE, 2019
    Co-Authors: Feng Feng, Morgan P Thompson, Beena E Thomas, Elizabeth R Duffy, Jiyoun Kim, Shinichiro Kurosawa, Joseph Y Tashjian, Yibing Wei
    Abstract:

    Biomarkers are fundamental to basic and clinical research outcomes by reporting host responses and providing insight into disease pathophysiology. Measuring biomarkers with research-use ELISA kits is universal, yet lack of kit standardization and unexpected lot-to-lot variability presents analytic challenges for long-term projects. During an ongoing two-year project measuring plasma biomarkers in cancer patients, control concentrations for one biomarker (PF) decreased significantly after changes in ELISA kit lots. A comprehensive operations review pointed to standard curve shifts with the new kits, an analytic variable that jeopardized data already collected on hundreds of patient samples. After excluding other reasonable contributors to data variability, a Computational Solution was developed to provide a uniform platform for data analysis across multiple ELISA kit lots. The Solution (ELISAtools) was developed within open-access R software in which variability between kits is treated as a batch effect. A defined best-fit Reference standard curve is modelled, a unique Shift factor “S” is calculated for every standard curve and data adjusted accordingly. The averaged S factors for PF ELISA kit lots #1–5 ranged from -0.086 to 0.735, and reduced control inter-assay variability from 62.4% to <9%, within quality control limits. S factors calculated for four other biomarkers provided a quantitative metric to monitor ELISAs over the 10 month study period for quality control purposes. Reproducible biomarker measurements are essential, particularly for long-term projects with valuable patient samples. Use of research-use ELISA kits is ubiquitous and judicious use of this Computational Solution maximizes biomarker reproducibility.

  • A Computational Solution To Improve Biomarker Reproducibility During Long-term Projects
    2018
    Co-Authors: Feng Feng, Morgan P Thompson, Beena E Thomas, Elizabeth R Duffy, Jiyoun Kim, Shinichiro Kurosawa, Joseph Y Tashjian, Yibing Wei, Chris Andry, Deborah J. Stearns-kurosawa
    Abstract:

    Biomarkers are fundamental to basic and clinical research outcomes by reporting host responses and providing insight into disease pathophysiology. Measuring biomarkers with research-use ELISA kits is universal, yet lack of kit standardization and unexpected lot-to-lot variability presents analytic challenges for long-term projects. During an ongoing two-year project measuring plasma biomarkers in cancer patients, control concentrations for one biomarker (PF) decreased significantly after changes in ELISA kit lots. A comprehensive operations review pointed to standard curve shifts with the new kits, an analytic variable that jeopardized data already collected on hundreds of patient samples. After excluding other reasonable contributors to data variability, a Computational Solution was developed to provide a uniform platform for data analysis across multiple ELISA kit lots. The Solution (ELISAtools) was developed within open-access R software in which variability between kits is treated as a batch effect. A defined best-fit Reference standard curve is modelled, a unique Shift factor "S" is calculated for every standard curve and data adjusted accordingly. The averaged S factors for PF ELISA kit lots #1-5 ranged from -0.086 to 0.735, and reduced control inter-assay variability from 62.4% to