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The late blight pathogen, , poses a significant threat to tomato crops worldwide. To understand the potential for rapid adaptation of this pathogen, we conducted an in vitro experimental evolution study with four US-23 lineages collected from tomato hosts with different combinations of resistance genes ( genes). These isolates were passed serially over eight generations on five tomato cultivars. After infection, targeted sequencing of the pathogen's RXLR effector genes was done. In just eight generations, we observed both phenotypic and genotypic changes in the US-23 lineages, with differences in disease severity among pathogen isolates and alterations in the RXLR genome. Our findings suggest rapid mutation even in a clonally reproducing lineage, highlighting the potential for adaptation of within a single growing season on tomato. These insights shed light on the adaptability of this devastating pathogen and emphasize the importance of considering tomato host resistance in late blight management strategies.
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http://dx.doi.org/10.1094/PHYTO-12-24-0401-R | DOI Listing |
Neurol Sci
September 2025
Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy.
The rapid evolution of digital tools in recent years after COVID-19 pandemic has transformed diagnostic and therapeutic practice in neurology. This shift has highlighted the urgent need to integrate digital competencies into the training of future specialists. Key innovations such as telemedicine, artificial intelligence, and wearable health technologies have become central to improving healthcare delivery and accessibility.
View Article and Find Full Text PDFNat Genet
September 2025
Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
Aberrant DNA methylation has been described in nearly all human cancers, yet its interplay with genomic alterations during tumor evolution is poorly understood. To explore this, we performed reduced representation bisulfite sequencing on 217 tumor and matched normal regions from 59 patients with non-small cell lung cancer from the TRACERx study to deconvolve tumor methylation. We developed two metrics for integrative evolutionary analysis with DNA and RNA sequencing data.
View Article and Find Full Text PDFLight Sci Appl
September 2025
National Laboratory of Solid State Microstructures, Key Laboratory of Intelligent Optical Sensing and Manipulation, College of Engineering and Applied Sciences, Nanjing University, 210023, Nanjing, China.
Planar optical elements incorporating space-varying Pancharatnam-Berry phase have revolutionized the manipulation of light fields by enabling continuous control over amplitude, phase, and polarization. While previous research focusing on linear functionalities using apolar liquid crystals (LCs) has attracted much attention, extending this concept to the nonlinear regime offers unprecedented opportunities for advanced optical processing. Here, we demonstrate the reconfigurable nonlinear Pancharatnam-Berry LC diffractive optics in photopatterned ion-doped ferroelectric nematics.
View Article and Find Full Text PDFJ Neurosci Methods
September 2025
Department of Computer Science and Engineering, IIT (ISM) Dhanbad, Dhanbad, 826004, Jharkhand, India. Electronic address:
Background: Interpretation of motor imagery (MI) in brain-computer interface (BCI) applications is largely driven by the use of electroencephalography (EEG) signals. However, precise classification in stroke patients remains challenging due to variability, non-stationarity, and abnormal EEG patterns.
New Methods: To address these challenges, an integrated architecture is proposed, combining multi-domain feature extraction with evolutionary optimization for enhanced EEG-based MI classification.
Cell
September 2025
Centre for Bacterial Resistance Biology, Imperial College London, London SW7 2AZ, UK; Fleming Initiative, Imperial College London, London W2 1NY, UK; Department of Life Sciences, Imperial College London, London SW7 2AZ, UK. Electronic address:
Artificial intelligence (AI) models have been proposed for hypothesis generation, but testing their ability to drive high-impact research is challenging since an AI-generated hypothesis can take decades to validate. Here, we challenge the ability of a recently developed large language model (LLM)-based platform, AI co-scientist, to generate high-level hypotheses by posing a question that took years to resolve experimentally but remained unpublished: how could capsid-forming phage-inducible chromosomal islands (cf-PICIs) spread across bacterial species? Remarkably, the AI co-scientist's top-ranked hypothesis matched our experimentally confirmed mechanism: cf-PICIs hijack diverse phage tails to expand their host range. We critically assess its five highest-ranked hypotheses, showing that some opened new research avenues in our laboratories.
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