Activation of pattern-triggered plant immunity requires recognition of microbe-derived molecular patterns (MAMPs) by plant-encoded pattern recognition receptors (PRRs). Many plant PRRs are found in selected plant genera only. Transfer of single PRRs or of cassettes expressing several PRRs (PRR stacking) across plant genus boundaries offers the potential to boost disease resistance by improving pathogen recognition features in economically important crop plants. The success of such an approach is most dependent on the availability of a large number of plant PRRs. Here, an efficient method for the identification of novel PRRs in the model plant Arabidopsis thaliana (hereafter, Arabidopsis for simplicity) is described. This method takes advantage of natural variation in microbial pattern sensitivity among hundreds of Arabidopsis accessions currently available. Identification of pattern-sensitive as well as pattern-insensitive accessions facilitates next-generation sequencing (NGS)-assisted mapping of PRRs. This approach is potentially applicable to the identification of PRRs that recognize patterns of any chemical nature. © 2017 by John Wiley & Sons, Inc.
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http://dx.doi.org/10.1002/cppb.20056 | DOI Listing |
JMIR Med Inform
January 2025
Department of Systems Design Engineering, Faculty of Engineering, University of Waterloo, Waterloo, ON, Canada.
Background: While expert optometrists tend to rely on a deep understanding of the disease and intuitive pattern recognition, those with less experience may depend more on extensive data, comparisons, and external guidance. Understanding these variations is important for developing artificial intelligence (AI) systems that can effectively support optometrists with varying degrees of experience and minimize decision inconsistencies.
Objective: The main objective of this study is to identify and analyze the variations in diagnostic decision-making approaches between novice and expert optometrists.
Immunol Rev
March 2025
Graduate Program in Translational Biology, Medicine, and Health, Virginia Tech, Roanoke, Virginia, USA.
A robust innate immune response is essential in combating viral pathogens. However, it is equally critical to quell overzealous immune signaling to limit collateral damage and enable inflammation resolution. Pattern recognition receptors are critical regulators of these processes.
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February 2025
Jiangsu Key Laboratory of Immunity and Metabolism, Department of Pathogenic Biology and Immunology, Xuzhou Medical University, Xuzhou, Jiangsu, China.
Pattern recognition receptors (PRRs), consisting of Toll-like receptors, RIG-I-like receptors, cytosolic DNA sensors, and NOD-like receptors, sense exogenous pathogenic molecules and endogenous damage signals to maintain physiological homeostasis. Upon activation, PRRs stimulate the sensitization of nuclear factor κB, mitogen-activated protein kinase, TANK-binding kinase 1-interferon (IFN) regulatory factor, and inflammasome signaling pathways to produce inflammatory factors and IFNs to activate Janus kinase/signal transducer and activator of transcription signaling pathways, resulting in anti-infection, antitumor, and other specific immune responses. Palmitoylation is a crucial type of post-translational modification that reversibly alters the localization, stability, and biological activity of target molecules.
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February 2025
ENI-G, a Joint Initiative of the University Medical Center Göttingen and the Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany.
Cricket song recognition is thought to evolve through modifications of a shared neural network. However, the species has an unusual recognition pattern that challenges this view: females respond to both normal male song pulse periods and periods twice as long. Of the three minimal models tested, only a single-neuron model with an oscillating membrane could explain this unusual behavior.
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January 2025
Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Introduction: Generating physician letters is a time-consuming task in daily clinical practice.
Methods: This study investigates local fine-tuning of large language models (LLMs), specifically LLaMA models, for physician letter generation in a privacy-preserving manner within the field of radiation oncology.
Results: Our findings demonstrate that base LLaMA models, without fine-tuning, are inadequate for effectively generating physician letters.
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