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Institution

Memorial University of Newfoundland

EducationSt. John's, Newfoundland and Labrador, Canada
About: Memorial University of Newfoundland is a education organization based out in St. John's, Newfoundland and Labrador, Canada. It is known for research contribution in the topics: Population & Context (language use). The organization has 13818 authors who have published 27785 publications receiving 743594 citations. The organization is also known as: Memorial University & Memorial University of Newfoundland and Labrador.


Papers
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Journal ArticleDOI
TL;DR: In this article, the authors discuss the scientific need for globally integrated deep-ocean observing, its status, and the key scientific questions and societal mandates driving observing requirements over the next decade.
Abstract: The deep ocean below 200 m water depth is the least observed, but largest habitat on our planet by volume and area. Over 150 years of exploration has revealed that this dynamic system provides critical climate regulation, houses a wealth of energy, mineral, and biological resources, and represents a vast repository of biological diversity. A long history of deep-ocean exploration and observation led to the initial concept for the Deep-Ocean Observing Strategy (DOOS), under the auspices of the Global Ocean Observing System (GOOS). Here we discuss the scientific need for globally integrated deep-ocean observing, its status, and the key scientific questions and societal mandates driving observing requirements over the next decade. We consider the Essential Ocean Variables (EOVs) needed to address deep-ocean challenges within the physical, biogeochemical, and biological/ecosystem sciences according to the Framework for Ocean Observing (FOO), and map these onto scientific questions. Opportunities for new and expanded synergies among deep-ocean stakeholders are discussed, including academic-industry partnerships with the oil and gas, mining, cable and fishing industries, the ocean exploration and mapping community, and biodiversity conservation initiatives. Future deep-ocean observing will benefit from the greater integration across traditional disciplines and sectors, achieved through demonstration projects and facilitated reuse and repurposing of existing deep-sea data efforts. We highlight examples of existing and emerging deep-sea methods and technologies, noting key challenges associated with data volume, preservation, standardization, and accessibility. Emerging technologies relevant to deep-ocean sustainability and the blue economy include novel genomics approaches, imaging technologies, and ultra-deep hydrographic measurements. Capacity building will be necessary to integrate capabilities into programs and projects at a global scale. Progress can be facilitated by Open Science and Findable, Accessible, Interoperable, Reusable (FAIR) data principles and converge on agreed to data standards, practices, vocabularies, and registries. We envision expansion of the deep-ocean observing community to embrace the participation of academia, industry, NGOs, national governments, international governmental organizations, and the public at large in order to unlock critical knowledge contained in the deep ocean over coming decades, and to realize the mutual benefits of thoughtful deep-ocean observing for all elements of a sustainable ocean.

166 citations

Journal ArticleDOI
TL;DR: In this article, the surfaces of Green Arrow pea and Eston lentil starches were modified after heat treatment, but the results showed that bonding forces within the amorphous regions of the granule, crystallite orientation and granule surface were altered during heat treatment.

165 citations

Journal ArticleDOI
Dana B. Hancock1, Dana B. Hancock2, María Soler Artigas3, Sina A. Gharib4, Amanda P. Henry5, Ani Manichaikul6, Adaikalavan Ramasamy, Daan W. Loth7, Medea Imboden8, Medea Imboden9, Beate Koch10, Wendy L. McArdle11, Albert V. Smith12, Joanna Smolonska13, Akshay Sood14, Wenbo Tang15, Jemma B. Wilk16, Jemma B. Wilk17, Guangju Zhai18, Guangju Zhai19, Jing Hua Zhao20, Hugues Aschard21, Kristin M. Burkart22, Ivan Curjuric8, Ivan Curjuric9, Mark Eijgelsheim7, Paul Elliott23, Xiangjun Gu24, Tamara B. Harris17, Christer Janson25, Georg Homuth10, Pirro G. Hysi18, Jason Z. Liu26, Laura R. Loehr27, Kurt Lohman28, Ruth J. F. Loos20, Alisa K. Manning29, Alisa K. Manning21, Kristin D. Marciante4, Ma'en Obeidat5, Dirkje S. Postma13, Melinda C. Aldrich30, Guy Brusselle31, Ting Hsu Chen32, Ting Hsu Chen33, Gudny Eiriksdottir, Nora Franceschini27, Joachim Heinrich, Jerome I. Rotter34, Cisca Wijmenga13, O. Dale Williams35, Amy R. Bentley17, Albert Hofman7, Cathy C. Laurie4, Thomas Lumley36, Alanna C. Morrison24, Bonnie R. Joubert17, Fernando Rivadeneira7, David Couper27, Stephen B. Kritchevsky28, Yongmei Liu28, Matthias Wjst37, Louise V. Wain3, Judith M. Vonk38, André G. Uitterlinden7, Thierry Rochat39, Stephen S. Rich6, Bruce M. Psaty40, Bruce M. Psaty4, George T. O'Connor17, George T. O'Connor32, Kari E. North27, Daniel B. Mirel29, Bernd Meibohm41, Lenore J. Launer17, Kay-Tee Khaw42, Anna-Liisa Hartikainen43, Christopher J Hammond18, Sven Gläser10, Jonathan Marchini26, Peter Kraft21, Nicholas J. Wareham20, Henry Völzke10, Bruno H. Stricker, Tim D. Spector18, Nicole Probst-Hensch8, Nicole Probst-Hensch9, Deborah Jarvis23, Marjo-Riitta Järvelin, Susan R. Heckbert4, Susan R. Heckbert40, Vilmundur Gudnason12, H. Marike Boezen38, R. Graham Barr22, Patricia A. Cassano15, David P. Strachan44, Myriam Fornage24, Ian P. Hall5, Josée Dupuis32, Josée Dupuis17, Martin D. Tobin3, Stephanie J. London17 
TL;DR: In this paper, the authors conducted genome-wide joint meta-analyses (JMA) of single nucleotide polymorphism (SNP) and SNP-by-smoking (ever-smoking or pack-years) associations on spirometic measures of pulmonary function, forced expiratory volume in one second (FEV(1)), and its ratio to forced vital capacity(FEV (1)/FVC).
Abstract: Genome-wide association studies have identified numerous genetic loci for spirometic measures of pulmonary function, forced expiratory volume in one second (FEV(1)), and its ratio to forced vital capacity (FEV(1)/FVC). Given that cigarette smoking adversely affects pulmonary function, we conducted genome-wide joint meta-analyses (JMA) of single nucleotide polymorphism (SNP) and SNP-by-smoking (ever-smoking or pack-years) associations on FEV(1) and FEV(1)/FVC across 19 studies (total N = 50,047). We identified three novel loci not previously associated with pulmonary function. SNPs in or near DNER (smallest P(JMA = )5.00×10(-11)), HLA-DQB1 and HLA-DQA2 (smallest P(JMA = )4.35×10(-9)), and KCNJ2 and SOX9 (smallest P(JMA = )1.28×10(-8)) were associated with FEV(1)/FVC or FEV(1) in meta-analysis models including SNP main effects, smoking main effects, and SNP-by-smoking (ever-smoking or pack-years) interaction. The HLA region has been widely implicated for autoimmune and lung phenotypes, unlike the other novel loci, which have not been widely implicated. We evaluated DNER, KCNJ2, and SOX9 and found them to be expressed in human lung tissue. DNER and SOX9 further showed evidence of differential expression in human airway epithelium in smokers compared to non-smokers. Our findings demonstrated that joint testing of SNP and SNP-by-environment interaction identified novel loci associated with complex traits that are missed when considering only the genetic main effects.

165 citations

Journal ArticleDOI
TL;DR: The study shows that in spite of obvious differences between the two environments and the analytical approaches employed in each case, the analyses of fatty acid biomarkers can provide relevant ecological information.

165 citations


Authors

Showing all 13990 results

NameH-indexPapersCitations
Daniel Levy212933194778
Rakesh K. Jain2001467177727
Peter W.F. Wilson181680139852
Martin G. Larson171620117708
Peter B. Jones145185794641
Dafna D. Gladman129103675273
Guoyao Wu12276456270
Fereidoon Shahidi11995157796
David Harvey11573894678
Robert C. Haddon11257752712
Se-Kwon Kim10276339344
John E. Dowling9430528116
Mark J. Sarnak9439342485
William T. Greenough9320029230
Soottawat Benjakul9289134336
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202386
2022269
20211,808
20201,749
20191,568
20181,516