#Methane is a potent greenhouse gas. A strain of bacteria has been found that eats methane even when it is present at relatively low concentrations. By 2050, the microbe could keep 240 million tons of methane out of the atmosphere. In PNAS: https://t.co/K11qb8CX6w
I’ve never experienced or imagined a fire season like this, which won’t be over for another month or two. >50% of residents of the enormous Northwest Territories 🇨🇦 are displaced. Canada’s largest northern city Yellowknife evacuated last night. PLEASE take note. #ClimateCrisis
Don't miss the opportunity: Our Department @uOttawaCHM at the University of Ottawa is hiring! Check out the link below for more info! Please share with potential candidates.
https://t.co/EXcq3wIG6Q
✨ Join ELIXIR #3DBioInfoCommunity webinar next Tuesday!
👉 @CharlotteMiton: Causes and Consequences of Epistasis in Protein Evolution and Design
👉 @PossuHuangLab: Protein Design for Epitope-specific Molecular Recognition
Register now: https://t.co/L46ssWLYes
Ready to learn about protein design? Our beginner's guide breaks down this complex field into easy-to-understand concepts. Discover the potential of proteins to transform our world: https://t.co/OGrrvh0BnR
Today we report in @NatureBiotech the development of dual-AAV in vivo delivery systems for prime editors that can support efficient prime editing in multiple organs such as brain, liver, and heart.
1/4
Recently finished watching the Deep Learning lecture series by Prof. Frank Noe (@FrankNoeBerlin). Extremely helpful and easy-to-follow resource to learn both fundamental and advanced topics. Strongly recommended. https://t.co/IbOpuXms7R
My lab is hiring 12 #postdocs for 12 projects.
Requirement: #passion & strong #publication record
CV to [email protected]
Not reviewing random applications that do not fit.
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Thanks!
@EngBioRC
We (@ZvxyWu, @francesarnold) wrote a review about machine learning for protein engineering, but there's so many new papers that we can't update it quickly enough. So Zach and I made a public list of papers covering machine learning on proteins. https://t.co/KYKhcDvv2h
New paper giving our perspective for how to explain deep learning models in chemistry. We hope the simplicity and utility is clear, and that more people will start providing explanations for predictions!
By @GWellawatte, @gandhi_heta, Aditi
https://t.co/mtfAc3mFyr