Sebastian Eves-van den Akker

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Sebastian Eves-van den Akker

Sebastian Eves-van den Akker

@Seb_EvdA

Professor of Biotic Interactions, University of Cambridge. @plantsci Fellow of King's College Cambridge. https://t.co/jwE0Oewr6U

Cambridge, England Katılım Mayıs 2014
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Sebastian Eves-van den Akker
Paper @PNASNews We think nematodes break open plant cells, sense small molecules (termed effectostimulins), which switch on a master regulator SUGR1, which switches on effectors, which break cells. Looks like a feed-forward loop driving infection. pnas.org/doi/10.1073/pn…
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Massimo
Massimo@Rainmaker1973·
The Robotics team from Wissahickon High School in Ambler, Pennsylvania, built the robot Miss Daisy XXIV that picks up balls and shoots them into a container.
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Shahid Siddique
Shahid Siddique@NemaPlant·
Excited to share our PNAS commentary, “A nematode-built conduit for cross-kingdom biotrophic interaction,” on a fascinating recent discovery in root-knot nematode biology from @MitchumLab . Please take a look : lnkd.in/g-EYB89C
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Yunha Hwang
Yunha Hwang@Micro_Yunha·
Applications for MIT Novo-Nordisk AI postdoc fellowships are due Apr 15. Focus area lists AI and Biology topics, apply to work on this exciting field with amazing peers! engineering.mit.edu/novo-nordisk
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Johnathan Napier
Johnathan Napier@JohnathanNapie1·
PhD studentship at Rothamsted - investigating/overcoming transgene-silencing in plant biotechnology. Interested in doing a project that combines novelty and real-world utility? See here for details! rothamsted.ac.uk/studentship/su…
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nature
nature@Nature·
AlphaFold database now includes 1.7 million 'homodimers' - comprising two interacting strands of the same molecule go.nature.com/3NxCOea
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MPMI Journal
MPMI Journal@MPMIjournal·
NEW H. H. Flor Distinguished Review: All pathogens must sense that they have arrived at their host. This is a necessary part of infection in order to effect the changes in pathogen biology required to progress through their life cycle. Rachel Hammond et al. review recent literature and provide speculation as to how this might happen, by analogy to the five human senses. Read “The Five Senses: How Do Plant Pathogens Know They Found Their Host?” to learn more: doi.org/10.1094/MPMI-1… @Seb_EvdA
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Zoe (is building utopia 🚀) || bio/acc 🧬
Many good biology ideas never get tested because the researcher can't afford $2,000 in lab supplies. At @PrimordiaGrants we aim to close that gap by funding tightly scoped experiments that can de-risk impactful ideas in 3-6 months. Apply now 👇
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Niko McCarty.
Niko McCarty.@NikoMcCarty·
Fast Biology Bounties close this Sunday at midnight. So far, we've given away about $8,000, and expect to give about $15,000 by the end. Please send in your ideas to speed up wet-lab biology! A couple paragraphs are sufficient.
Niko McCarty. tweet media
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Oded Rechavi
Oded Rechavi@OdedRechavi·
A new mechanism for “RNA memory”! 😱 Thrilled to share another crazy paper from the lab (can’t believe we posted 2 in 2 days!), summarizing >10 years of research: Work on transgenerational inheritance of small RNAs in the powerful model organism C. elegans changed how we think about what’s possible in inheritance and evolution, because it allows the most heretical thing: inheritance of parental responses to the environment! However, it’s still unclear whether RNAs are inherited across generations in other animals, largely because the RNA-dependent RNA polymerases that amplify heritable small RNAs and prevent their dilution in C. elegans are not conserved in mammals. In this new work, an amazing collaboration with the Rink and Wurtzel labs, we show that planarians establish long-lasting and heritable small RNA–based gene regulatory states despite lacking canonical RNA-dependent RNA polymerases and nuclear RNAi machinery (that are required in C. elegans). You might say “they are both worms…” BUT planarians are evolutionarily very distant from C. elegans (flatworms vs. roundworms, diverged more than 500 million years ago), making this particularly surprising. These are totally different animals. We find that ingestion of double-stranded RNA induces sequence-specific silencing that persists for months and survives repeated cycles of whole-body regeneration. Even more strikingly, RNAi can be transferred between animals, echoing James V. McConnell’s controversial “RNA memory” experiments from the 1970s (his lab was targeted by the Unabomber terrorist Ted Kaczynski, who sent McConnell a bomb. This and other controversies ended this line of experiments…) Mechanistically, we find that the response transitions from a transient systemic dsRNA-triggered phase to a stable, cell-autonomous post-transcriptional “memory phase” maintained by antisense small RNAs. Using a new luminescence reporter (transgenesis is currently impossible in planarians), we show that silencing spreads along the targeted gene and identify a weird type of planarian small RNAs with untemplated polyA tails. RNAi inheritance without canonical RdRPs establishes planarians as a powerful system for studying RNA-based regulatory inheritance beyond C. elegans and raises the possibility that RNA-mediated inheritance may be more broadly conserved in animals, potentially even in mammals. Here’s a video of a planarian that is treated by RNAi against β-catenin and develops multiple heads instead of just one. This is one of the phenotypes that is inherited. Another phenotype is “loss of eyes” (which we show is not only inherited across multiple regeneration cycles, but can also be transmitted between animals in transplantation experiments). Amazing work led by first authors Prakash Cherian and Idit Aviram (co-supervised by Omri and me). Please read the preprint, the link is in the next tweet, and share!
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Oded Rechavi
Oded Rechavi@OdedRechavi·
Humanity is notorious for leaving problems for the next generations, but in animals we are discovering the biological mechanisms: we found that C. elegans develop germline tumors if THEIR GRAND GRANDPARENTS’ cells fail to clear the garbage from their body cavity… What the hell, you ask? Read our new preprint! 👇 Congrats to Itai Rieger, Yael Mor, Itamar Lev, and all the other authors on a superb job (this was many years in the making): “Scavenger Cells Failure to Maintain Systemic RNA Homeostasis Causes Epigenetically Inherited Germline Tumors” biorxiv.org/content/10.648…
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Laura 🌲 ⛰️
Laura 🌲 ⛰️@LauraDeming·
reversible cryo is one of the most important and beautiful ideas I've ever met wrote a primer about it for the curious! might go through another draft so feedback welcome :) notebook.ldeming.com/whyilovecryo/
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Sophien Kamoun
Sophien Kamoun@KamounLab·
Hugely honored to attend Professor Ryohei Terauchi's @ryoheiterauchi lecture at Kyoto University @KyotoU_News. What a spectacular career exploring crop evolution and molecular plant pathology / immunity. And a lasting impact on many Early Career Scientists.
Sophien Kamoun tweet mediaSophien Kamoun tweet media
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Abdul Șhakoor
Abdul Șhakoor@abxxai·
BREAKING: 🚨 Someone just tested 35 AI models across 172 billion tokens of real document questions. The hallucination numbers should end the "just give it the documents" argument forever. Here is what the data actually showed. The best model in the entire study, under perfect conditions, fabricated answers 1.19% of the time. That sounds small until you realize that is the ceiling. The absolute best case. Under optimal settings that almost no real deployment uses. Typical top models sit at 5 to 7% fabrication on document Q&A. Not on questions from memory. Not on abstract reasoning. On questions where the answer is sitting right there in the document in front of it. The median across all 35 models tested was around 25%. One in four answers fabricated, even with the source material provided. Then they tested what happens when you extend the context window. Every company selling 128K and 200K context as the hallucination solution needs to read this part carefully. At 200K context length, every single model in the study exceeded 10% hallucination. The rate nearly tripled compared to optimal shorter contexts. The longer the window people want, the worse the fabrication gets. The exact feature being sold as the fix is making the problem significantly worse. There is one more finding that does not get talked about enough. Grounding skill and anti-fabrication skill are completely separate capabilities in these models. A model that is excellent at finding relevant information in a document is not necessarily good at avoiding making things up. They are measuring two different things that do not reliably correlate. You cannot assume a model that retrieves well also fabricates less. 172 billion tokens. 35 models. The conclusion is the same across all of them. Handing an LLM the actual document does not solve hallucination. It just changes the shape of it.
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Bo Wang
Bo Wang@BoWang87·
This post is getting popular, making me believe the old-time technical Twitter community is back! More info on the work: 📺 Detailed walkthrough from the authors: youtube.com/watch?v=9JhVKu… 📰 Nature report: nature.com/articles/d4158… That said, worth noting the limitations of this work: → JCVI-Syn3A has <500 genes, and ~94 of them still have unknown functions. The simulation routes around those gaps — it doesn't model them → Kinetic rate parameters are largely borrowed from other, better-studied organisms. There isn't a complete set measured directly in Syn3A → Resolution is 10 nanometers — molecular positions, not atoms. It's coarse-grained, not a physics simulation from first principles → The model is a hybrid of stochastic and deterministic methods to handle the timescale differences. Elegant engineering, but not a single unified framework None of this diminishes the achievement. But the gap between "simulated the simplest possible cell" and "simulate a human cell responding to a drug or any interventions" is where the next 5-10 years of work lives.
YouTube video
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Bo Wang tweet media
Bo Wang@BoWang87

This is really cool (and wild): Scientists simulated a complete living cell for the first time. Every molecule, every reaction, from DNA replication to cell division. The paper (Luthey-Schulten et al., Cell 2026, doi.org/10.1016/j.cell…), just out today, used JCVI-Syn3A — a synthetic minimal bacterium with fewer than 500 genes. A 3D+time simulation of the full 105-minute cell cycle: DNA replication, protein translation, metabolism, division. Every gene, protein, RNA, and chemical reaction tracked through physical space. It took years to build. Multiple GPUs. Six days of compute time per run. And this is the simplest possible cell. A human cell has ~20,000 genes. It lives in tissue. It interacts with neighbors. It differentiates. It responds to drugs in ways that depend on context we haven't fully measured. Mechanistic simulation of the minimal cell costs 6 GPU-days for 105 minutes of biology. You cannot scale that to human cells. The complexity isn't 40x harder. It's exponentially harder. This is why the field pivoted to data-driven models. You can't hand-encode the regulatory wiring of a human hepatocyte. But you can learn it — if you have the right perturbation data collected across enough diverse biological contexts. The two approaches aren't competing. Papers like this generate the ground truth that future ML models need for validation. But the path to a clinically useful virtual cell runs through foundation models, not through scaling up mechanistic simulation. Amazing work!

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