Institution
National University of Mongolia
Education•Ulaanbaatar, Mongolia•
About: National University of Mongolia is a education organization based out in Ulaanbaatar, Mongolia. It is known for research contribution in the topics: Population & Oribatida. The organization has 950 authors who have published 1323 publications receiving 15108 citations. The organization is also known as: NUM & MUIS.
Topics: Population, Oribatida, Steppe, Climate change, Species richness
Papers published on a yearly basis
Papers
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TL;DR: A model ofTLR4-MD-2 dimerization induced by LPS is proposed and a series of hybrids of human TLR4 and hagfish VLR are produced and their structures with and without bound MD-2 and Eritoran are determined.
1,063 citations
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TL;DR: The results demonstrate that without proper waste management, low-density populations can heavily pollute freshwater systems with consumer plastics.
977 citations
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TL;DR: The super-repeat in the TLR7 family suggests strongly that "bacterial" and "typical" LRRs evolved from a common precursor and is inferred to play a key role in the structure and/or function of their TLRs.
Abstract: Toll-like receptors (TLRs) play a central role in innate immunity. TLRs are membrane glycoproteins and contain leucine rich repeat (LRR) motif in the ectodomain. TLRs recognize and respond to molecules such as lipopolysaccharide, peptidoglycan, flagellin, and RNA from bacteria or viruses. The LRR domains in TLRs have been inferred to be responsible for molecular recognition. All LRRs include the highly conserved segment, LxxLxLxxNxL, in which "L" is Leu, Ile, Val, or Phe and "N" is Asn, Thr, Ser, or Cys and "x" is any amino acid. There are seven classes of LRRs including "typical" ("T") and "bacterial" ("S"). All known domain structures adopt an arc or horseshoe shape. Vertebrate TLRs form six major families. The repeat numbers of LRRs and their "phasing" in TLRs differ with isoforms and species; they are aligned differently in various databases. We identified and aligned LRRs in TLRs by a new method described here. The new method utilizes known LRR structures to recognize and align new LRR motifs in TLRs and incorporates multiple sequence alignments and secondary structure predictions. TLRs from thirty-four vertebrate were analyzed. The repeat numbers of the LRRs ranges from 16 to 28. The LRRs found in TLRs frequently consists of LxxLxLxxNxLxxLxxxxF/LxxLxx ("T") and sometimes short motifs including LxxLxLxxNxLxxLPx(x)LPxx ("S"). The TLR7 family (TLR7, TLR8, and TLR9) contain 27 LRRs. The LRRs at the N-terminal part have a super-motif of STT with about 80 residues. The super-repeat is represented by STTSTTSTT or _TTSTTSTT. The LRRs in TLRs form one or two horseshoe domains and are mostly flanked by two cysteine clusters including two or four cysteine residue. Each of the six major TLR families is characterized by their constituent LRR motifs, their repeat numbers, and their patterns of cysteine clusters. The central parts of the TLR1 and TLR7 families and of TLR4 have more irregular or longer LRR motifs. These central parts are inferred to play a key role in the structure and/or function of their TLRs. Furthermore, the super-repeat in the TLR7 family suggests strongly that "bacterial" and "typical" LRRs evolved from a common precursor.
352 citations
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Thompson Rivers University1, University of British Columbia2, University of Bayreuth3, Tel Aviv University4, University of Tartu5, University of Tehran6, University of Western Australia7, University of Alberta8, University of Kansas9, National University of Mongolia10, Universidade Federal do Rio Grande do Sul11, University of South Africa12, National University of Cordoba13, University of Camerino14, Trinity College, Dublin15, Sao Paulo State University16, Syracuse University17, University of Akron18, University of Wyoming19, University of Western Ontario20, Corvinus University of Budapest21, Islamic Azad University22, University of Otago23, Lanzhou University24, University of Bern25, Ferdowsi University of Mashhad26, University of Florida27, Princeton University28, Hirosaki University29, Szent István University30, University of Pretoria31, Free University of Bozen-Bolzano32, Landcare Research33
TL;DR: In this paper, by using data from coordinated surveys conducted throughout grasslands worldwide and comprising a wide range of site productivities, the authors provide evidence in support of the humped-back model (HBM) pattern at both global and regional extents.
Abstract: The search for predictions of species diversity across environmental gradients has challenged ecologists for decades The humped-back model (HBM) suggests that plant diversity peaks at intermediate productivity; at low productivity few species can tolerate the environmental stresses, and at high productivity a few highly competitive species dominate Over time the HBM has become increasingly controversial, and recent studies claim to have refuted it Here, by using data from coordinated surveys conducted throughout grasslands worldwide and comprising a wide range of site productivities, we provide evidence in support of the HBM pattern at both global and regional extents The relationships described here provide a foundation for further research into the local, landscape, and historical factors that maintain biodiversity
308 citations
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University of Copenhagen1, Paris Diderot University2, Technical University of Denmark3, Leiden University4, University of Cambridge5, Stanford University6, Karagandy State University7, University of Oslo8, Russian Academy of Sciences9, National Academy of Sciences10, Irkutsk State University11, Mongolian State University of Agriculture12, National University of Mongolia13, Tuvan State University14, Radcliffe Institute for Advanced Study15, Hashemite University16, Children's Hospital of Philadelphia17, Russian-Armenian (Slavonic) University18, Armenian National Academy of Sciences19, University of Arizona20, Slovak Academy of Sciences21, First Faculty of Medicine, Charles University in Prague22, University of Gothenburg23, Arizona State University24, University of Alberta25, Paul Sabatier University26, University of California, Berkeley27, Wellcome Trust Sanger Institute28
TL;DR: The genomes of 137 ancient and 502 modern human genomes illuminate the population history of the Eurasian steppes after the Bronze Age and document the replacement of Indo-European speakers of West Eurasian ancestry by Turkic-speaking groups of East Asian ancestry.
Abstract: For thousands of years the Eurasian steppes have been a centre of human migrations and cultural change. Here we sequence the genomes of 137 ancient humans (about 1× average coverage), covering a period of 4,000 years, to understand the population history of the Eurasian steppes after the Bronze Age migrations. We find that the genetics of the Scythian groups that dominated the Eurasian steppes throughout the Iron Age were highly structured, with diverse origins comprising Late Bronze Age herders, European farmers and southern Siberian hunter-gatherers. Later, Scythians admixed with the eastern steppe nomads who formed the Xiongnu confederations, and moved westward in about the second or third century bc, forming the Hun traditions in the fourth–fifth century ad, and carrying with them plague that was basal to the Justinian plague. These nomads were further admixed with East Asian groups during several short-term khanates in the Medieval period. These historical events transformed the Eurasian steppes from being inhabited by Indo-European speakers of largely West Eurasian ancestry to the mostly Turkic-speaking groups of the present day, who are primarily of East Asian ancestry. Sequences of 137 ancient and 502 modern human genomes illuminate the population history of the Eurasian steppes after the Bronze Age and document the replacement of Indo-European speakers of West Eurasian ancestry by Turkic-speaking groups of East Asian ancestry.
280 citations
Authors
Showing all 961 results
Name | H-index | Papers | Citations |
---|---|---|---|
Jiří Šponer | 73 | 329 | 19600 |
Christoph Leuschner | 69 | 406 | 17610 |
Gordon C. Jacoby | 55 | 111 | 9531 |
Martin Pfeiffer | 42 | 156 | 6699 |
Markus Hauck | 40 | 150 | 4117 |
Jiaojun Zhu | 30 | 161 | 2937 |
Takeshi Nakano | 26 | 69 | 3291 |
Choimaa Dulamsuren | 26 | 62 | 1645 |
Maher Moakher | 24 | 60 | 3497 |
Kirk A. Olson | 23 | 39 | 2264 |
Bazartseren Boldgiv | 20 | 64 | 2371 |
Leiv K. Sydnes | 20 | 180 | 1926 |
Ochbadrakh Chuluunbaatar | 19 | 165 | 1267 |
Sender Dovchin | 16 | 56 | 611 |
Ts. Gantsog | 15 | 31 | 483 |