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  1. Article ; Online: Revealed Preferences on Meat Substitute Consumption and Political Attitudes - Testing the Left-Right and Environmental Concerns Framework.

    Petersen, Thies / Denker, Tom / Koppenberg, Maximilian / Hirsch, Stefan

    Appetite

    2024  , Page(s) 107371

    Abstract: The promotion of meat substitutes to reduce meat intake is a promising way to reduce the environmental and public health externalities of meat consumption while preserving the important role of taste and texture in meat products. However, the market for ... ...

    Abstract The promotion of meat substitutes to reduce meat intake is a promising way to reduce the environmental and public health externalities of meat consumption while preserving the important role of taste and texture in meat products. However, the market for meat substitutes is developing more slowly than expected. Therefore, we analyze the factors associated with the heterogeneity in meat substitute consumption in Germany, a country where meat traditionally plays an important role. We use revealed preference data on meat substitute sales from 1,025 individual retailers, sociodemographic data, and election results from 92 regions in Germany over the period 2017-2021, to analyze whether differences in meat substitute consumption are associated with consumers' political orientation (liberal/left or conservative/right) and socio-demographic variables. We also investigate whether election results for parties with stronger climate protection goals are associated with meat substitute consumption. Our results show that meat substitute consumption varies significantly across Germany and that this is related to differences in socio-demographic characteristics and voting behavior across regions. Voting for the Green Party and parties with strong climate protection ambitions is positively related to the market share of meat substitutes. In contrast, voting for Germany's most conservative party, which has the lowest ambitions in terms of climate protection targets, is associated with lower meat substitute consumption. Therefore, manufacturers could develop tailored marketing strategies that specifically target these voter groups in order to increase the market share of meat substitutes as alternatives to meat products.
    Language English
    Publishing date 2024-05-01
    Publishing country England
    Document type Journal Article
    ZDB-ID 1461347-5
    ISSN 1095-8304 ; 0195-6663
    ISSN (online) 1095-8304
    ISSN 0195-6663
    DOI 10.1016/j.appet.2024.107371
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Utilizing weak pump depletion to stabilize squeezed vacuum states.

    Denker, T / Schütte, D / Wimmer, M H / Wheatley, T A / Huntington, E H / Heurs, M

    Optics express

    2015  Volume 23, Issue 13, Page(s) 16517–16528

    Abstract: We propose and demonstrate a pump-phase locking technique that makes use of weak pump depletion (WPD) - an unavoidable effect that is usually neglected - in a sub-threshold optical parametric oscillator (OPO). We show that the phase difference between ... ...

    Abstract We propose and demonstrate a pump-phase locking technique that makes use of weak pump depletion (WPD) - an unavoidable effect that is usually neglected - in a sub-threshold optical parametric oscillator (OPO). We show that the phase difference between seed and pump beam is imprinted on both light fields by the non-linear interaction in the crystal and can be read out without disturbing the squeezed output. In our experimental setup we observe squeezing levels of 1.96 ± 0.01 dB, with an anti-squeezing level of 3.78 ± 0.02 dB (for a 0.55 mW seed beam at 1064 nm and 67.8 mW of pump light at 532 nm). Our new locking technique allows for the first experimental realization of a pump-phase lock by reading out the pre-existing phase information in the pump field. There is no degradation of the detected squeezed states required to implement this scheme.
    Language English
    Publishing date 2015-06-29
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1491859-6
    ISSN 1094-4087 ; 1094-4087
    ISSN (online) 1094-4087
    ISSN 1094-4087
    DOI 10.1364/OE.23.016517
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article: Epidemiology, clinical characteristics, and outcome of candidemia in critically ill patients in Germany: a single-center retrospective 10-year analysis.

    Schroeder, Maria / Weber, Theresa / Denker, Timme / Winterland, Sarah / Wichmann, Dominic / Rohde, Holger / Ozga, Ann-Kathrin / Fischer, Marlene / Kluge, Stefan

    Annals of intensive care

    2020  Volume 10, Issue 1, Page(s) 142

    Abstract: Background: Despite advances in the management of bloodstream infections (BSI) caused by Candida spp., the mortality still remains high in critically ill patients. The worldwide epidemiology of yeast-related BSI is subject to changing species ... ...

    Abstract Background: Despite advances in the management of bloodstream infections (BSI) caused by Candida spp., the mortality still remains high in critically ill patients. The worldwide epidemiology of yeast-related BSI is subject to changing species distribution and resistance patterns, challenging antifungal treatment strategies. The aim of this single-center study was to identify predictors of mortality after 28 and 180 days in a cohort of mixed surgical and medical critically ill patients with candidemia.
    Methods: Patients, who had been treated for laboratory-confirmed BSI caused by Candida spp. in one of 12 intensive care units (ICU) at a University hospital between 2008 and 2017, were retrospectively identified. We retrieved data including clinical characteristics, Candida species distribution, and antifungal management from electronic health records to identify risk factors for mortality at 28 and 180 days using a Cox regression model.
    Results: A total of 391 patients had blood cultures positive for Candida spp. (incidence 4.8/1000 ICU admissions). The mortality rate after 28 days was 47% (n = 185) and increased to 60% (n = 234) after 180 days. Age (HR 1.02 [95% CI 1.01-1.03]), a history of liver cirrhosis (HR 1.54 [95% CI 1.07-2.20]), septic shock (HR 2.41 [95% CI 1.73-3.37]), the Sepsis-related Organ Failure Assessment score (HR 1.12 [95% CI 1.07-1.17]), Candida score (HR 1.25 [95% CI 1.11-1.40]), and the length of ICU stay at culture positivity (HR 1.01 [95% CI 1.00-1.01]) were significant risk factors for death at 180 days. Patients, who had abdominal surgery (HR 0.66 [95% CI 0.48-0.91]) and patients, who received adequate (HR 0.36 [95% CI 0.24-0.52]) or non-adequate (HR 0.31 [95% CI 0.16-0.62]) antifungal treatment, had a reduced mortality risk compared to medical admission and no antifungal treatment, respectively.
    Conclusions: The mortality of critically ill patients with Candida BSI is high and is mainly determined by disease severity, multiorgan dysfunction, and antifungal management rather than species distribution and susceptibility. Our results underline the importance of timely treatment of candidemia. However, controversies remain on the optimal definition of adequate antifungal management.
    Language English
    Publishing date 2020-10-16
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 2617094-2
    ISSN 2110-5820
    ISSN 2110-5820
    DOI 10.1186/s13613-020-00755-8
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Book ; Online: Community ecology in 3D

    Frelat, Romain / Lindegren, Martin / Spaanheden Denker, Tim / Floeter, Jens / Fock, Heino O. / Sguotti, Camilla / Stäbler, Moritz / Otto, Saskia A. / Möllmann, Christian

    PLOS ONE, 12(11):e0188205

    Tensor decomposition reveals spatio-temporal dynamics of large ecological communities

    2017  

    Abstract: Understanding spatio-temporal dynamics of biotic communities containing large numbers of species is crucial to guide ecosystem management and conservation efforts. However, traditional approaches usually focus on studying community dynamics either in ... ...

    Abstract Understanding spatio-temporal dynamics of biotic communities containing large numbers of species is crucial to guide ecosystem management and conservation efforts. However, traditional approaches usually focus on studying community dynamics either in space or in time, often failing to fully account for interlinked spatio-temporal changes. In this study, we demonstrate and promote the use of tensor decomposition for disentangling spatio-temporal community dynamics in long-term monitoring data. Tensor decomposition builds on traditional multivariate statistics (e.g. Principal Component Analysis) but extends it to multiple dimensions. This extension allows for the synchronized study of multiple ecological variables measured repeatedly in time and space. We applied this comprehensive approach to explore the spatio-temporal dynamics of 65 demersal fish species in the North Sea, a marine ecosystem strongly altered by human activities and climate change. Our case study demonstrates how tensor decomposition can successfully (i) characterize the main spatio-temporal patterns and trends in species abundances, (ii) identify sub-communities of species that share similar spatial distribution and temporal dynamics, and (iii) reveal external drivers of change. Our results revealed a strong spatial structure in fish assemblages persistent over time and linked to differences in depth, primary production and seasonality. Furthermore, we simultaneously characterized important temporal distribution changes related to the low frequency temperature variability inherent in the Atlantic Multidecadal Oscillation. Finally, we identified six major sub-communities composed of species sharing similar spatial distribution patterns and temporal dynamics. Our case study demonstrates the application and benefits of using tensor decomposition for studying complex community data sets usually derived from large-scale monitoring programs.
    Keywords Principal component analysis ; Ecosystems ; Fish biology ; Community structure ; Community ecology ; Marine ecology ; Species diversity ; Statistical data
    Subject code 333
    Language English
    Publishing country de
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: Community ecology in 3D

    Frelat, Romain / Lindegren, Martin / Spaanheden Denker, Tim / Floeter, Jens / Fock, Heino O. / Sguotti, Camilla / Stäbler, Moritz / Otto, Saskia A. / Möllmann, Christian

    PLOS ONE, 12(11):e0188205

    Tensor decomposition reveals spatio-temporal dynamics of large ecological communities

    2017  

    Abstract: Understanding spatio-temporal dynamics of biotic communities containing large numbers of species is crucial to guide ecosystem management and conservation efforts. However, traditional approaches usually focus on studying community dynamics either in ... ...

    Abstract Understanding spatio-temporal dynamics of biotic communities containing large numbers of species is crucial to guide ecosystem management and conservation efforts. However, traditional approaches usually focus on studying community dynamics either in space or in time, often failing to fully account for interlinked spatio-temporal changes. In this study, we demonstrate and promote the use of tensor decomposition for disentangling spatio-temporal community dynamics in long-term monitoring data. Tensor decomposition builds on traditional multivariate statistics (e.g. Principal Component Analysis) but extends it to multiple dimensions. This extension allows for the synchronized study of multiple ecological variables measured repeatedly in time and space. We applied this comprehensive approach to explore the spatio-temporal dynamics of 65 demersal fish species in the North Sea, a marine ecosystem strongly altered by human activities and climate change. Our case study demonstrates how tensor decomposition can successfully (i) characterize the main spatio-temporal patterns and trends in species abundances, (ii) identify sub-communities of species that share similar spatial distribution and temporal dynamics, and (iii) reveal external drivers of change. Our results revealed a strong spatial structure in fish assemblages persistent over time and linked to differences in depth, primary production and seasonality. Furthermore, we simultaneously characterized important temporal distribution changes related to the low frequency temperature variability inherent in the Atlantic Multidecadal Oscillation. Finally, we identified six major sub-communities composed of species sharing similar spatial distribution patterns and temporal dynamics. Our case study demonstrates the application and benefits of using tensor decomposition for studying complex community data sets usually derived from large-scale monitoring programs.
    Keywords Principal component analysis ; Ecosystems ; Fish biology ; Community structure ; Community ecology ; Marine ecology ; Species diversity ; Statistical data
    Subject code 333
    Language English
    Publishing country de
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Book ; Online: Community ecology in 3D

    Frelat, Romain / Lindegren, Martin / Spaanheden Denker, Tim / Floeter, Jens / Fock, Heino O. / Sguotti, Camilla / Stäbler, Moritz / Otto, Saskia A. / Möllmann, Christian

    PLOS ONE, 12(11):e0188205

    Tensor decomposition reveals spatio-temporal dynamics of large ecological communities

    2017  

    Abstract: Understanding spatio-temporal dynamics of biotic communities containing large numbers of species is crucial to guide ecosystem management and conservation efforts. However, traditional approaches usually focus on studying community dynamics either in ... ...

    Abstract Understanding spatio-temporal dynamics of biotic communities containing large numbers of species is crucial to guide ecosystem management and conservation efforts. However, traditional approaches usually focus on studying community dynamics either in space or in time, often failing to fully account for interlinked spatio-temporal changes. In this study, we demonstrate and promote the use of tensor decomposition for disentangling spatio-temporal community dynamics in long-term monitoring data. Tensor decomposition builds on traditional multivariate statistics (e.g. Principal Component Analysis) but extends it to multiple dimensions. This extension allows for the synchronized study of multiple ecological variables measured repeatedly in time and space. We applied this comprehensive approach to explore the spatio-temporal dynamics of 65 demersal fish species in the North Sea, a marine ecosystem strongly altered by human activities and climate change. Our case study demonstrates how tensor decomposition can successfully (i) characterize the main spatio-temporal patterns and trends in species abundances, (ii) identify sub-communities of species that share similar spatial distribution and temporal dynamics, and (iii) reveal external drivers of change. Our results revealed a strong spatial structure in fish assemblages persistent over time and linked to differences in depth, primary production and seasonality. Furthermore, we simultaneously characterized important temporal distribution changes related to the low frequency temperature variability inherent in the Atlantic Multidecadal Oscillation. Finally, we identified six major sub-communities composed of species sharing similar spatial distribution patterns and temporal dynamics. Our case study demonstrates the application and benefits of using tensor decomposition for studying complex community data sets usually derived from large-scale monitoring programs.
    Keywords Principal component analysis ; Ecosystems ; Fish biology ; Community structure ; Community ecology ; Marine ecology ; Species diversity ; Statistical data
    Subject code 333
    Language English
    Publishing country de
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Book ; Online: Community ecology in 3D

    Frelat, Romain / Lindegren, Martin / Spaanheden Denker, Tim / Floeter, Jens / Fock, Heino O. / Sguotti, Camilla / Stäbler, Moritz / Otto, Saskia A. / Möllmann, Christian

    PLOS ONE, 12(11):e0188205

    Tensor decomposition reveals spatio-temporal dynamics of large ecological communities

    2017  

    Abstract: Understanding spatio-temporal dynamics of biotic communities containing large numbers of species is crucial to guide ecosystem management and conservation efforts. However, traditional approaches usually focus on studying community dynamics either in ... ...

    Abstract Understanding spatio-temporal dynamics of biotic communities containing large numbers of species is crucial to guide ecosystem management and conservation efforts. However, traditional approaches usually focus on studying community dynamics either in space or in time, often failing to fully account for interlinked spatio-temporal changes. In this study, we demonstrate and promote the use of tensor decomposition for disentangling spatio-temporal community dynamics in long-term monitoring data. Tensor decomposition builds on traditional multivariate statistics (e.g. Principal Component Analysis) but extends it to multiple dimensions. This extension allows for the synchronized study of multiple ecological variables measured repeatedly in time and space. We applied this comprehensive approach to explore the spatio-temporal dynamics of 65 demersal fish species in the North Sea, a marine ecosystem strongly altered by human activities and climate change. Our case study demonstrates how tensor decomposition can successfully (i) characterize the main spatio-temporal patterns and trends in species abundances, (ii) identify sub-communities of species that share similar spatial distribution and temporal dynamics, and (iii) reveal external drivers of change. Our results revealed a strong spatial structure in fish assemblages persistent over time and linked to differences in depth, primary production and seasonality. Furthermore, we simultaneously characterized important temporal distribution changes related to the low frequency temperature variability inherent in the Atlantic Multidecadal Oscillation. Finally, we identified six major sub-communities composed of species sharing similar spatial distribution patterns and temporal dynamics. Our case study demonstrates the application and benefits of using tensor decomposition for studying complex community data sets usually derived from large-scale monitoring programs.
    Keywords Principal component analysis ; Ecosystems ; Fish biology ; Community structure ; Community ecology ; Marine ecology ; Species diversity ; Statistical data
    Subject code 333
    Language English
    Publishing country de
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    More links

    Kategorien

  8. Book ; Online: Community ecology in 3D

    Frelat, Romain / Lindegren, Martin / Spaanheden Denker, Tim / Floeter, Jens / Fock, Heino O. / Sguotti, Camilla / Stäbler, Moritz / Otto, Saskia A. / Möllmann, Christian

    PLOS ONE, 12(11):e0188205

    Tensor decomposition reveals spatio-temporal dynamics of large ecological communities

    2017  

    Abstract: Understanding spatio-temporal dynamics of biotic communities containing large numbers of species is crucial to guide ecosystem management and conservation efforts. However, traditional approaches usually focus on studying community dynamics either in ... ...

    Abstract Understanding spatio-temporal dynamics of biotic communities containing large numbers of species is crucial to guide ecosystem management and conservation efforts. However, traditional approaches usually focus on studying community dynamics either in space or in time, often failing to fully account for interlinked spatio-temporal changes. In this study, we demonstrate and promote the use of tensor decomposition for disentangling spatio-temporal community dynamics in long-term monitoring data. Tensor decomposition builds on traditional multivariate statistics (e.g. Principal Component Analysis) but extends it to multiple dimensions. This extension allows for the synchronized study of multiple ecological variables measured repeatedly in time and space. We applied this comprehensive approach to explore the spatio-temporal dynamics of 65 demersal fish species in the North Sea, a marine ecosystem strongly altered by human activities and climate change. Our case study demonstrates how tensor decomposition can successfully (i) characterize the main spatio-temporal patterns and trends in species abundances, (ii) identify sub-communities of species that share similar spatial distribution and temporal dynamics, and (iii) reveal external drivers of change. Our results revealed a strong spatial structure in fish assemblages persistent over time and linked to differences in depth, primary production and seasonality. Furthermore, we simultaneously characterized important temporal distribution changes related to the low frequency temperature variability inherent in the Atlantic Multidecadal Oscillation. Finally, we identified six major sub-communities composed of species sharing similar spatial distribution patterns and temporal dynamics. Our case study demonstrates the application and benefits of using tensor decomposition for studying complex community data sets usually derived from large-scale monitoring programs.
    Keywords Principal component analysis ; Ecosystems ; Fish biology ; Community structure ; Community ecology ; Marine ecology ; Species diversity ; Statistical data
    Subject code 333
    Language English
    Publishing country de
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    More links

    Kategorien

  9. Book ; Online: Community ecology in 3D

    Frelat, Romain / Lindegren, Martin / Spaanheden Denker, Tim / Floeter, Jens / Fock, Heino O. / Sguotti, Camilla / Stäbler, Moritz / Otto, Saskia A. / Möllmann, Christian

    PLOS ONE, 12(11):e0188205

    Tensor decomposition reveals spatio-temporal dynamics of large ecological communities

    2017  

    Abstract: Understanding spatio-temporal dynamics of biotic communities containing large numbers of species is crucial to guide ecosystem management and conservation efforts. However, traditional approaches usually focus on studying community dynamics either in ... ...

    Abstract Understanding spatio-temporal dynamics of biotic communities containing large numbers of species is crucial to guide ecosystem management and conservation efforts. However, traditional approaches usually focus on studying community dynamics either in space or in time, often failing to fully account for interlinked spatio-temporal changes. In this study, we demonstrate and promote the use of tensor decomposition for disentangling spatio-temporal community dynamics in long-term monitoring data. Tensor decomposition builds on traditional multivariate statistics (e.g. Principal Component Analysis) but extends it to multiple dimensions. This extension allows for the synchronized study of multiple ecological variables measured repeatedly in time and space. We applied this comprehensive approach to explore the spatio-temporal dynamics of 65 demersal fish species in the North Sea, a marine ecosystem strongly altered by human activities and climate change. Our case study demonstrates how tensor decomposition can successfully (i) characterize the main spatio-temporal patterns and trends in species abundances, (ii) identify sub-communities of species that share similar spatial distribution and temporal dynamics, and (iii) reveal external drivers of change. Our results revealed a strong spatial structure in fish assemblages persistent over time and linked to differences in depth, primary production and seasonality. Furthermore, we simultaneously characterized important temporal distribution changes related to the low frequency temperature variability inherent in the Atlantic Multidecadal Oscillation. Finally, we identified six major sub-communities composed of species sharing similar spatial distribution patterns and temporal dynamics. Our case study demonstrates the application and benefits of using tensor decomposition for studying complex community data sets usually derived from large-scale monitoring programs.
    Keywords Principal component analysis ; Ecosystems ; Fish biology ; Community structure ; Community ecology ; Marine ecology ; Species diversity ; Statistical data
    Subject code 333
    Language English
    Publishing country de
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    More links

    Kategorien

  10. Book ; Online: Community ecology in 3D

    Frelat, Romain / Lindegren, Martin / Spaanheden Denker, Tim / Floeter, Jens / Fock, Heino O. / Sguotti, Camilla / Stäbler, Moritz / Otto, Saskia A. / Möllmann, Christian

    PLOS ONE, 12(11):e0188205

    Tensor decomposition reveals spatio-temporal dynamics of large ecological communities

    2017  

    Abstract: Understanding spatio-temporal dynamics of biotic communities containing large numbers of species is crucial to guide ecosystem management and conservation efforts. However, traditional approaches usually focus on studying community dynamics either in ... ...

    Abstract Understanding spatio-temporal dynamics of biotic communities containing large numbers of species is crucial to guide ecosystem management and conservation efforts. However, traditional approaches usually focus on studying community dynamics either in space or in time, often failing to fully account for interlinked spatio-temporal changes. In this study, we demonstrate and promote the use of tensor decomposition for disentangling spatio-temporal community dynamics in long-term monitoring data. Tensor decomposition builds on traditional multivariate statistics (e.g. Principal Component Analysis) but extends it to multiple dimensions. This extension allows for the synchronized study of multiple ecological variables measured repeatedly in time and space. We applied this comprehensive approach to explore the spatio-temporal dynamics of 65 demersal fish species in the North Sea, a marine ecosystem strongly altered by human activities and climate change. Our case study demonstrates how tensor decomposition can successfully (i) characterize the main spatio-temporal patterns and trends in species abundances, (ii) identify sub-communities of species that share similar spatial distribution and temporal dynamics, and (iii) reveal external drivers of change. Our results revealed a strong spatial structure in fish assemblages persistent over time and linked to differences in depth, primary production and seasonality. Furthermore, we simultaneously characterized important temporal distribution changes related to the low frequency temperature variability inherent in the Atlantic Multidecadal Oscillation. Finally, we identified six major sub-communities composed of species sharing similar spatial distribution patterns and temporal dynamics. Our case study demonstrates the application and benefits of using tensor decomposition for studying complex community data sets usually derived from large-scale monitoring programs.
    Keywords Principal component analysis ; Ecosystems ; Fish biology ; Community structure ; Community ecology ; Marine ecology ; Species diversity ; Statistical data
    Subject code 333
    Language English
    Publishing country de
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    More links

    Kategorien

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