Keyword search (4,163 papers available)

"Mixture" Keyword-tagged Publications:

Title Authors PubMed ID
1 Trajectories of Alcohol-Related Problems Among First-Year Nursing Students: Nature, Predictors, and Outcomes Cheyroux P; Morin AJS; O' Connor RM; Colombat P; Vancappel A; Eltanoukhi R; Gillet N; 41797206
PSYCHOLOGY
2 Scientists warning: we must change paradigm for a revolution in toxicology and world food supply Seralini GE; Jungers G; Andersen A; Antoniou M; Aschner M; Bacon MH; Bertrand M; Bohn T; Bonfleur ML; Bücking E; Defarge N; Djemil R; Domingo JL; Douzelet J; Fagan J; Fournier T; Garcia JLY; Gil S; Hervé-Gruyer P; Hilbeck A; Hilty L; Huber D; Joyeux H; Khan I; Kouretas D; Lemarchand F; Loening U; Longo G; Mesnage R; Nikolopoulou DI; Panoff JM; Parente C; Robinson C; Scherber C; Sprangers D; Sultan C; Tsatsakis A; Vandelac L; Wan NF; Wynne B; Zaller JG; Zerrad-Saadi A; Zhang X; 41551494
CHEMBIOCHEM
3 Optimizing Mixtures of Metal-Organic Frameworks for Robust and Bespoke Passive Atmospheric Water Harvesting Harriman C; Ke Q; Vlugt TJH; Howarth AJ; Simon CM; 41427123
CHEMBIOCHEM
4 Deep clustering analysis via variational autoencoder with Gamma mixture latent embeddings Guo J; Fan W; Amayri M; Bouguila N; 39662201
ENCS
5 Developmental heterogeneity of school burnout across the transition from upper secondary school to higher education: A 9-year follow-up study Nadon L; Morin AJS; Gilbert W; Olivier E; Salmela-Aro K; 39645324
PSYCHOLOGY
6 Self-consolidating concrete: Dataset on mixture design and key properties Amine El Mahdi Safhi 38533116
ENCS
7 Unsupervised Mixture Models on the Edge for Smart Energy Consumption Segmentation with Feature Saliency Al-Bazzaz H; Azam M; Amayri M; Bouguila N; 37837127
ENCS
8 Entropy-Based Variational Scheme with Component Splitting for the Efficient Learning of Gamma Mixtures Bourouis S; Pawar Y; Bouguila N; 35009726
ENCS
9 Mixtures of rare earth elements show antagonistic interactions in Chlamydomonas reinhardtii Morel E; Cui L; Zerges W; Wilkinson KJ; 34175518
BIOLOGY
10 BioMiCo: a supervised Bayesian model for inference of microbial community structure. Shafiei M, Dunn KA, Boon E, MacDonald SM, Walsh DA, Gu H, Bielawski JP 25774293
BIOLOGY

 

Title:Mixtures of rare earth elements show antagonistic interactions in Chlamydomonas reinhardtii
Authors:Morel ECui LZerges WWilkinson KJ
Link:https://pubmed.ncbi.nlm.nih.gov/34175518/
DOI:10.1016/j.envpol.2021.117594
Publication:Environmental pollution (Barking, Essex : 1987)
Keywords:MicroalgaeMixtureRare earth elementsTranscriptomic analysis
PMID:34175518 Category: Date Added:2021-06-28
Dept Affiliation: BIOLOGY
1 Biophysical Environmental Chemistry Group, University of Montreal, P.O. Box 6128, Succ. Centre-Ville, Montreal, QC, Canada.
2 Dept. of Biology, Concordia University, 7141 Sherbrooke W., H4B 1R6, Montreal, QC, Canada.
3 Biophysical Environmental Chemistry Group, University of Montreal, P.O. Box 6128, Succ. Centre-Ville, Montreal, QC, Canada. Electronic address: kj.wilkinson@umontreal.ca.

Description:

In order to better understand the environmental risks of the rare earth elements (REEs), it is necessary to determine their fate and biological effects under environmentally relevant conditions (e.g. at low concentrations, REE mixtures). Here, the unicellular freshwater microalga, Chlamydomonas reinhardtii, was exposed for 2 h to one of three soluble REEs (Ce, Tm, Y) salts at 0.5 µM or to an equimolar mixture of these REEs. RNA sequencing revealed common biological effects among the REEs. Known functions of the differentially expressed genes support effects of REEs on protein processing in the endoplasmic reticulum, phosphate transport and the homeostasis of Fe and Ca. The only stress response detected was related to protein misfolding in the endoplasmic reticulum. When the REEs were applied as a mixture, antagonistic effects were overwhelmingly observed with transcriptomic results suggesting that the REEs were initially competing with each other for bio-uptake. Metal biouptake results were consistent with this interpretation. These results suggest that the approach of government agencies to regulate the REEs using biological effects data from single metal exposures may be a largely conservative approach.





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