@ThomasMBury

Doing maths in cardiac electrophysiology, climate science and ecology. Postdoc. McGill Uni.

 Montréal, Québec
thomasbury.net/
Joined September 2015
  • Tweets74
  • Following458
  • Followers167
3Media
Combining #AI and dynamical systems theory improves tipping point detection. Check out our new publication in PNAS: “Deep learning for early warning signals of tipping points”. pnas.org/content/118/39/e210… Summary thread below:

Deep learning for early warning signals of tipping points | PNAS

Many natural systems exhibit tipping points where slowly changing environmental conditions spark a sudden shift to a new and sometimes very differe...

pnas.org
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Fantastic postdoc opportunity to work at the interface of maths and climate change: workingat.nioz.nl/o/postdoc-…
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📢 Exciting News! 🧬 Introducing FateNet, a computational method co-developed by @ThomasMBury and I. By combining dynamical systems theory and deep learning, FateNet investigates cell decision-making processes using scRNA-seq data. 🧪 biorxiv.org/content/10.1101/…

FateNet: an integration of dynamical systems and deep learning for cell fate prediction

Understanding cellular decision-making, particularly its timing and impact on the biological system such as tissue health and function, is a fundamental challenge in biology and medicine. Existing...

biorxiv.org
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Bridging #AI and nonlinear dynamics can help us predict tipping points in discrete-time systems, notably heart rhythm! New paper out in @NatureComms with @gilbublab , @globalecochange , @cbauch1 and others: nature.com/articles/s41467-0…

Predicting discrete-time bifurcations with deep learning

Nature Communications - Critical transitions and qualitative changes of dynamics in cardiac, ecological, and economical systems, can be characterized by discrete-time bifurcations. The authors...

nature.com
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Excited to see the research that comes out of this centre!
Sujith R I@SujithRI6 Jun 2023
We have established a Centre of Excellence for studying Critical Transitions in Complex Systems @iitmadras. The Centre will perform research on predicting catastrophic events and developing early warning technologies for natural and engineering systems.
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Listening to Spotify while training an ML model is a bad time...poor laptop.
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Greatly enjoyed #SIAMDS23 so far! 🤓 Check out our 3-part MS tmr & on Thur: “Phase Transitions in Electrophysiological Systems” (MS135, 155 &170) organized by @AravindKumar264 and myself from @SMQB_UoB. @TheSIAMNews
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“The subtle math of a heartbeat gone wrong”, published in @PhysicsToday , is lucid description of our research with @khady_dgn, @gilbublab, and others. Thank you Johanna Miller for taking the time to digest and present our work with such clarity and wit! physicstoday.scitation.org/d…
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Pleased to see my Python package ewstools published with JOSS. It provides tools to compute early warning signals for tipping points in time series data. Give it a spin - tutorials included. #OpenSource
JOSS@JOSS_TheOJ11 Feb 2023
Just published in JOSS: 'ewstools: A Python package for early warning signals of bifurcations in time series data' doi.org/10.21105/joss.05038

ewstools: A Python package for early warning signals of bifurcations in time series data

Bury, T. M., (2023). ewstools: A Python package for early warning signals of bifurcations in time series data. Journal of Open Source Software, 8(82), 5038, https://doi.org/10.21105/joss.05038

joss.theoj.org
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Thomas Bury retweeted
Khady Diagne@khady_dgn11 Jan 2023
Very happy to see my first publication, in @PhysRevLett . Wouldn't have been possible without our great team, congrats everyone! @gilbublab @ThomasMBury
Gil Bub@gilbublab11 Jan 2023
Our article on parasystole is now out in PRL, with a APS news commentary by Phillip Ball. Congats to Khady, Tom, Leon and the rest of the team! News: physics.aps.org/articles/v16… Article: journals.aps.org/prl/abstrac…

Signatures of Competing Cardiac Pacemakers

Computer simulations and experiments with cardiac cells identify signatures of a condition in which two sets of pacemaker cells lead to an abnormal heartbeat.

physics.aps.org
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The mathematics of competing cardiac pacemakers is extraordinary. Really pleased to see this work with @khady_dgn, @gilbublab and others published in @PhysRevLett. Thanks to @philipcball for writing an excellent commentary.
Gil Bub@gilbublab11 Jan 2023
Our article on parasystole is now out in PRL, with a APS news commentary by Phillip Ball. Congats to Khady, Tom, Leon and the rest of the team! News: physics.aps.org/articles/v16… Article: journals.aps.org/prl/abstrac…

Signatures of Competing Cardiac Pacemakers

Computer simulations and experiments with cardiac cells identify signatures of a condition in which two sets of pacemaker cells lead to an abnormal heartbeat.

physics.aps.org
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Thomas Bury retweeted
Plotly
@plotlygraphs1 Sep 2022
#Plotly #Dash and #Python for heart health? ✅ @ThomasMBury used 𝘥𝘢𝘴𝘩.𝘥𝘦𝘱𝘦𝘯𝘥𝘦𝘯𝘤𝘪𝘦𝘴, 𝘱𝘭𝘰𝘵𝘭𝘺.𝘦𝘹𝘱𝘳𝘦𝘴𝘴 and callbacks to explore electrocardiogram recordings on Physionet as a (big) data source, supporting research on cardiac arrhythmias. 🫀
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Interested in building a dashboard to interactively view your data? I've just written a @Medium article on how to do this using @plotly's Dash and Python. We use thousands of ECG recordings from Physionet as a use case: link.medium.com/oj30T6SzWsb

Building a dashboard in Plotly Dash

To explore thousands of ECG recordings on Physionet

link.medium.com
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Our deep learning model is trained on data that has been exclusively detrended using a Lowess filter. Using other filters (or no filter) can yield erroneous outcomes - please preprocess your data using a Lowess filter if using our model! Thank you to @fdabl for catching this.
Fabian Dablander (@fdabl.bsky.social)@fdabl10 May 2022
Early warning signals for tipping points based on deep learning substantially outperform traditional indicators, as @ThomasMBury et al. showed. In a short note, we illustrate an unintended behavior of the method, stressing the importance of preprocessing: psyarxiv.com/j7xug
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Thomas Bury retweeted
Sujith R I@SujithRI7 Sep 2021
It gives me great pleasure to announce the launch of a webinar series on "Critical Transitions in Complex Systems" (ctcs-iitm.com/) jointly hosted by @iitmadras and @PIK_Climate . [1/9]
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#UofGResearch | Ecologist Dr. Madhur Anand (@globalecochange) and colleagues have created a model to account for how social polarization leads to inaction on fighting climate change. @UofG @UofG_SES @UofGResearch fal.cn/3irwQ

When Group Conflicts Heat Up, So Does Earth: U of G Study

Conflict between rich and poor over how to mitigate climate change can itself lead to more global warming – by as much as 0.7 degrees Celsius, according to a new study led by a University of Guelph…

news.uoguelph.ca
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New #SES_research published in @RSocPublishing by our own Dr. Madhur Anand (@globalecochange) in collaboration with @WaterlooMath shows how social systems, such as resource inequalities, influence #ClimateChange. Original: royalsocietypublishing.org/d… #Mitigation #UofG
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In conclusion: a deep learning classifier can be trained on data from a universe of possible models to detect and classify approaching bifurcations/tipping points with better performance than conventional early warning signals.
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Thanks to all collaborators on this work: @cbauch1, @globalecochange, @MartenScheffer, R. I Sujith, Induja Pavithran & Timothy Lenton. All code and data used in this study are available at github.com/ThomasMBury/deep-… and public repositories cited therein.

GitHub - ThomasMBury/deep-early-warnings-pnas: Repository to accompany the publication 'Deep...

Repository to accompany the publication 'Deep learning for early warning signals of tipping points', PNAS (2021) - ThomasMBury/deep-early-warnings-pnas

github.com
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