Institution
Yerevan State University
Education•Yerevan, Armenia•
About: Yerevan State University is a education organization based out in Yerevan, Armenia. It is known for research contribution in the topics: Magnetic field & Electron. The organization has 2860 authors who have published 5056 publications receiving 46619 citations. The organization is also known as: YSU.
Topics: Magnetic field, Electron, Laser, Field (physics), Liquid crystal
Papers published on a yearly basis
Papers
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Jeffrey D. Stanaway1, Ashkan Afshin1, Emmanuela Gakidou1, Stephen S Lim1 +1050 more•Institutions (346)
TL;DR: This study estimated levels and trends in exposure, attributable deaths, and attributable disability-adjusted life-years (DALYs) by age group, sex, year, and location for 84 behavioural, environmental and occupational, and metabolic risks or groups of risks from 1990 to 2017 and explored the relationship between development and risk exposure.
2,910 citations
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TL;DR: It is found that the risk of all-cause mortality, and of cancers specifically, rises with increasing levels of consumption, and the level of consumption that minimises health loss is zero.
1,831 citations
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University of Copenhagen1, University of Gothenburg2, Technical University of Denmark3, Leiden University4, Lund University5, University of Oxford6, University of Wrocław7, University of Zurich8, Wrocław Medical University9, University of Toronto10, Gorno-Altaisk State University11, South Ural State University12, Polish Academy of Sciences13, Ludwig Maximilian University of Munich14, Eötvös Loránd University15, Hungarian Natural History Museum16, Hungarian Academy of Sciences17, Masaryk University18, Academy of Sciences of the Czech Republic19, University of Tartu20, Yerevan State University21, Hungarian National Museum22, University of Szeged23, University of Wisconsin-Madison24, Russian Academy of Sciences25, First Faculty of Medicine, Charles University in Prague26, Armenian National Academy of Sciences27, Moscow State University28, University of California, Berkeley29
TL;DR: It is shown that the Bronze Age was a highly dynamic period involving large-scale population migrations and replacements, responsible for shaping major parts of present-day demographic structure in both Europe and Asia.
Abstract: The Bronze Age of Eurasia (around 3000-1000 BC) was a period of major cultural changes. However, there is debate about whether these changes resulted from the circulation of ideas or from human migrations, potentially also facilitating the spread of languages and certain phenotypic traits. We investigated this by using new, improved methods to sequence low-coverage genomes from 101 ancient humans from across Eurasia. We show that the Bronze Age was a highly dynamic period involving large-scale population migrations and replacements, responsible for shaping major parts of present-day demographic structure in both Europe and Asia. Our findings are consistent with the hypothesized spread of Indo-European languages during the Early Bronze Age. We also demonstrate that light skin pigmentation in Europeans was already present at high frequency in the Bronze Age, but not lactose tolerance, indicating a more recent onset of positive selection on lactose tolerance than previously thought.
1,088 citations
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Broad Institute1, Whitman College2, Simon Fraser University3, Howard Hughes Medical Institute4, University College Dublin5, University of Coimbra6, Emory University7, Chinese Academy of Sciences8, University of Ferrara9, University of Miskolc10, Armenian National Academy of Sciences11, University of Pennsylvania12, University of Winnipeg13, Alexandru Ioan Cuza University14, University of Edinburgh15, Royal College of Surgeons in Ireland16, Spanish National Research Council17, Imperial College London18, Max Planck Society19, Binghamton University20, University of Huddersfield21, University of Pavia22, Yerevan State University23
TL;DR: This paper reported genome-wide ancient DNA from 44 ancient Near Easterners ranging in time between ~12,000 and 1,400 bc, from Natufian hunter-gatherers to Bronze Age farmers, showing that the earliest populations of the Near East derived around half their ancestry from a 'Basal Eurasian' lineage that had little if any Neanderthal admixture and that separated from other non-African lineages before their separation from each other.
Abstract: We report genome-wide ancient DNA from 44 ancient Near Easterners ranging in time between ~12,000 and 1,400 bc, from Natufian hunter–gatherers to Bronze Age farmers. We show that the earliest populations of the Near East derived around half their ancestry from a ‘Basal Eurasian’ lineage that had little if any Neanderthal admixture and that separated from other non-African lineages before their separation from each other. The first farmers of the southern Levant (Israel and Jordan) and Zagros Mountains (Iran) were strongly genetically differentiated, and each descended from local hunter–gatherers. By the time of the Bronze Age, these two populations and Anatolian-related farmers had mixed with each other and with the hunter–gatherers of Europe to greatly reduce genetic differentiation. The impact of the Near Eastern farmers extended beyond the Near East: farmers related to those of Anatolia spread westward into Europe; farmers related to those of the Levant spread southward into East Africa; farmers related to those of Iran spread northward into the Eurasian steppe; and people related to both the early farmers of Iran and to the pastoralists of the Eurasian steppe spread eastward into South Asia.
695 citations
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TL;DR: This work proposes four clinical prediction benchmarks using data derived from the publicly available Medical Information Mart for Intensive Care (MIMIC-III) database, covering a range of clinical problems including modeling risk of mortality, forecasting length of stay, detecting physiologic decline, and phenotype classification.
Abstract: Health care is one of the most exciting frontiers in data mining and machine learning. Successful adoption of electronic health records (EHRs) created an explosion in digital clinical data available for analysis, but progress in machine learning for healthcare research has been difficult to measure because of the absence of publicly available benchmark data sets. To address this problem, we propose four clinical prediction benchmarks using data derived from the publicly available Medical Information Mart for Intensive Care (MIMIC-III) database. These tasks cover a range of clinical problems including modeling risk of mortality, forecasting length of stay, detecting physiologic decline, and phenotype classification. We propose strong linear and neural baselines for all four tasks and evaluate the effect of deep supervision, multitask training and data-specific architectural modifications on the performance of neural models.
504 citations
Authors
Showing all 2929 results
Name | H-index | Papers | Citations |
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John B Ketterson | 60 | 814 | 16929 |
Frederick D. Lewis | 57 | 390 | 11049 |
Paulo J.M. Monteiro | 56 | 315 | 14804 |
Hiromasa Ito | 45 | 424 | 7568 |
Kodo Kawase | 41 | 389 | 7813 |
Armen Sedrakian | 40 | 186 | 4859 |
Massimo Turatto | 39 | 58 | 8466 |
Arshak Poghossian | 39 | 170 | 4632 |
Yury Popov | 35 | 75 | 4281 |
Armen Trchounian | 33 | 224 | 3674 |
Aram A. Saharian | 31 | 289 | 3988 |
Armen Nersessian | 28 | 148 | 2471 |
Torsten Fiebig | 28 | 58 | 2691 |
Ara A. Asatryan | 28 | 130 | 2318 |
Sergei Sarkissian | 27 | 61 | 3561 |