INRA, UMR BGPI INRA/CIRAD/SupAgro, Campus International de Baillarguet Montpellier, France.
Published: October 2016
AI Article Synopsis
Article Abstract
Plants are often facing several stresses simultaneously. Understanding how they react and the way pathogens adapt to such combinational stresses is poorly documented. Here, we developed an experimental system mimicking field intermittent drought on rice followed by inoculation by the pathogenic fungus . This experimental system triggers an enhancement of susceptibility that could be correlated with the dampening of several aspects of plant immunity, namely the oxidative burst and the transcription of several pathogenesis-related genes. Quite strikingly, the analysis of fungal transcription by RNASeq analysis under drought reveals that the fungus is greatly modifying its virulence program: genes coding for small secreted proteins were massively repressed in droughted plants compared to unstressed ones whereas genes coding for enzymes involved in degradation of cell-wall were induced. We also show that drought can lead to the partial breakdown of several major resistance genes by affecting plant gene and/or pathogen effector expression. We propose a model where a yet unknown plant signal can trigger a change in the virulence program of the pathogen to adapt to a plant host that was affected by drought prior to infection.
Background: Traditional cognitive assessments, often reliant on paper-and-pencil tests and professional evaluators, suffer from subjectivity and limited result discrimination. This study introduces the Baguan Online Cognitive Assessment System (BOCAS), a tablet-based system that evaluates both general cognitive ability (GCA) and domain-specific functions across six domains: sensory-motor skills, processing speed, sustained attention, working memory, cognitive flexibility, and spatial ability.
Methods: BOCAS was validated with 151 healthy Chinese adults aged 18-40.
Background: Genetic markers are crucial for breeding crops with desired agronomic traits, and their development can be expedited using next-generation sequencing (NGS) and bioinformatics tools. Numerous tools have been developed to design molecular markers, enhancing the convenience, accuracy, and efficiency of molecular breeding. However, these tools primarily focus on genetic variants within short user-input sequences, despite the availability of extensive omics data for genomic variants.
Background: Cerebrospinal fluid (CSF) motion and pulsatility has been proposed to play a crucial role in clearing brain waste. Although its driving forces remain debated, increasing evidence suggests that large amplitude vasomotion drives such CSF fluctuations. Recently, a fast blood-oxygen-level-dependent (BOLD) fMRI sequence was used to measure the coupling between CSF fluctuations and low-frequency hemodynamic oscillations in the human cortex.
Metasurface with natural static structure limits the development of dynamic metasurface holographic display with rapid response and broadband. Currently, liquid crystal (LC) was integrated onto the metasurface to convert the passive metasuface into an active one. But, majority of LC-assisted active metasurfaces often exhibit trade-offs among degree of freedom (DoF, typically less than 2), information capacity, response speed, and crosstalk.
Microscopic imaging is a critical tool in scientific research, biomedical studies, and engineering applications, with an urgent need for system miniaturization and rapid, precision autofocus techniques. However, traditional microscopes and autofocus methods face hardware limitations and slow software speeds in achieving this goal. In response, this paper proposes the implementation of an adaptive Liquid Lens Microscope System utilizing Deep Reinforcement Learning-based Autofocus (DRLAF).
