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Zymoseptoria tritici stealth infection is facilitated by stage-specific downregulation of a β-glucanase
Item type: Journal Article
Rebaque , Diego; Carrasco-López , Cristian; Krishnan , Parvathy; et al. (2025)
Plant cell walls constitute a major defence barrier against pathogens, although it is unclear how specific cell wall components impact pathogen colonisation. Pathogens secrete cell wall-degrading enzymes (CWDEs) to facilitate plant colonisation, but damaged or infected cells are often a source of cell wall-derived oligosaccharides that trigger host immunity. The mechanisms by which pathogens minimise the release of cell wall-derived oligosaccharides while colonising the host remain to be elucidated. We combined biochemical, molecular, and transcriptomic analyses to functionally characterise a glycoside hydrolase (ZtGH45) from the wheat pathogen Zymoseptoria tritici. ZtGH45 gene is expressed during the necrotrophic phase. At this stage, wheat β-1,3/1,4-mixed-linked glucan (MLG)-derived oligosaccharides are also accumulated. We show that overexpression of ZtGH45 in Z. tritici enhances hydrolysis of MLG from wheat cell walls, and the released MLG oligosaccharides trigger an immune response in wheat. The results demonstrate that tight regulation of ZtGH45 is critical for the infection process as it prevents early accumulation of MLG oligosaccharides that would prematurely induce host immunity, thereby counterbalancing fungal virulence. We suggest that the balance between plant cell wall degradation by fungal CWDEs and the release of immunogenic wall-derived oligosaccharides governs the outcome of host invasion by pathogens.
Toward a Dynamic Closed-Loop Neuromorphic Trajectory Interpolation Model: Hardware Emulation and Software Simulation
Item type: Journal Article
Casanueva-Morato, Daniel; Wu, Chenxi; Indiveri, Giacomo; et al. (2025)
Neuromorphic engineering aims to incorporate the computational principles found in animal brains into modern technological systems. Controllers for robotic arms targeting specific positions often fall short in executing dynamically smooth trajectories, exhibiting rather segmented, step-like motions. This brief presents a closed-loop neuromorphic control system for event-based robotic arms, emphasizing a dynamic approach to trajectory interpolation through hardware emulation and software simulation. Our model employs a Shifted Winner-Take-All spiking network to interpolate reference trajectories and a spiking comparator network to ensure trajectory continuity against real-time positions, closing the control loop dynamically. The model's implementation on various neuromorphic platforms highlights its flexibility and adaptability across distinct computational paradigms, such as analog hardware emulation and digital software simulation. Experimental results demonstrate the efficacy of the analog implementation in terms of robustness and energy efficiency, while the digital simulations produce precise and stable performance. These outcomes substantiate the model's capacity to enhance robotic trajectory control and lay the foundation for the development of future neuromorphic robotic control systems.
Ultrasound-induced Particle Dynamics in Pathological Vascular Vortices
Item type: Working Paper
Medany, Mahmoud; Nama, Nitesh; Ahmed, Daniel (2025)
Disturbed flow is a hallmark of diseased vasculature, yet its influence on particle behavior under external actuation remains poorly understood. We uncover distinct behaviors of microparticles under disturbed flow when exposed to ultrasound, revealing selective trapping and aggregation phenomena that differ fundamentally between soft and rigid particles. Using microfluidic models of disturbed vascular flow, we show that microbubbles become trapped at the eye of vortices and self-assemble via ultrasound-induced forces. As clusters grow to a critical size, they are ejected and adhere to the wall opposite the ultrasound source, forming nuclei that progressively occupy aneurysm cavities—a mechanism that could enable targeted, noninvasive treatment. These findings reveal an unexplored interplay between ultrasound and hydrodynamic forces, offering a new strategy for ultrasound-guided therapeutic delivery in vascular disease.
Intact endogenous pain inhibition in complex regional pain syndrome type 1
Item type: Journal Article
Allmendinger, Florin; Sirucek, Laura; De Schoenmacker, Iara; et al. (2025)
Introduction:
Impaired endogenous pain modulation is a common characteristic of various chronic pain conditions and may be a key mechanism underlying the development of chronic pain in complex regional pain syndrome (CRPS). One way to assess endogenous pain modulation in humans is through conditioned pain modulation (CPM) paradigms.
Objectives:
This study aimed to assess CPM capacity in chronic CRPS type 1 and its potential association with the individual pain phenotype.
Methods:
Sixteen individuals with chronic CRPS type 1 and 15 age- and sex-matched healthy controls (HC) were enrolled. Parallel and sequential CPM paradigms were performed at 2 testing sites: the most painful area and a remote, pain-free area using pressure pain thresholds as a test stimulus and a cold water bath as a conditioning stimulus. Individuals with CRPS underwent pain phenotyping to assess the characteristics of CRPS-related pain.
Results:
The CPM capacity did not differ between individuals with CRPS and HC (P's > 0.05). Complex regional pain syndrome showed intact pain inhibition assessed in both areas in the parallel and sequential CPM paradigm (P's < 0.05). Furthermore, the CPM capacity of individuals with CRPS did not correlate with individual pain characteristics (ie, pain intensity, pain extent, and pain duration; P's > 0.05).
Conclusion:
Our results indicate that in individuals with chronic CRPS type 1, endogenous pain inhibition is intact and comparable with healthy individuals. Therefore, pathomechanisms underlying chronic CRPS are likely related to increased pain facilitatory rather than impaired inhibitory processes.
Deep learning-based object detection on LiDAR-derived hillshade images: insights into grain size distribution and longitudinal sorting of debris flows
Item type: Journal Article
Schmid, Paul E.; Hirschberg, Jacob; Spielmann, Raffaele; et al. (2025)
Debris flows are hazardous natural phenomena characterized by rapid movements of sediment-water mixtures in steep channels, posing significant risks to life and infrastructure. Better understanding and managing these hazards requires new methods to collect and process high-resolution data. This study introduces a novel method that leverages hillshade images derived from a high temporal resolution LiDAR scanner and deep learning-based object detection models to analyze debris-flow dynamics. By transforming 3D point clouds into hillshade projections, the method enables efficient detection and tracking of key flow features, including boulders, rolling boulders, surge waves, and woody debris, independent of ambient light conditions. Outputs include object velocities, sizes, and tracks, offering high-resolution insights into debris-flow phenomena such as longitudinal sorting. Six state-of-the-art object detection models were evaluated, with YOLOv11 achieving the best balance of precision, recall, and processing speed. We used the framework to calculate dynamic grain size distributions and found that the median grain size decreased continuously throughout the event. The proposed framework is scalable, significantly reduces processing time compared to manual analysis, and sets the foundation for real-time monitoring and early-warning of debris flows across diverse locations and conditions.