DUSHA (“SOUL”) and TELO (“BODY”) in the Aspect of Contrastive Analysis of Cultural Concepts: Materials for the Linguo-Cultorological Phraseological Dictionary of Russian-Kazakh Correlations

Some preliminary results of joint research lexicographic project of Russian and Kazakh linguists on linguo-cultorological contrastive analysis of Russian-Kazakh phraseological correlations are discussed in the article. The work presents the experience of contrastive lexicographic description of the concepts ZHAN - DUSHA (“SOUL”) and DENE / BOY - TELO (“BODY”) in the aspect of their language objectification in Russian and Kazakh phraseologisms. The purpose is to give contrastive analysis of the opposite concepts ZHAN - DUSHA (“SOUL”) and DENE / BOY - TELO (“BODY”) in their phraseological representation in Russian and Kazakh. The material for the study is the data of Russian and Kazakh phraseological dictionaries. The theoretical basis of the work is the ideas of language conceptualization of the world, the principles cognitive interpretation of elements of “a language of culture” and the methodology of linguo-cultorological study in phraseologisms. The standard method of conceptual analysis and the method of contrastive phraseological analysis are used un the article. It is shown that there are a lot of substantial resemblances in phraseological representations of the concepts in Russian and Kazakh lingual cultures resulting from general principles of language conceptualization of reality in languages of the world. On the other side, some conceptual divergences between compared languages are also revealed: they are caused by peculiarities of nature and culture environment as well as by diversity in changing of nominations for imaginative denotation of approximately analogous substance. The authors come at a conclusion that the contrary concepts ZHAN - DUSHA (“SOUL”) and DENE / BOY - TELO (“BODY”) are revealed in the mirror of each other in phraseology of compared languages

BioTIME: A database of biodiversity time series for the Anthropocene.

MotivationThe BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community-led open-source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene.Main types of variables includedThe database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of 2 years, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology and contextual information about each record.Spatial location and grainBioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km2 (158 cm2) to 100 km2 (1,000,000,000,000 cm2).Time period and grainBioTIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year.Major taxa and level of measurementBioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates.Software format.csv and .SQL

Primary stroke prevention worldwide : translating evidence into action

Funding Information: The stroke services survey reported in this publication was partly supported by World Stroke Organization and Auckland University of Technology. VLF was partly supported by the grants received from the Health Research Council of New Zealand. MOO was supported by the US National Institutes of Health (SIREN U54 HG007479) under the H3Africa initiative and SIBS Genomics (R01NS107900, R01NS107900-02S1, R01NS115944-01, 3U24HG009780-03S5, and 1R01NS114045-01), Sub-Saharan Africa Conference on Stroke Conference (1R13NS115395-01A1), and Training Africans to Lead and Execute Neurological Trials & Studies (D43TW012030). AGT was supported by the Australian National Health and Medical Research Council. SLG was supported by a National Heart Foundation of Australia Future Leader Fellowship and an Australian National Health and Medical Research Council synergy grant. We thank Anita Arsovska (University Clinic of Neurology, Skopje, North Macedonia), Manoj Bohara (HAMS Hospital, Kathmandu, Nepal), Denis ?erimagi? (Poliklinika Glavi?, Dubrovnik, Croatia), Manuel Correia (Hospital de Santo Ant?nio, Porto, Portugal), Daissy Liliana Mora Cuervo (Hospital Moinhos de Vento, Porto Alegre, Brazil), Anna Cz?onkowska (Institute of Psychiatry and Neurology, Warsaw, Poland), Gloria Ekeng (Stroke Care International, Dartford, UK), Jo?o Sargento-Freitas (Centro Hospitalar e Universit?rio de Coimbra, Coimbra, Portugal), Yuriy Flomin (MC Universal Clinic Oberig, Kyiv, Ukraine), Mehari Gebreyohanns (UT Southwestern Medical Centre, Dallas, TX, USA), Ivete Pillo Gon?alves (Hospital S?o Jos? do Avai, Itaperuna, Brazil), Claiborne Johnston (Dell Medical School, University of Texas, Austin, TX, USA), Kristaps Jurj?ns (P Stradins Clinical University Hospital, Riga, Latvia), Rizwan Kalani (University of Washington, Seattle, WA, USA), Grzegorz Kozera (Medical University of Gda?sk, Gda?sk, Poland), Kursad Kutluk (Dokuz Eylul University, ?zmir, Turkey), Branko Malojcic (University Hospital Centre Zagreb, Zagreb, Croatia), Micha? Maluchnik (Ministry of Health, Warsaw, Poland), Evija Migl?ne (P Stradins Clinical University Hospital, Riga, Latvia), Cassandra Ocampo (University of Botswana, Princess Marina Hospital, Botswana), Louise Shaw (Royal United Hospitals Bath NHS Foundation Trust, Bath, UK), Lekhjung Thapa (Upendra Devkota Memorial-National Institute of Neurological and Allied Sciences, Kathmandu, Nepal), Bogdan Wojtyniak (National Institute of Public Health, Warsaw, Poland), Jie Yang (First Affiliated Hospital of Chengdu Medical College, Chengdu, China), and Tomasz Zdrojewski (Medical University of Gda?sk, Gda?sk, Poland) for their comments on early draft of the manuscript. The views expressed in this article are solely the responsibility of the authors and they do not necessarily reflect the views, decisions, or policies of the institution with which they are affiliated. We thank WSO for funding. The funder had no role in the design, data collection, analysis and interpretation of the study results, writing of the report, or the decision to submit the study results for publication. Funding Information: The stroke services survey reported in this publication was partly supported by World Stroke Organization and Auckland University of Technology. VLF was partly supported by the grants received from the Health Research Council of New Zealand. MOO was supported by the US National Institutes of Health (SIREN U54 HG007479) under the H3Africa initiative and SIBS Genomics (R01NS107900, R01NS107900-02S1, R01NS115944-01, 3U24HG009780-03S5, and 1R01NS114045-01), Sub-Saharan Africa Conference on Stroke Conference (1R13NS115395-01A1), and Training Africans to Lead and Execute Neurological Trials & Studies (D43TW012030). AGT was supported by the Australian National Health and Medical Research Council. SLG was supported by a National Heart Foundation of Australia Future Leader Fellowship and an Australian National Health and Medical Research Council synergy grant. We thank Anita Arsovska (University Clinic of Neurology, Skopje, North Macedonia), Manoj Bohara (HAMS Hospital, Kathmandu, Nepal), Denis Čerimagić (Poliklinika Glavić, Dubrovnik, Croatia), Manuel Correia (Hospital de Santo António, Porto, Portugal), Daissy Liliana Mora Cuervo (Hospital Moinhos de Vento, Porto Alegre, Brazil), Anna Członkowska (Institute of Psychiatry and Neurology, Warsaw, Poland), Gloria Ekeng (Stroke Care International, Dartford, UK), João Sargento-Freitas (Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal), Yuriy Flomin (MC Universal Clinic Oberig, Kyiv, Ukraine), Mehari Gebreyohanns (UT Southwestern Medical Centre, Dallas, TX, USA), Ivete Pillo Gonçalves (Hospital São José do Avai, Itaperuna, Brazil), Claiborne Johnston (Dell Medical School, University of Texas, Austin, TX, USA), Kristaps Jurjāns (P Stradins Clinical University Hospital, Riga, Latvia), Rizwan Kalani (University of Washington, Seattle, WA, USA), Grzegorz Kozera (Medical University of Gdańsk, Gdańsk, Poland), Kursad Kutluk (Dokuz Eylul University, İzmir, Turkey), Branko Malojcic (University Hospital Centre Zagreb, Zagreb, Croatia), Michał Maluchnik (Ministry of Health, Warsaw, Poland), Evija Miglāne (P Stradins Clinical University Hospital, Riga, Latvia), Cassandra Ocampo (University of Botswana, Princess Marina Hospital, Botswana), Louise Shaw (Royal United Hospitals Bath NHS Foundation Trust, Bath, UK), Lekhjung Thapa (Upendra Devkota Memorial-National Institute of Neurological and Allied Sciences, Kathmandu, Nepal), Bogdan Wojtyniak (National Institute of Public Health, Warsaw, Poland), Jie Yang (First Affiliated Hospital of Chengdu Medical College, Chengdu, China), and Tomasz Zdrojewski (Medical University of Gdańsk, Gdańsk, Poland) for their comments on early draft of the manuscript. The views expressed in this article are solely the responsibility of the authors and they do not necessarily reflect the views, decisions, or policies of the institution with which they are affiliated. We thank WSO for funding. The funder had no role in the design, data collection, analysis and interpretation of the study results, writing of the report, or the decision to submit the study results for publication. Funding Information: VLF declares that the PreventS web app and Stroke Riskometer app are owned and copyrighted by Auckland University of Technology; has received grants from the Brain Research New Zealand Centre of Research Excellence (16/STH/36), Australian National Health and Medical Research Council (NHMRC; APP1182071), and World Stroke Organization (WSO); is an executive committee member of WSO, honorary medical director of Stroke Central New Zealand, and CEO of New Zealand Stroke Education charitable Trust. AGT declares funding from NHMRC (GNT1042600, GNT1122455, GNT1171966, GNT1143155, and GNT1182017), Stroke Foundation Australia (SG1807), and Heart Foundation Australia (VG102282); and board membership of the Stroke Foundation (Australia). SLG is funded by the National Health Foundation of Australia (Future Leader Fellowship 102061) and NHMRC (GNT1182071, GNT1143155, and GNT1128373). RM is supported by the Implementation Research Network in Stroke Care Quality of the European Cooperation in Science and Technology (project CA18118) and by the IRIS-TEPUS project from the inter-excellence inter-cost programme of the Ministry of Education, Youth and Sports of the Czech Republic (project LTC20051). BN declares receiving fees for data management committee work for SOCRATES and THALES trials for AstraZeneca and fees for data management committee work for NAVIGATE-ESUS trial from Bayer. All other authors declare no competing interests. Publisher Copyright: © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licenseStroke is the second leading cause of death and the third leading cause of disability worldwide and its burden is increasing rapidly in low-income and middle-income countries, many of which are unable to face the challenges it imposes. In this Health Policy paper on primary stroke prevention, we provide an overview of the current situation regarding primary prevention services, estimate the cost of stroke and stroke prevention, and identify deficiencies in existing guidelines and gaps in primary prevention. We also offer a set of pragmatic solutions for implementation of primary stroke prevention, with an emphasis on the role of governments and population-wide strategies, including task-shifting and sharing and health system re-engineering. Implementation of primary stroke prevention involves patients, health professionals, funders, policy makers, implementation partners, and the entire population along the life course.publishersversionPeer reviewe

The Role of Summer Intensive Programmes on Improving Students’ Learning Outcomes in Rural Areas

Many countries face serious challenges with education in rural areas. Vast majority of education in rural areas are disadvantaged. In Kazakhstan, ineffective outcomes of education in rural areas impact on most rural students are lack employability and entrepreneurial skills. This research investigates the role of intensive summer programmes on improving students’ learning outcomes aim to develop and Inspire employability and entrepreneurial skills and STEAM-education in rural schools of Kazakhstan. This research suggest that the summer program transferred practical skills and knowledge to students, while increasing their motivation to learn outside of the classroom in a way that traditional educational system has not. There are social and economic challenges in the rural areas: unemployment, the lack of highly skilled professionals, and low motivation. Summer programs appear to address all above issues by motivating youth to learn and providing them with practical skills that can be used in their private life and careers

BioTIME: A database of biodiversity time series for the Anthropocene

Motivation: The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community-led open-source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene. Main types of variables included: The database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of 2 years, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology and contextual information about each record. Spatial location and grain: BioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km(2) (158 cm(2)) to 100 km(2) (1,000,000,000,000 cm(2)). Time period and grainBio: TIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year. Major taxa and level of measurement: BioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates.European Research Council; EU [AdG-250189, PoC-727440, ERC-SyG-2013-610028]; Natural Environmental Research Council [NE/L002531/1]; National Science Foundation [DEB-1237733, DEB-1456729, 9714103, 0632263, 0856516, 1432277, DEB 9705814, BSR-8811902, DEB 9411973, DEB 0080538, DEB 0218039, DEB 0620910, DEB 0963447, DEB-1546686, DEB-129764]; National Science Foundation (LTER) [DEB-1235828, DEB-1440297, DBI-0620409, DEB-9910514, DEB-1237517, OCE-0417412, OCE-1026851, OCE-1236905, OCE-1637396, DEB 1440409, DEB-0832652, DEB-0936498, DEB-0620652, DEB-1234162, DEB-0823293, OCE-9982105, OCE-0620276, OCE-1232779]; Fundacao para a Ciencia e Tecnologia [POPH/FSE SFRH/BD/90469/2012, SFRH/BD/84030/2012, PTDC/BIA-BIC/111184/2009]; Ciencia sem Fronteiras/CAPES [1091/13-1]; Instituto Milenio de Oceanografia [IC120019]; ARC Centre of Excellence [CE0561432]; NSERC Canada; CONICYT/FONDECYT [1160026, ICM PO5-002, 11110351, 1151094, 1070808, 1130511]; RSF [14-50-00029]; Gordon and Betty Moore Foundation [GBMF4563]; Catalan Government; Marie Curie Individual Fellowship [QLK5-CT2002-51518, MERG-CT-2004-022065]; CNPq [306170/2015-9, 475434/2010-2, 403809/2012-6, 561897/2010, 306595-2014-1]; FAPESP (Sao Paulo Research Foundation) [2015/10714-6, 2015/06743-0, 2008/10049-9, 2013/50714-0, 1999/09635-0 e 2013/50718-5]; EU CLIMOOR [ENV4-CT97-0694]; VULCAN [EVK2-CT2000-00094]; DFG [120/10-2]; Polar Continental Shelf Program; CENPES - PETROBRAS; FAPERJ [E-26/110.114/ 2013]; German Academic Exchange Service; New Zealand Department of Conservation; Wellcome Trust [105621/Z/14/Z]; Smithsonian Atherton Seidell Fund; Botanic Gardens and Parks Authority; Research Council of Norway; Conselleria de Innovacio, Hisenda i Economia; Yukon Government Herschel Island-Qikiqtaruk Territorial Park; UK Natural Environment Research Council ShrubTundra Grant [NE/M016323/1]; IPY; Memorial University; ArcticNet; Netherlands Organization for Scientific Research in the Tropics NWO [W84-194]; Ciencias sem Fronteiras and Coordenacao de Pessoal de Nivel Superior (CAPES, Brazil) [1091/13-1]; U.S. Fish and Wildlife Service/State Wildlife federal grant [T-15]; Australian Research Council Centre of Excellence for Coral Reef Studies [CE140100020]; Australian Research Council Future Fellowship [FT110100609]; University of Lodz; NSF DEB [1353139]; Catalan Government fellowships (DURSI) [1998FI-00596, 2001BEAI200208]; MECD Post-doctoral fellowship [EX2002-0022]; FONDECYT [1141037]; FONDAP [15150003]; [SFRH/BD/80488/2011]; [PD/BD/52597/2014]; [REN2000-0278/CCI]; [REN2001-003/GLO]; [CGL2016-79835-P]; [AGAUR SGR-2014453]; [SGR-2017-1005]; [FCT - SFRH / BPD / 82259 / 2011]; [OCE 95-21184]; [OCE-0099226]; [OCE 03-5234]; [OCE-0623874]; [OCE-1031061]; [OCE-1336206]; [DEB-1354563]; [OPP-1440435]12 month embargo; published online: 24 July 2018This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

BioTIME : a database of biodiversity time series for the Anthropocene [data paper]

Motivation: The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community-led open-source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene. Main types of variables included: The database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of 2 years, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology and contextual information about each record. Spatial location and grain: BioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km(2) (158 cm(2)) to 100 km(2) (1,000,000,000,000 cm(2)). Time period and grainBio: TIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year. Major taxa and level of measurement: BioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates