The Experts below are selected from a list of 162 Experts worldwide ranked by ideXlab platform
Cyril Zipfel - One of the best experts on this subject based on the ideXlab platform.
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Function Discovery and exploitation of plant pattern recognition receptors for broad spectrum disease resistance
Annual Review of Phytopathology, 2017Co-Authors: Freddy Boutrot, Cyril ZipfelAbstract:Plants are constantly exposed to would-be pathogens and pests, and thus have a sophisticated immune system to ward off these threats, which otherwise can have devastating ecological and economic consequences on ecosystems and agriculture. Plants employ receptor kinases (RKs) and receptor-like proteins (RLPs) as pattern recognition receptors (PRRs) to monitor their apoplastic environment and detect non-self and damaged-self patterns as signs of potential danger. Plant PRRs contribute to both basal and non-host resistances, and treatment with pathogen-/microbe-associated molecular patterns (PAMPs/MAMPs) or damage-associated molecular patterns (DAMPs) recognized by plant PRRs induces both local and systemic immunity. Here, we comprehensively review known PAMPs/DAMPs recognized by plants as well as the plant PRRs described to date. In particular, we describe the different methods that can be used to identify PAMPs/DAMPs and PRRs. Finally, we emphasize the emerging biotechnological potential use of PRRs to imp...
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Function Discovery and exploitation of plant pattern recognition receptors for broad spectrum disease resistance
Annual Review of Phytopathology, 2017Co-Authors: Freddy Boutrot, Cyril ZipfelAbstract:Plants are constantly exposed to would-be pathogens and pests, and thus have a sophisticated immune system to ward off these threats, which otherwise can have devastating ecological and economic consequences on ecosystems and agriculture. Plants employ receptor kinases (RKs) and receptor-like proteins (RLPs) as pattern recognition receptors (PRRs) to monitor their apoplastic environment and detect non-self and damaged-self patterns as signs of potential danger. Plant PRRs contribute to both basal and non-host resistances, and treatment with pathogen-/microbe-associated molecular patterns (PAMPs/MAMPs) or damage-associated molecular patterns (DAMPs) recognized by plant PRRs induces both local and systemic immunity. Here, we comprehensively review known PAMPs/DAMPs recognized by plants as well as the plant PRRs described to date. In particular, we describe the different methods that can be used to identify PAMPs/DAMPs and PRRs. Finally, we emphasize the emerging biotechnological potential use of PRRs to improve broad-spectrum, and potentially durable, disease resistance in crops.
Norbert Perrimon - One of the best experts on this subject based on the ideXlab platform.
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gene2Function an integrated online resource for gene Function Discovery
G3: Genes Genomes Genetics, 2017Co-Authors: Aram Comjean, Stephanie E Mohr, Norbert PerrimonAbstract:One of the most powerful ways to develop hypotheses regarding the biological Functions of conserved genes in a given species, such as humans, is to first look at what is known about their Function in another species. Model organism databases and other resources are rich with Functional information but difficult to mine. Gene2Function addresses a broad need by integrating information about conserved genes in a single online resource.
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gene2Function an integrated online resource for gene Function Discovery
bioRxiv, 2017Co-Authors: Aram Comjean, Stephanie E Mohr, Norbert PerrimonAbstract:One of the most powerful ways to develop hypotheses regarding biological Functions of conserved genes in a given species, such as in humans, is to first look at what is known about Function in another species. Model organism databases (MODs) and other resources are rich with Functional information but difficult to mine. Gene2Function (G2F) addresses a broad need by integrating information about conserved genes in a single online resource.
Peter G Klein - One of the best experts on this subject based on the ideXlab platform.
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opportunity Discovery entrepreneurial action and economic organization
Strategic Entrepreneurship Journal, 2008Co-Authors: Peter G KleinAbstract:This article reviews and critiques the opportunity Discovery approach to entrepreneurship and argues that entrepreneurship can be more thoroughly grounded, and more closely linked to more general problems of economic organization by adopting the Cantillon-Knight-Mises understanding of entrepreneurship as judgment. The article begins by distinguishing among occupational, structural, and Functional approaches to entrepreneurship and distinguishing among two influential interpretations of the entrepreneurial Function—Discovery and judgment. It turns next to the contemporary literature on opportunity identification and argues that this literature misinterprets Kirzner's instrumental use of the Discovery metaphor and mistakenly makes opportunities the unit of analysis. The article then describes an alternative approach in which investment is the unit of analysis and link this approach to Austrian capital theory. I close with some applications to organizational form and entrepreneurial teams. Copyright © 2008 Strategic Management Society.
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opportunity Discovery entrepreneurial action and economic organization
2008Co-Authors: Peter G KleinAbstract:This paper reviews and critiques the "opportunity Discovery" approach to entrepreneurship and argues that entrepreneurship can be more thoroughly grounded, and more closely linked to more general problems of economic organization, by adopting the Cantillon-Knight-Mises understanding of entrepreneurship as judgment. I begin by distinguishing among occupational, structural, and Functional approaches to entrepreneurship and distinguishing among two influential interpretations of the entrepreneurial Function, Discovery and judgment. I turn next to the contemporary literature on opportunity identification and argue that this literature misinterprets Kirzner's instrumental use of the Discovery metaphor and mistakenly makes "opportunities" the unit of analysis. I then describe an alternative approach in which investment is the unit of analysis and link this approach to Austrian capital theory. I close with some applications to organizational form and entrepreneurial teams.
Freddy Boutrot - One of the best experts on this subject based on the ideXlab platform.
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Function Discovery and exploitation of plant pattern recognition receptors for broad spectrum disease resistance
Annual Review of Phytopathology, 2017Co-Authors: Freddy Boutrot, Cyril ZipfelAbstract:Plants are constantly exposed to would-be pathogens and pests, and thus have a sophisticated immune system to ward off these threats, which otherwise can have devastating ecological and economic consequences on ecosystems and agriculture. Plants employ receptor kinases (RKs) and receptor-like proteins (RLPs) as pattern recognition receptors (PRRs) to monitor their apoplastic environment and detect non-self and damaged-self patterns as signs of potential danger. Plant PRRs contribute to both basal and non-host resistances, and treatment with pathogen-/microbe-associated molecular patterns (PAMPs/MAMPs) or damage-associated molecular patterns (DAMPs) recognized by plant PRRs induces both local and systemic immunity. Here, we comprehensively review known PAMPs/DAMPs recognized by plants as well as the plant PRRs described to date. In particular, we describe the different methods that can be used to identify PAMPs/DAMPs and PRRs. Finally, we emphasize the emerging biotechnological potential use of PRRs to imp...
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Function Discovery and exploitation of plant pattern recognition receptors for broad spectrum disease resistance
Annual Review of Phytopathology, 2017Co-Authors: Freddy Boutrot, Cyril ZipfelAbstract:Plants are constantly exposed to would-be pathogens and pests, and thus have a sophisticated immune system to ward off these threats, which otherwise can have devastating ecological and economic consequences on ecosystems and agriculture. Plants employ receptor kinases (RKs) and receptor-like proteins (RLPs) as pattern recognition receptors (PRRs) to monitor their apoplastic environment and detect non-self and damaged-self patterns as signs of potential danger. Plant PRRs contribute to both basal and non-host resistances, and treatment with pathogen-/microbe-associated molecular patterns (PAMPs/MAMPs) or damage-associated molecular patterns (DAMPs) recognized by plant PRRs induces both local and systemic immunity. Here, we comprehensively review known PAMPs/DAMPs recognized by plants as well as the plant PRRs described to date. In particular, we describe the different methods that can be used to identify PAMPs/DAMPs and PRRs. Finally, we emphasize the emerging biotechnological potential use of PRRs to improve broad-spectrum, and potentially durable, disease resistance in crops.
Evelina Fedorenko - One of the best experts on this subject based on the ideXlab platform.
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fmri reveals language specific predictive coding during naturalistic sentence comprehension
Neuropsychologia, 2020Co-Authors: Cory Shain, Idan Blank, Marten Van Schijndel, William Schuler, Evelina FedorenkoAbstract:Much research in cognitive neuroscience supports prediction as a canonical computation of cognition across domains. Is such predictive coding implemented by feedback from higher-order domain-general circuits, or is it locally implemented in domain-specific circuits? What information sources are used to generate these predictions? This study addresses these two questions in the context of language processing. We present fMRI evidence from a naturalistic comprehension paradigm (1) that predictive coding in the brain's response to language is domain-specific, and (2) that these predictions are sensitive both to local word co-occurrence patterns and to hierarchical structure. Using a recently developed continuous-time deconvolutional regression technique that supports data-driven hemodynamic response Function Discovery from continuous BOLD signal fluctuations in response to naturalistic stimuli, we found effects of prediction measures in the language network but not in the domain-general multiple-demand network, which supports executive control processes and has been previously implicated in language comprehension. Moreover, within the language network, surface-level and structural prediction effects were separable. The predictability effects in the language network were substantial, with the model capturing over 37% of explainable variance on held-out data. These findings indicate that human sentence processing mechanisms generate predictions about upcoming words using cognitive processes that are sensitive to hierarchical structure and specialized for language processing, rather than via feedback from high-level executive control mechanisms.
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fmri reveals language specific predictive coding during naturalistic sentence comprehension
bioRxiv, 2019Co-Authors: Cory Shain, Evelina Fedorenko, Idan Blank, Marten Van Schijndel, William SchulerAbstract:Abstract Much research in cognitive neuroscience supports prediction as a canonical computation of cognition in many domains. Is such predictive coding implemented by feedback from higher-order domain-general circuits, or is it locally implemented in domain-specific circuits? What information sources are used to generate these predictions? This study addresses these two questions in the context of language processing. We present fMRI evidence from a naturalistic comprehension paradigm (1) that predictive coding in the brain’s response to language is domain-specific, and (2) that these predictions are sensitive both to local word co-occurrence patterns and to hierarchical structure. Using a recently developed deconvolutional time series regression technique that supports data-driven hemodynamic response Function Discovery from continuous BOLD signal fluctuations in response to naturalistic stimuli, we found we found effects of prediction measures in the language network but not in the domain-general, multiple-demand network. Moreover, within the language network, surface-level and structural prediction effects were separable. The predictability effects in the language network were substantial, with the model capturing over 37% of explainable variance on held-out data. These findings indicate that human sentence processing mechanisms generate predictions about upcoming words using cognitive processes that are sensitive to hierarchical structure and specialized for language processing, rather than via feedback from high-level executive control mechanisms.
