77 research outputs found

    Transkingdom Networks: A Systems Biology Approach to Identify Causal Members of Host-Microbiota Interactions

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    Improvements in sequencing technologies and reduced experimental costs have resulted in a vast number of studies generating high-throughput data. Although the number of methods to analyze these "omics" data has also increased, computational complexity and lack of documentation hinder researchers from analyzing their high-throughput data to its true potential. In this chapter we detail our data-driven, transkingdom network (TransNet) analysis protocol to integrate and interrogate multi-omics data. This systems biology approach has allowed us to successfully identify important causal relationships between different taxonomic kingdoms (e.g. mammals and microbes) using diverse types of data

    Current challenges facing the assessment of the allergenic capacity of food allergens in animal models

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    Food allergy is a major health problem of increasing concern. The insufficiency of protein sources for human nutrition in a world with a growing population is also a significant problem. The introduction of new protein sources into the diet, such as newly developed innovative foods or foods produced using new technologies and production processes, insects, algae, duckweed, or agricultural products from third countries, creates the opportunity for development of new food allergies, and this in turn has driven the need to develop test methods capable of characterizing the allergenic potential of novel food proteins. There is no doubt that robust and reliable animal models for the identification and characterization of food allergens would be valuable tools for safety assessment. However, although various animal models have been proposed for this purpose, to date, none have been formally validated as predictive and none are currently suitable to test the allergenic potential of new foods. Here, the design of various animal models are reviewed, including among others considerations of species and strain, diet, route of administration, dose and formulation of the test protein, relevant controls and endpoints measured

    Proposal for an Extended Run of T2K to 20×102120\times10^{21} POT

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    68 pages, 31 figures68 pages, 31 figures68 pages, 31 figuresRecent measurements by the T2K neutrino oscillation experiment indicate that CP violation in neutrino mixing may be observed in the future by long-baseline neutrino oscillation experiments. We propose an extension to the currently approved T2K running from 7.8\times 10^{21}~\mbox{POT} to 20\times 10^{21}~\mbox{POT}, aiming at initial observation of CP violation with 3σ\,\sigma or higher significance for the case of maximum CP violation. The program also contains a measurement of mixing parameters, θ23\theta_{23} and Δm322\Delta m^2_{32}, with a precision of 1.7^\circ or better and 1%, respectively. With accelerator and beamline upgrades, as well as analysis improvements, this program would occur before the next generation of long-baseline neutrino oscillation experiments that are expected to start operation in 2026

    Physics Potentials with the Second Hyper-Kamiokande Detector in Korea

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    We have conducted sensitivity studies on an alternative configuration of the Hyper-Kamiokande experiment by locating the 2nd Hyper-Kamiokande detector in Korea at \sim1100 -\ 1300 km baseline. Having two detectors at different baselines improves sensitivity to leptonic CP violation, neutrino mass ordering as well as nonstandard neutrino interactions. There are several candidate sites in Korea with greater than 1 km high mountains ranged at an 1-3 degree off-axis angle. Thanks to larger overburden of the candidate sites in Korea, low energy physics, such as solar and supernova neutrino physics as well as dark matter search, is expected to be improved. In this paper sensitivity studies on the CP violation phase and neutrino mass ordering are performed using current T2K systematic uncertainties in most cases. We plan to improve our sensitivity studies in the near future with better estimation of our systematic uncertainties
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