313 research outputs found
ReInform: Selecting paths with reinforcement learning for contextualized link prediction
We propose to use reinforcement learning to inform transformer-based
contextualized link prediction models by providing paths that are most useful
for predicting the correct answer. This is in contrast to previous approaches,
that either used reinforcement learning (RL) to directly search for the answer,
or based their prediction on limited or randomly selected context. Our
experiments on WN18RR and FB15k-237 show that contextualized link prediction
models consistently outperform RL-based answer search, and that additional
improvements (of up to 13.5\% MRR) can be gained by combining RL with a link
prediction model
Scorzonera sensu lato (Asteraceae, Cichorieae) β taxonomic reassessment in the light of new molecular phylogenetic and carpological analyses
Scorzonera comprises 180β190 species and belongs to the subtribe Scorzonerinae. Its circumscription has long been the subject of debate and available molecular phylogenetic analyses affirmed the polyphyly of Scorzonera in its wide sense. We provide a re-evaluation of Scorzonera and other related genera, based on carpological (including anatomical) and extended molecular phylogenetic analyses. We present, for the first time, a comprehensive sampling, including Scorzonera in its widest sense and all other genera recognised in the Scorzonerinae. We conducted phylogenetic analyses using Maximum Parsimony, Maximum Likelihood and Bayesian analyses, based on sequences of the nuclear ribosomal ITS and of two plastid markers (partial rbcL and matK) and Maximum Parsimony for reconstructing the carpological character states at ancestral nodes. Achene characters, especially related to pericarp anatomy, such as general topography of the tissue types, disposition of the mechanical tissue and direction of its fibres, presence or absence of air cavities, provide, in certain cases, support for the phylogenetic lineages revealed. Confirming the polyphyly of Scorzonera, we propose a revised classification of the subtribe, accepting the genera Scorzonera (including four major clades: Scorzonera s. str., S. purpurea, S. albicaulis and Podospermum), Gelasia, Lipschitzia gen. nov. (for the Scorzonera divaricata clade), Pseudopodospermum, Pterachaenia (also including Scorzonera codringtonii), Ramaliella gen. nov. (for the S. polyclada clade) and Takhtajaniantha. A key to the revised genera and a characterisation of the genera and major clades are provided
Mounting Replicas in Stories of Angara Area Residents as Compositional Feature of Oral Narrative
Compositional technique, which is regularly found in the oral stories (narratives) of natives and residents of areas of the Angara river, - insert replicas, which are essentially collapsed themes (micro-themes) are considered for the first time. It is noted that insert have a complete composition. It is argued that the appearance of such speech structures is determined by the high importance of designated content for the narrator. The author defines the structure of the narrative as a relatively arbitrary: inset replicas appear in the place of the narrative, which seems appropriate to the speaker. The presence of such micro-thematic inserts allowed the author to assume that the informant, telling about one event or period of his / her life, at the same time implies a general picture of the narrative, that is, correlates the content of the replica with a holistic view of himself in the opposition βpart - wholeβ. For example, as it was shown by the analysis of the collected material, the appearance of insert remarks about the death of relatives (a kind of folded βtexts of deathβ) is dictated not by the logic and the topic of conversation, but by the metha-communicative task of the speaker. The narrator seems to perform mandatory labeling of the main stages of the human life cycle: birth - living - death. The author calls the content of such replicas micro-genealogy, as they are a brief mention of all the family members
Climatically driven loss of calcium in steppe soil as a sink for atmospheric carbon
During the last several thousand years the semiβarid, cold climate of the Russian steppe formed highly fertile soils rich in organic carbon and calcium (classified as Chernozems in the Russian system). Analysis of archived soil samples collected in Kemannaya Steppe Preserve in 1920, 1947, 1970, and fresh samples collected in 1998 indicated that the native steppe Chernozems, however, lost 17β28 kg mβ2 of calcium in the form of carbonates in 1970β1998. Here we demonstrate that the loss of calcium was caused by fundamental shift in the steppe hydrologic balance. Previously unleached soils where precipitation was less than potential evapotranspiration are now being leached due to increased precipitation and, possibly, due to decreased actual evapotranspiration. Because this region receives low levels of acidic deposition, the dissolution of carbonates involves the consumption of atmospheric CO2. Our estimates indicate that this climatically driven terrestrial sink of atmospheric CO2 is βΌ2.1β7.4 g C mβ2 aβ1. In addition to the net sink of atmospheric carbon, leaching of pedogenic carbonates significantly amplified seasonal amplitude of CO2 exchange between atmosphere and steppe soil
18-Year Land-Surface Hydrology Model Simulations for a Midlatitude Grassland Catchment in Valdai, Russia
Off-line simulations of improved bucket hydrology and Simplified Simple Biosphere (SSiB) models are performed for a grassland vegetation catchment region, located at the Valdai water-balance research station in Russia, forced by observed meteorological and simulated actinometric data for 1966-83. Evaluation of the model simulations is performed using observations of total soil moisture in the top 1 m, runoff, evaporation, snow depth, and water-table depth made within the catchment. The Valdai study demonstrates that using only routine meteorological measurements, long-term simulations of land-surface schemes suitable for model evaluation can be made. The Valdai dataset is available for use in the evaluation of other land-surface schemes. Both the SSiB and the bucket models reproduce the observed hydrology averaged over the simulation period (1967-83) and its interannual variability reasonably well. However, the models' soil moisture interannual variability is too low during the fall and winter when compared to observations. In addition, some discrepancies in the models' seasonal behavior with respect to observations are seen. The models are able to reproduce extreme hydrological events to some degree, but some inconsistencies in the model mechanisms are seen. The bucket model's soil-moisture variability is limited by its inability to rise above its prescribed field capacity for the case where the observed water table rises into the top 1-m layer of soil, which can lead to erroneous simulations of evaporation and runoff. SSiB's snow depth simulations are generally too low due to high evaporation from the snow surface. SSiB typically produces drainage out of its bottom layer during the summer, which appears inconsistent to the runoff observations of the catchment
ΠΡΡΠ΅Π²ΡΠ΅ ΠΏΡΠΎΡΠ²Π»Π΅Π½ΠΈΡ Π½ΠΎΠ²ΠΎΠΉ ΠΊΠΎΡΠΎΠ½Π°Π²ΠΈΡΡΡΠ½ΠΎΠΉ ΠΈΠ½ΡΠ΅ΠΊΡΠΈΠΈ COVID-19
Purpose. To evaluate the radiological patterns of a new COVID-19 coronavirus infection. Materials and methods. Review of literature sources. Results. COVID-19 causes the acute severe viral pneumonia. Radiological diagnostics of COVID-19 is very important, because CT can be the first study that shows the signs of viral lung lesion, and allows to assess the severity of the lesion and adverse prognostic signs of its further development. The initial CT pattern of COVID-19 is a pattern of infiltration of secondary pulmonary lobules on the type of Β«frosted glassΒ» (a symptom of Β«dry leafΒ») with a subsequent decrease in the volume of lesions at favorable results, or their increase, accession of CT pattern of Β«cobblestone roadΒ» and the appearance inΒ the area of Β«frosted glassΒ» the alveolar infiltration in unfavorable course of disease. These symptoms are the precursors to the development of respiratory distress syndrome. At a later primary examination, the primary CT symptoms are the pattern of Β«cobblestone roadΒ» and areas of alveolar infiltration, which correlates with an unfavorable further course and outcome. There was noted that viral pneumonia in COVID-19 was characterized by the location of changes in the posterior subpleural and peribronchial areas. All authors confirmed that cavities, nodules, pleural and pericardial effusions, and lymphadenopathy were absent in COVID-19. In the course of observation, quantitative characteristics of the lesions with a score were proposed, the use of which can help in determining the prognosis. Also identified temporary staging of the process and the formation in some of patients the residual changes in the lungs the same as in influenza pneumonia H1N1 (2008β9Π³Π³, 2015β16.) and SARS SARS-CoV-2 (2003)which can start the process of development of progressive pulmonary fibrosis. There is a need for frequent CT studies (every 4 days) to enable timely assessment of rapid dynamics and changes in treatment tactics. The analysis of the results of the examination should be performed by at least 2 radiologists experienced in thoracic radiology, with the involvement of a third independent expert, in case of disagreement. All the authors confirmed the low information content of traditional radiography in assessing viral lung lesions. In some studies, chest radiographs were not performed, only CT as a more sensitive method for detecting early changes, similar to previous outbreaks of coronavirus. However, the role of traditional radiography was recognized as unquestionable when evaluating changes in reanimation department conditions. Conclusions. The accumulation of experience in clinical and radiological examination of COVID-19 patients allowed to determine the radiological semiotics of the process, which is important for determining the treatment tactics.Π¦Π΅Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ: ΠΎΡΠ΅Π½ΠΈΡΡ Π»ΡΡΠ΅Π²ΡΠ΅ ΠΏΠ°ΡΡΠ΅ΡΠ½Ρ Π½ΠΎΠ²ΠΎΠΉ ΠΊΠΎΡΠΎΠ½Π°Π²ΠΈΡΡΡΠ½ΠΎΠΉ ΠΈΠ½ΡΠ΅ΠΊΡΠΈΠΈ COVID-19. ΠΠ°ΡΠ΅ΡΠΈΠ°Π»Ρ ΠΈ ΠΌΠ΅ΡΠΎΠ΄Ρ. ΠΠ±Π·ΠΎΡ Π»ΠΈΡΠ΅ΡΠ°ΡΡΡΠ½ΡΡ
ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠ². Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ. COVID-19 Π²ΡΠ·ΡΠ²Π°Π΅Ρ ΠΎΡΡΡΡΡ ΡΡΠΆΠ΅Π»ΡΡ ΡΠΎΡΠΌΡ Π²ΠΈΡΡΡΠ½ΠΎΠΉ ΠΏΠ½Π΅Π²ΠΌΠΎΠ½ΠΈΠΈ. ΠΡΡΠ΅Π²Π°Ρ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠ° COVID-19 ΠΎΡΠ΅Π½Ρ Π²Π°ΠΆΠ½Π°, ΡΠ°ΠΊ ΠΊΠ°ΠΊ ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½Π°Ρ ΡΠΎΠΌΠΎΠ³ΡΠ°ΡΠΈΡ (ΠΠ’) ΠΌΠΎΠΆΠ΅Ρ Π±ΡΡΡ ΠΏΠ΅ΡΠ²ΡΠΌ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ, ΠΊΠΎΡΠΎΡΠΎΠ΅ Π΄Π΅ΠΌΠΎΠ½ΡΡΡΠΈΡΡΠ΅Ρ ΠΏΡΠΈΠ·Π½Π°ΠΊΠΈ Π²ΠΈΡΡΡΠ½ΠΎΠ³ΠΎ ΠΏΠΎΡΠ°ΠΆΠ΅Π½ΠΈΡ Π»Π΅Π³ΠΊΠΈΡ
, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΠΎΡΠ΅Π½ΠΈΡΡ ΡΡΠΆΠ΅ΡΡΡ ΠΏΠΎΡΠ°ΠΆΠ΅Π½ΠΈΡ ΠΈ Π½Π΅Π±Π»Π°Π³ΠΎΠΏΡΠΈΡΡΠ½ΡΠ΅ ΠΏΡΠΎΠ³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΏΡΠΈΠ·Π½Π°ΠΊΠΈ Π΅Π³ΠΎ Π΄Π°Π»ΡΠ½Π΅ΠΉΡΠ΅Π³ΠΎ ΡΠ°Π·Π²ΠΈΡΠΈΡ. ΠΠ΅ΡΠ²ΠΈΡΠ½ΡΠΌ ΠΠ’-ΠΏΠ°ΡΡΠ΅ΡΠ½ΠΎΠΌ COVID-19 ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΊΠ°ΡΡΠΈΠ½Π° ΠΈΠ½ΡΠΈΠ»ΡΡΡΠ°ΡΠΈΠΈ ΠΎΡΠ΄Π΅Π»ΡΠ½ΡΡ
Π²ΡΠΎΡΠΈΡΠ½ΡΡ
Π»Π΅Π³ΠΎΡΠ½ΡΡ
Π΄ΠΎΠ»Π΅ΠΊ ΠΏΠΎ ΡΠΈΠΏΡ Β«ΠΌΠ°ΡΠΎΠ²ΠΎΠ³ΠΎ ΡΡΠ΅ΠΊΠ»Π°Β» (ΡΠΈΠΌΠΏΡΠΎΠΌ Β«ΡΡΡ
ΠΎΠ³ΠΎ Π»ΠΈΡΡΠ°Β») Ρ ΠΏΠΎΡΠ»Π΅Π΄ΡΡΡΠΈΠΌ ΡΠΌΠ΅Π½ΡΡΠ΅Π½ΠΈΠ΅ΠΌ ΠΎΠ±ΡΠ΅ΠΌΠ° ΠΏΠΎΡΠ°ΠΆΠ΅Π½ΠΈΡ ΠΏΡΠΈ Π±Π»Π°Π³ΠΎΠΏΡΠΈΡΡΠ½ΠΎΠΌ ΡΠ°Π·Π²ΠΈΡΠΈΠΈ ΡΠΎΠ±ΡΡΠΈΠΉ Π»ΠΈΠ±ΠΎ ΠΈΡ
Π½Π°ΡΠ°ΡΡΠ°Π½ΠΈΠΈ, ΠΏΡΠΈΡΠΎΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΠΈ ΠΠ’-ΠΊΠ°ΡΡΠΈΠ½Ρ Β«Π±ΡΠ»ΡΠΆΠ½ΠΎΠΉ ΠΌΠΎΡΡΠΎΠ²ΠΎΠΉΒ» ΠΈ ΠΏΠΎΡΠ²Π»Π΅Π½ΠΈΠΈ Π² Π·ΠΎΠ½Π΅ Β«ΠΌΠ°ΡΠΎΠ²ΠΎΠ³ΠΎ ΡΡΠ΅ΠΊΠ»Π°Β» Π°Π»ΡΠ²Π΅ΠΎΠ»ΡΡΠ½ΠΎΠΉ ΠΈΠ½ΡΠΈΠ»ΡΡΡΠ°ΡΠΈΠΈ ΠΏΡΠΈ Π½Π΅Π±Π»Π°Π³ΠΎΠΏΡΠΈΡΡΠ½ΠΎΠΌ Π²Π°ΡΠΈΠ°Π½ΡΠ΅ ΡΠ΅ΡΠ΅Π½ΠΈΡ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΡ. ΠΡΠΈ ΡΠΈΠΌΠΏΡΠΎΠΌΡ ΡΠ²Π»ΡΡΡΡΡ ΠΏΡΠ΅Π΄Π²Π΅ΡΡΠ½ΠΈΠΊΠ°ΠΌΠΈ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΠ΅ΡΠΏΠΈΡΠ°ΡΠΎΡΠ½ΠΎΠ³ΠΎ Π΄ΠΈΡΡΡΠ΅ΡΡ-ΡΠΈΠ½Π΄ΡΠΎΠΌΠ°. ΠΡΠΈ Π±ΠΎΠ»Π΅Π΅ ΠΏΠΎΠ·Π΄Π½Π΅ΠΌ ΠΏΠ΅ΡΠ²ΠΈΡΠ½ΠΎΠΌ ΠΎΠ±ΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΈ ΠΏΠ΅ΡΠ²ΠΈΡΠ½ΡΠΌΠΈ ΠΠ’-ΡΠΈΠΌΠΏΡΠΎΠΌΠ°ΠΌΠΈ ΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΡ ΠΏΠ°ΡΡΠ΅ΡΠ½ Β«Π±ΡΠ»ΡΠΆΠ½ΠΎΠΉ ΠΌΠΎΡΡΠΎΠ²ΠΎΠΉΒ» ΠΈ ΡΡΠ°ΡΡΠΊΠΈ Π°Π»ΡΠ²Π΅ΠΎΠ»ΡΡΠ½ΠΎΠΉ ΠΈΠ½ΡΠΈΠ»ΡΡΡΠ°ΡΠΈΠΈ, ΡΡΠΎ ΠΊΠΎΡΡΠ΅Π»ΠΈΡΡΠ΅Ρ Ρ Π½Π΅Π±Π»Π°Π³ΠΎΠΏΡΠΈΡΡΠ½ΡΠΌ Π΄Π°Π»ΡΠ½Π΅ΠΉΡΠΈΠΌ ΡΠ΅ΡΠ΅Π½ΠΈΠ΅ΠΌ ΠΈ ΠΈΡΡ
ΠΎΠ΄ΠΎΠΌ. ΠΡΠΌΠ΅ΡΠ΅Π½ΠΎ, ΡΡΠΎ Π΄Π»Ρ Π²ΠΈΡΡΡΠ½ΠΎΠΉ ΠΏΠ½Π΅Π²ΠΌΠΎΠ½ΠΈΠΈ ΠΏΡΠΈ COVID-19 Π±ΡΠ»ΠΎ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠ½ΠΎ ΡΠ°ΡΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠΉ Π² Π·Π°Π΄Π½ΠΈΡ
ΡΡΠ±ΠΏΠ»Π΅Π²ΡΠ°Π»ΡΠ½ΡΡ
ΠΈ ΠΏΠ΅ΡΠΈΠ±ΡΠΎΠ½Ρ
ΠΈΠ°Π»ΡΠ½ΡΡ
ΠΎΡΠ΄Π΅Π»Π°Ρ
. ΠΡΠ΅ Π°Π²ΡΠΎΡΡ ΠΏΠΎΠ΄ΡΠ²Π΅ΡΠΆΠ΄Π°Π»ΠΈ, ΡΡΠΎ ΠΏΠΎΠ»ΠΎΡΡΠΈ, ΡΠ·Π»ΠΎΠ²ΡΠ΅ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ, ΠΏΠ»Π΅Π²ΡΠ°Π»ΡΠ½ΡΠ΅ ΠΈ ΠΏΠ΅ΡΠΈΠΊΠ°ΡΠ΄ΠΈΠ°Π»ΡΠ½ΡΠ΅ Π²ΡΠΏΠΎΡΡ ΠΈ Π»ΠΈΠΌΡΠ°Π΄Π΅Π½ΠΎΠΏΠ°ΡΠΈΡ ΠΏΡΠΈ COVID-19 ΠΎΡΡΡΡΡΡΠ²ΠΎΠ²Π°Π»ΠΈ. Π ΠΏΡΠΎΡΠ΅ΡΡΠ΅ Π½Π°Π±Π»ΡΠ΄Π΅Π½ΠΈΡ Π±ΡΠ»ΠΈ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Ρ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΠ΅ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠΈ ΠΏΠΎΡΠ°ΠΆΠ΅Π½ΠΈΡ Ρ Π±Π°Π»Π»ΡΠ½ΠΎΠΉ ΠΎΡΠ΅Π½ΠΊΠΎΠΉ, ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΊΠΎΡΠΎΡΡΡ
ΠΌΠΎΠΆΠ΅Ρ ΠΏΠΎΠΌΠΎΡΡ Π² ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠΈ ΠΏΡΠΎΠ³Π½ΠΎΠ·Π°. Π’Π°ΠΊΠΆΠ΅ Π±ΡΠ»Π° ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π° Π²ΡΠ΅ΠΌΠ΅Π½Π½Π°Ρ ΡΡΠ°Π΄ΠΈΠΉΠ½ΠΎΡΡΡ ΠΏΡΠΎΡΠ΅ΡΡΠ° ΠΈ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ Ρ ΡΠ°ΡΡΠΈ Π±ΠΎΠ»ΡΠ½ΡΡ
ΠΎΡΡΠ°ΡΠΎΡΠ½ΡΡ
ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠΉ Π² Π»Π΅Π³ΠΊΠΈΡ
, ΠΊΠΎΡΠΎΡΡΠ΅, ΠΊΠ°ΠΊ ΠΏΡΠΈ Π³ΡΠΈΠΏΠΏΠΎΠ·Π½ΠΎΠΉ ΠΏΠ½Π΅Π²ΠΌΠΎΠ½ΠΈΠΈ H1N1 (2008β2019 Π³Π³., 2015β2016 Π³Π³.) ΠΈ Π°ΡΠΈΠΏΠΈΡΠ½ΠΎΠΉ ΠΏΠ½Π΅Π²ΠΌΠΎΠ½ΠΈΠΈ SARS-CoV-2 (2003 Π³.), ΠΌΠΎΠ³ΡΡ Π·Π°ΠΏΡΡΠΊΠ°ΡΡ ΠΏΡΠΎΡΠ΅ΡΡΡ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΠΏΡΠΎΠ³ΡΠ΅ΡΡΠΈΡΡΡΡΠ΅Π³ΠΎ Π»Π΅Π³ΠΎΡΠ½ΠΎΠ³ΠΎ ΡΠΈΠ±ΡΠΎΠ·Π°. ΠΡΠΌΠ΅ΡΠ°Π΅ΡΡΡ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΡ ΡΠ°ΡΡΠΎΠ³ΠΎ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ ΠΠ’-ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ (ΠΊΠ°ΠΆΠ΄ΡΠ΅ 4 Π΄Π½Ρ) Π΄Π»Ρ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠΈ ΡΠ²ΠΎΠ΅Π²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠΉ ΠΎΡΠ΅Π½ΠΊΠΈ Π±ΡΡΡΡΠΎΠΉ Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠΈ ΠΈ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ Π»Π΅ΡΠ΅Π±Π½ΠΎΠΉ ΡΠ°ΠΊΡΠΈΠΊΠΈ. ΠΠ½Π°Π»ΠΈΠ· ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ² ΠΎΠ±ΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π΄ΠΎΠ»ΠΆΠ½Ρ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΡΡ ΠΌΠΈΠ½ΠΈΠΌΡΠΌ Π΄Π²Π° ΡΠ΅Π½ΡΠ³Π΅Π½ΠΎΠ»ΠΎΠ³Π°, ΠΈΠΌΠ΅ΡΡΠΈΡ
ΠΎΠΏΡΡ ΡΠ°Π±ΠΎΡΡ Π² ΡΠΎΡΠ°ΠΊΠ°Π»ΡΠ½ΠΎΠΉ ΡΠ°Π΄ΠΈΠΎΠ»ΠΎΠ³ΠΈΠΈ, Ρ ΠΏΡΠΈΠ²Π»Π΅ΡΠ΅Π½ΠΈΠ΅ΠΌ ΡΡΠ΅ΡΡΠ΅Π³ΠΎ Π½Π΅Π·Π°Π²ΠΈΡΠΈΠΌΠΎΠ³ΠΎ ΡΠΊΡΠΏΠ΅ΡΡΠ°, Π² ΡΠ»ΡΡΠ°Π΅ ΡΠ°ΡΡ
ΠΎΠΆΠ΄Π΅Π½ΠΈΡ ΠΌΠ½Π΅Π½ΠΈΠΉ. ΠΡΠ΅ Π°Π²ΡΠΎΡΡ ΠΏΠΎΠ΄ΡΠ²Π΅ΡΠΆΠ΄Π°Π»ΠΈ Π½ΠΈΠ·ΠΊΡΡ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠ²Π½ΠΎΡΡΡ ΡΡΠ°Π΄ΠΈΡΠΈΠΎΠ½Π½ΠΎΠΉ ΡΠ΅Π½ΡΠ³Π΅Π½ΠΎΠ³ΡΠ°ΡΠΈΠΈ Π² ΠΎΡΠ΅Π½ΠΊΠ΅ Π²ΠΈΡΡΡΠ½ΠΎΠ³ΠΎ ΠΏΠΎΡΠ°ΠΆΠ΅Π½ΠΈΡ Π»Π΅Π³ΠΊΠΈΡ
, Π² Π½Π΅ΠΊΠΎΡΠΎΡΡΡ
ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡΡ
Π½Π΅ Π²ΡΠΏΠΎΠ»Π½ΡΠ»Π°ΡΡ ΡΠ΅Π½ΡΠ³Π΅Π½ΠΎΠ³ΡΠ°ΡΠΈΡ Π³ΡΡΠ΄Π½ΠΎΠΉ ΠΊΠ»Π΅ΡΠΊΠΈ, ΠΏΡΠΈΠΌΠ΅Π½ΡΠ»ΠΈ ΡΠΎΠ»ΡΠΊΠΎ ΠΠ’ ΠΊΠ°ΠΊ Π±ΠΎΠ»Π΅Π΅ ΡΡΠ²ΡΡΠ²ΠΈΡΠ΅Π»ΡΠ½ΡΠΉ ΠΌΠ΅ΡΠΎΠ΄ Π²ΡΡΠ²Π»Π΅Π½ΠΈΡ ΡΠ°Π½Π½ΠΈΡ
ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠΉ, ΠΏΠΎ Π°Π½Π°Π»ΠΎΠ³ΠΈΠΈ Ρ ΠΏΡΠ΅Π΄ΡΠ΄ΡΡΠΈΠΌΠΈ Π²ΡΠΏΡΡΠΊΠ°ΠΌΠΈ ΠΊΠΎΡΠΎΠ½Π°Π²ΠΈΡΡΡΠ°. ΠΠ΄Π½Π°ΠΊΠΎ ΡΠΎΠ»Ρ ΡΡΠ°Π΄ΠΈΡΠΈΠΎΠ½Π½ΠΎΠΉ ΡΠ΅Π½ΡΠ³Π΅Π½ΠΎΠ³ΡΠ°ΡΠΈΠΈ ΠΏΡΠΈΠ·Π½Π°Π²Π°Π»Π°ΡΡ Π½Π΅ΡΠΎΠΌΠ½Π΅Π½Π½ΠΎΠΉ ΠΏΡΠΈ ΠΎΡΠ΅Π½ΠΊΠ΅ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠΉ Π² ΡΡΠ»ΠΎΠ²ΠΈΡΡ
ΡΠ΅Π°Π½ΠΈΠΌΠ°ΡΠΈΠΈ. ΠΡΠ²ΠΎΠ΄Ρ. ΠΠ°ΠΊΠΎΠΏΠ»Π΅Π½ΠΈΠ΅ ΠΎΠΏΡΡΠ° ΠΊΠ»ΠΈΠ½ΠΈΠΊΠΎ-Π»ΡΡΠ΅Π²ΠΎΠ³ΠΎ ΠΎΠ±ΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π±ΠΎΠ»ΡΠ½ΡΡ
COVID-19 ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΠ» ΠΎΠΏΡΠ΅Π΄Π΅Π»ΠΈΡΡ Π»ΡΡΠ΅Π²ΡΡ ΡΠ΅ΠΌΠΈΠΎΡΠΈΠΊΡ ΠΏΡΠΎΡΠ΅ΡΡΠ°, Π²Π°ΠΆΠ½ΡΡ Π΄Π»Ρ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ Π»Π΅ΡΠ΅Π±Π½ΠΎΠΉ ΡΠ°ΠΊΡΠΈΠΊΠΈ
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