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The plant microbiome plays a vital role in host fitness. However, the complexity of plant systems makes it difficult to disentangle the roles of individual bacterial species and their interactions with the host. Here, we developed two screening approaches using Chlamydomonas reinhardtii as a model for investigating bacterial pathogenicity and host immunity. First, we measured the effect of ~120 bacterial strains previously isolated from healthy Arabidopsis thaliana roots in a halo assay. We found nine bacterial strains with inhibitory effect on C. reinhardtii growth, all of which previously demonstrated pathogenicity towards A. thaliana, which suggests conserved mechanisms. Focusing on a green lineage specific pathogenic Burkholderia strain (MF6), we revealed it exerts its antagonistic effect through a contact-dependent secretion system. We, next, employed forward genetics in both Chlamydomonas and MF6 to address the genetic basis of pathogenicity and immunity, further characterized by RNA-seq, proteomics and functional assays.In another strategy, we characterized the genetic basis of immunity in a natural habitat. We inoculated the pooled deletion mutant library in Chlamydomonas in soil samples containing various microbial communities and quantified mutant unique barcodes abundance as a proxy for mutant fitness. A promising subset of genes was identified and provides new insights into the defense strategies and potential symbiotic mechanisms employed by green algae with conservation throughout the green lineage.Altogether, these findings highlight conserved plant/alga–bacteria interactions and establish Chlamydomonas as a fascinating system for unraveling the molecular mechanisms of host–pathogen interactions across the plant superkingdom.

As networks become more programmable, they are increasingly built around flexible software components. While this programmability enables new functionality and faster innovation, it also makes network behavior harder to reason about. In this talk, I will present a research agenda that brings ideas from formal methods to programmable networks. In particular, I will present techniques that leverage programmable-network semantics for concurrency safety, traffic monitoring, and failure recovery. More broadly, this work illustrates how semantic foundations can help bring stronger correctness guarantees to modern networked systems.

Bio
Guy Amir is a Postdoctoral Researcher at Cornell University, conducting research at the intersection of formal methods, networking, and systems. He earned his Ph.D. in 2024 from the Hebrew University of Jerusalem, where he studied AI safety, focusing on formally verifying reactive AI systems and interpreting neural networks. He holds an M.Sc. in Computer Science and a B.Sc. in Computational Biology and Computer Science, both from the Hebrew University. He has received Rothschild, Fulbright, AI-Net, and Charles Clore fellowships, as well as an ICML Spotlight and KLA Award.

Despite the dominance of diffusion and flow-based models in visual and multi-modal generation, they remain paradoxically dependent on external encoders for strong semantic representations, leading to limited generalization and unexpected scaling behavior. This reliance suggests a fundamental limitation: standard local denoising provides little incentive for models to develop global semantic structure. To address this, we introduce Self-Flow, a self-supervised framework that unifies representation learning and generative modeling within a single pipeline. By utilizing Dual-Timestep Scheduling to create an information asymmetry across tokens, we force the model to infer corrupted inputs from cleaner context, naturally driving the emergence of strong representations alongside generative capabilities. Because Self-Flow operates without external models, it generalizes seamlessly across modalities and follows expected scaling laws, offering a path toward world models that are both perceptually grounded and semantically rich. Teaser video in this Link.

Bio:

Dr. Hila Chefer is a Research Scientist at Black Forest Labs and an incoming Assistant Professor (Senior Lecturer) at Tel Aviv University. Her research focuses on architecture development and interpretability for visual foundation models, aiming to both understand and advance their generative capabilities. Hila earned her PhD from Tel Aviv University under the supervision of Prof. Lior Wolf, where she developed the de facto leading methods for Transformer interpretability and the Attend-and-Excite framework for text-to-image control. Her work in video generation includes the development of Lumiere during her time at Google and VideoJAM at Meta AI.

Atmospheric mineral dust is a well-established source of nutrients to marine ecosystems,yet its contribution to terrestrial plant nutrition has long been underestimated, largely due tothe assumption that nutrient acquisition occurs predominantly through root uptake fromsoils. Here, we present evidence from controlled greenhouse experiments under ambientand elevated CO₂, laboratory simulations of leaf microenvironments, isotopic andgeochemical tracing, and field fertilization experiments conducted in both a Mediterraneanecosystem and a tropical forest in Puerto Rico, demonstrating that plants can directlyacquire nutrients through their leaf surfaces following atmospheric dust deposition. Usingrare earth elements and Nd isotopes, we distinguish nutrients derived from soils from thosedelivered by deposited atmospheric particles. Laboratory simulations show that mildlyacidic leaf surfaces, together with organic acids secreted by leaves, enhance mineraldissolution and facilitate foliar uptake of dust-borne nutrients. In a pioneering Mediterraneanfield experiment explicitly designed to isolate foliar uptake, we quantified the bioavailablefraction of key nutrients supplied by dust, including P, Fe, Mn, and Cu, and observed clearenrichment of multiple micronutrients in leaf tissues following dust application. These fieldbasedmeasurements enabled the construction of a global geospatial framework integratingdust deposition with soil nutrient fluxes, indicating that dust-derived inputs can constitute ameaningful fraction of total nutrient supply across large regions, and that during dustevents, short-term foliar inputs can rival or exceed soil-derived fluxes. Complementary fieldobservations in a tropical forest in Puerto Rico further reveal foliar nutrient responsesconsistent with direct dust uptake. Building on these results, we outline a pathway forincorporating foliar dust uptake into Earth system representations of terrestrial nutrientcycling by explicitly accounting for atmospheric nutrient inputs at the canopy level and theirinteraction with soil-derived fluxes. Together, these findings identify foliar dust uptake as anoverlooked but consequential nutrient acquisition pathway and highlight its relevance inhighly weathered, nutrient-limited tropical forests, where atmospheric inputs may play acritical role in regulating nutrient availability and carbon–nutrient interactions.

Dear Colleagues,As part of the Multidisciplinary Vesicle Program Webinar Series, we are pleased to invite you to a special webinar entitled: "Introduction to Analytical Ultracentrifugation (AUC)" This session will provide an overview of Analytical Ultracentrifugation (AUC) and its applications in the characterization of extracellular vesicles, nanoparticles, macromolecular complexes and other biological systems. The webinar will highlight the principles of sedimentation analysis, methodological considerations and the advantages of AUC as a powerful label free analytical platform for assessing size distribution, heterogeneity, aggregation state and sample purity. The session is intended for researchers interested in advanced biophysical characterization approaches and scalable analytical solutions for EV and nanoparticle research.