Steve Pressé

Perspective: An outlook on fluorescence tracking Tracking single fluorescent molecules has provided new insights into dynamic molecular processes at the single-molecule level. This Perspective traces the evolu

Here is the original:

pubs.aip.org/aip/jcp/arti...

Tracing fluorescence tracking’s past, present, and future Artificial intelligence and parallel computing could refine studies of the movement of single molecules inside cells.

Tracking has always been at the ❤️ of the scientific method, from planetary motion to single particles.

Weiqing’s thesis intro on tracking was so beautiful we turned it into a perspective in JCP who then selected it as a #Scilight.

Take a look! 😊

www.aip.org/scilights/tr...

News story our APS fellowship.

A testament to the great people we work with (and the unwavering rigor of Bayesian approaches, impervious to hype) 😊 🙏

news.asu.edu/b/20251021-a...

It’s an Honor to be named 2025 @apsphysics.bsky.social Fellow.

🙏 our lab members and collaborators.

Citation: “For pioneering contributions to Bayesian method development in biological physics, particularly […] to imaging and single-molecule experiments. 

#APSFellow

The journey continues. UT Austin and Oden tomorrow and Friday. Here with Dima Makarov :)
(Discussing Bach, Richter, Gould, Yuja Wang, and maybe some single molecule 😊)

One of my favorites by Voltaire in describing Canada
“quelques arpents de neige, habités par des barbares, des ours et des castors” 🙃

Thanks to the CTBP for the wonderful invitation for a seminar at Rice! 🙏
Joining me are Tolya Kolomeisky and Oleg Igoshin.

Hello everyone,

Tomorrow I’ll be giving a chalk talk on our new eLife:
“REPOP: bacterial population quantification from plate counts”

elifesciences.org/reviewed-pre...

Looking forward to seeing you!!

#eLife #datascience #biophysics #bioinformatics #Bayesian #REPOP

Thanks for the link to your paper!

How Easy Is It to Learn Motion Models from Widefield Fluorescence Single Particle Tracks? Motion models (i.e., transition probability densities) are often deduced from fluorescence widefield tracking experiments by analyzing single-particle trajectories post-processed from data. This analy...

Can we learn motion models from post-processed tracks? 🧐
Not really 😢

Emission noise accounts for ~99% of the likelihood.

TLDR: What you think is anomalous diffusion… might just be noise. 🤷‍♂️🤷‍♂️🤷‍♂️🤷‍♂️

🔗 Read more in our latest preprint: arxiv.org/abs/2507.05599

#Biophysics

GitHub - PessoaP/REPOP Contribute to PessoaP/REPOP development by creating an account on GitHub.

6/6
If you plate, you need REPOP.

Software -- github.com/PessoaP/REPOP
Preprint -- elifesciences.org/reviewed-pre...

Special thanks to the Lab Members @pedropessoaphd.bsky.social, Carol Lu and Stanimir Tashev

As well as Rory Kruithoff and @dpshepherd.bsky.social
#Biophysics #QuantitativeBiology

GitHub - PessoaP/REPOP Contribute to PessoaP/REPOP development by creating an account on GitHub.

5/6
This is why we built REPOP, an #opensource tool to REconstruct POpulations from Plates.

Straightforward to use and with tutorials available on #GitHub

github.com/PessoaP/REPOP

With all the #Bayesian rigor and #PyTorch speed

4/6
As we show in the paper, this
- Overestimatese variability
- Can miss real structure in your population: Subpopulations and/or multimodality as biological differences across samples,

3/6
This assumes:
– No randomness in how many bacteria end up on the plate
– No randomness in the original swab

In reality, every step is noisy.

2/6
Plate counting is a simple:

You dilute a sample, plate a small volume, and count colonies.

Say you dilute by 200×, and count 50 colonies.
Easy just multiply 50 × 200 = 10k bacteria, right?

NOT QUITE...

Hello all,

If you do #PlateCounting, you may want to take a look at our new eLife @elife.bsky.social

If you don't, I still encourage you to join for an interesting discussion.

Follow the thread 🧵

elifesciences.org/reviewed-pre...

#Microbiology #DataScience #PyTorch #QuantitativeBiology #REPOP

3/3
In it, we simulate some general physical systems that violate the HMM's assumptions and demonstrate contradictory results that can arise. Surprisingly, the problems with HMM analysis only grow with better data acquisition (higher data acquisition rate and/or reduced noise).

2/3
HMM are classic in time series analysis, but they can yield confusing, seemingly contradictory results. In particular, when applying HMMs to physical systems where two key HMM assumptions, that state spaces are discrete, and that transitions are instantaneous, don't apply.

A cautious user's guide in applying HMMs to physical systems Nature, as far as we know, evolves continuously through space and time. Yet the ubiquitous hidden Markov model (HMM)--originally developed for discrete time and space analysis in natural language proc...

Have you ever applied a Hidden Markov Model (HMM)?
Does the results seems contradictory?

If so, you are going to love our new preprint:

arxiv.org/abs/2506.05707

YouTube video by Xiaol.x Mamba time series forecasting with uncertainty quantification

So… I was googling myself and made quite a discovery

🎧 There's an AI-generated podcast of my #TimeSeriesForecasting paper 🤔🤔
www.youtube.com/watch?v=3BNz...

Not sure whether to feel flattered, creeped out, or alarmed.

That is the future I guess 🤷‍♂️🤷‍♂️

To read the full paper: doi.org/10.1088/2632...

The Pressé Lab is happy to join the #biophysics #imaging & #singlemolecule scientific communities here.

Hopefully high SNR messaging from this account about low SNR data :)

#science #machinelearning #datamodeling