What is he best known for?
The fields of study he is best known for:
His primary areas of investigation include Microbiology, 16S ribosomal RNA, Genetics, Pasteurellaceae and Phylogenetic tree.
His biological study focuses on Serotype.
His research integrates issues of Ribosomal RNA, Actinobacillus, Sequence analysis and Ribosomal DNA in his study of 16S ribosomal RNA.
Taxonomy, Genus and Actinobacillus suis is closely connected to Subspecies in his research, which is encompassed under the umbrella topic of Genetics.
His Pasteurellaceae study combines topics in areas such as Bacterial taxonomy, Computational biology and Genomics.
His Phylogenetic tree research incorporates themes from Shigella, Phylogenetics and Salmonella bongori.
His most cited work include:
- Proposed minimal standards for the use of genome data for the taxonomy of prokaryotes (978 citations)
- DNA-DNA hybridization determined in micro-wells using covalent attachment of DNA. (172 citations)
- Phylogeny of the family Pasteurellaceae based on rpoB sequences. (152 citations)
What are the main themes of his work throughout his whole career to date?
Henrik Christensen mostly deals with Feed additive, Microbiology, Food science, Animal science and Animal feed.
The various areas that Henrik Christensen examines in his Microbiology study include Genetics, 16S ribosomal RNA, Phylogenetic tree and Pasteurella multocida.
Henrik Christensen combines subjects such as Ribotyping, Ribosomal RNA and Actinobacillus with his study of 16S ribosomal RNA.
He focuses mostly in the field of Pasteurella multocida, narrowing it down to topics relating to Multilocus sequence typing and, in certain cases, Pulsed-field gel electrophoresis and Veterinary medicine.
Henrik Christensen interconnects Bacillus subtilis, Lysine, Saccharomyces cerevisiae and Recombinant DNA in the investigation of issues within Food science.
His research in Pasteurellaceae intersects with topics in Taxonomy and Pasteurella.
He most often published in these fields:
- Feed additive (32.23%)
- Microbiology (26.86%)
- Food science (26.65%)
What were the highlights of his more recent work (between 2018-2021)?
- Feed additive (32.23%)
- Food science (26.65%)
- Animal science (13.02%)
In recent papers he was focusing on the following fields of study:
Feed additive, Food science, Animal science, Animal feed and Authorization are his primary areas of study.
Feed additive combines with fields such as Weaned piglets, Porcine species, Genetically modified organism, Context and Phytase in his investigation.
His research on Food science focuses in particular on Fermentation.
His Fermentation research incorporates elements of Amino acid, Recombinant DNA and Escherichia coli.
His study on Coccidiostat is often connected to Laying and Komagataella phaffii as part of broader study in Animal science.
Henrik Christensen merges Authorization with Biotechnology in his research.
Between 2018 and 2021, his most popular works were:
- Guidance on the assessment of the safety of feed additives for the environment. (40 citations)
- Diversity and Population Overlap between Avian and Human Escherichia coli Belonging to Sequence Type 95 (20 citations)
- Safety and efficacy of copper chelates of lysine and glutamic acid as a feed additive for all animal species (14 citations)
In his most recent research, the most cited papers focused on:
Henrik Christensen mainly focuses on Food science, Feed additive, Animal science, Amino acid and Fermentation.
His research links Lactobacillus farciminis with Food science.
His work on Coccidiostat as part of general Animal science study is frequently linked to Phytase, Authorization and Weaned piglets, therefore connecting diverse disciplines of science.
In his study, Lysine and Rumen is inextricably linked to Corynebacterium glutamicum, which falls within the broad field of Amino acid.
His biological study spans a wide range of topics, including Sulfate and Strain.
His study in Fermentation is interdisciplinary in nature, drawing from both Recombinant DNA, Valine and Escherichia coli.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
