The Experts below are selected from a list of 408 Experts worldwide ranked by ideXlab platform
Bailin Cong - One of the best experts on this subject based on the ideXlab platform.
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validation of housekeeping genes for gene expression studies in an Ice Alga chlamydomonas during freezing acclimation
Extremophiles, 2012Co-Authors: Chenlin Liu, Xiaohang Huang, Shenghao Liu, Bailin CongAbstract:Antarctic Ice Alga Chlamydomonas sp. Ice-L can endure extreme low temperature and high salinity stress under freezing conditions. To elucidate the molecular acclimation mechanisms using gene expression analysis, the expression stabilities of ten housekeeping genes of Chlamydomonas sp. Ice-L during freezing stress were analyzed. Some discrepancies were detected in the ranking of the candidate reference genes between geNorm and NormFinder programs, but there was substantial agreement between the groups of genes with the most and the least stable expression. RPL19 was ranked as the best candidate reference genes. Pairwise variation (V) analysis indicated the combination of two reference genes was sufficient for qRT-PCR data normalization under the experimental conditions. Considering the co-regulation between RPL19 and RPL32 (the most stable gene pairs given by geNorm program), we propose that the mean data rendered by RPL19 and GAPDH (the most stable gene pairs given by NormFinder program) be used to normalize gene expression values in Chlamydomonas sp. Ice-L more accurately. The example of FAD3 gene expression calculation demonstrated the importance of selecting an appropriate category and number of reference genes to achieve an accurate and reliable normalization of gene expression during freeze acclimation in Chlamydomonas sp. Ice-L.
Senneville Simon - One of the best experts on this subject based on the ideXlab platform.
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Spatial and temporal variability of Ice Algal production in a 3D Ice–ocean model of the Hudson Bay, Hudson Strait and Foxe Basin system
'Co-Action Publishing', 2010Co-Authors: Sibert Virginie, Zakardjian Bruno, Saucier François, Gosselin Michel, Starr Michel, Senneville SimonAbstract:Primary production, the basic component of the food web and a sink for dissolved inorganic carbon, is a major unknown in Arctic seas, particularly Ice Algal production, for which detailed and comprehensive studies are often limited in space and time. We present here a simple Ice Alga model and its coupling with a regional 3D Ice–ocean model of the Hudson Bay system (HBS), including Hudson Strait and Foxe Basin, as a first attempt to estimate Ice Algal production and its potential contribution to the pelagic ecosystem on a regional scale. The Ice Algal growth rate is forced by sub-Ice light and nutrient availability, whereas grazing and Ice melt control biomass loss from the underside of the Ice. The simulation shows the primary role of sea-Ice dynamics on the distribution and production of Ice Algae with a high spatio-temporal variability in response to the great variability of Ice conditions in different parts of the HBS. In addition to favourable light and nutrient conditions, there must be a sufficient time lag between the onset of sufficient light and Ice melt to ensure significant Ice Algal production. This suggests that, in the context of enhanced warming in Arctic and sub-Arctic regions, earlier melt could be more damaging for Ice Algal production than later freezing. The model also includes a particulate organic matter (POM) variable, fed by Ice melting losses to the water column, and shows a large redistribution of the POM produced by the Ice ecosystem on a regional scale
Simon Senneville - One of the best experts on this subject based on the ideXlab platform.
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Spatial and temporal variability of Ice Algal production in a 3D Ice-ocean model of the Hudson Bay, Hudson Strait and Foxe Basin system
Polar Research, 2010Co-Authors: V. Sibert, Bruno Zakardjian, Michel Starr, Michel Gosselin, Francois Saucier, Simon SennevilleAbstract:Primary production, the basic component of the food web and a sink for dissolved inorganic carbon, is a major unknown in Arctic seas, particularly Ice Algal production, for which detailed and comprehensive studies are often limited in space and time. We present here a simple Ice Alga model and its coupling with a regional 3D Ice-ocean model of the Hudson Bay system (HBS), including Hudson Strait and Foxe Basin, as a first attempt to estimate Ice Algal production and its potential contribution to the pelagic ecosystem on a regional scale. The Ice Algal growth rate is forced by sub-Ice light and nutrient availability, whereas grazing and Ice melt control biomass loss from the underside of the Ice. The simulation shows the primary role of sea-Ice dynamics on the distribution and production of Ice Algae with a high spatio-temporal variability in response to the great variability of Ice conditions in different parts of the HBS. In addition to favourable light and nutrient conditions, there must be a sufficient time lag between the onset of sufficient light and Ice melt to ensure significant Ice Algal production. This suggests that, in the context of enhanced warming in Arctic and sub-Arctic regions, earlier melt could be more damaging for Ice Algal production than later freezing. The model also includes a particulate organic matter (POM) variable, fed by Ice melting losses to the water column, and shows a large redistribution of the POM produced by the Ice ecosystem on a regional scale.
Chenlin Liu - One of the best experts on this subject based on the ideXlab platform.
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validation of housekeeping genes for gene expression studies in an Ice Alga chlamydomonas during freezing acclimation
Extremophiles, 2012Co-Authors: Chenlin Liu, Xiaohang Huang, Shenghao Liu, Bailin CongAbstract:Antarctic Ice Alga Chlamydomonas sp. Ice-L can endure extreme low temperature and high salinity stress under freezing conditions. To elucidate the molecular acclimation mechanisms using gene expression analysis, the expression stabilities of ten housekeeping genes of Chlamydomonas sp. Ice-L during freezing stress were analyzed. Some discrepancies were detected in the ranking of the candidate reference genes between geNorm and NormFinder programs, but there was substantial agreement between the groups of genes with the most and the least stable expression. RPL19 was ranked as the best candidate reference genes. Pairwise variation (V) analysis indicated the combination of two reference genes was sufficient for qRT-PCR data normalization under the experimental conditions. Considering the co-regulation between RPL19 and RPL32 (the most stable gene pairs given by geNorm program), we propose that the mean data rendered by RPL19 and GAPDH (the most stable gene pairs given by NormFinder program) be used to normalize gene expression values in Chlamydomonas sp. Ice-L more accurately. The example of FAD3 gene expression calculation demonstrated the importance of selecting an appropriate category and number of reference genes to achieve an accurate and reliable normalization of gene expression during freeze acclimation in Chlamydomonas sp. Ice-L.
V. Sibert - One of the best experts on this subject based on the ideXlab platform.
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Spatial and temporal variability of Ice Algal production in a 3D Ice-ocean model of the Hudson Bay, Hudson Strait and Foxe Basin system
Polar Research, 2010Co-Authors: V. Sibert, Bruno Zakardjian, Michel Starr, Michel Gosselin, Francois Saucier, Simon SennevilleAbstract:Primary production, the basic component of the food web and a sink for dissolved inorganic carbon, is a major unknown in Arctic seas, particularly Ice Algal production, for which detailed and comprehensive studies are often limited in space and time. We present here a simple Ice Alga model and its coupling with a regional 3D Ice-ocean model of the Hudson Bay system (HBS), including Hudson Strait and Foxe Basin, as a first attempt to estimate Ice Algal production and its potential contribution to the pelagic ecosystem on a regional scale. The Ice Algal growth rate is forced by sub-Ice light and nutrient availability, whereas grazing and Ice melt control biomass loss from the underside of the Ice. The simulation shows the primary role of sea-Ice dynamics on the distribution and production of Ice Algae with a high spatio-temporal variability in response to the great variability of Ice conditions in different parts of the HBS. In addition to favourable light and nutrient conditions, there must be a sufficient time lag between the onset of sufficient light and Ice melt to ensure significant Ice Algal production. This suggests that, in the context of enhanced warming in Arctic and sub-Arctic regions, earlier melt could be more damaging for Ice Algal production than later freezing. The model also includes a particulate organic matter (POM) variable, fed by Ice melting losses to the water column, and shows a large redistribution of the POM produced by the Ice ecosystem on a regional scale.
