Hey everyone,

I'm Luca Celebrano, a medical student at University of Naples Federico II. For the past few months I’ve been somewhat obsessed with a clinical puzzle: why do so many EGFR-mutated lung cancer patients relapse after targeted therapy (like osimertinib), even when their initial scans look amazing?

Most literature focuses purely on genetic secondary mutations, but looking at histology, I kept wondering about the physical transport side. Severe stroma compression and high interstitial pressure could simply be preventing the drug from physically reaching cytotoxic levels in pockets of the tissue, creating quiet little sanctuaries where cells survive.

I sat down and tried to model the continuum physics using a cylindrical reaction-diffusion solver (Bessel K0 radial gradients) around capillaries, and then ran survival models against \~3,400 patients from cBioPortal/TCGA to see if the signal actually held up clinically.

I just put up the preprint and code:

\- Preprint (Zenodo): https://doi.org/10.5281/zenodo.24118328

\- Repo: https://github.com/Lukecele/inflammatory-onco-physics

Since I come from medicine and not pure physics, my transport math might be a bit rough around the edges. I would really appreciate an honest sanity check from anyone who does continuum mechanics or tumor biophysics: does this simplification hold water, or am I overcooking the assumptions?

Thanks a lot,

Luca

can someone help me please ?

doi : 10.1109/IPEC61310.2024.00090

url : Evaluation Method of Node Importance in Complex Networks Based on K-Shell Position and Node Information Entropy | IEEE Conference Publication | IEEE Xplore

Hi! I’m really interested in computational biology, especially modeling biological and physiological systems. I’d love to work on mathematical models and simulations of the human body, maybe even develop digital twins someday.

I’m considering studying computer science, but I’m wondering if it’s a good starting point for this field.

For those working in computational biology, do you think CS is a good choice? Or would biomedical engineering be a better option?

I’d love to hear your thoughts and experiences!

Hi all,

We've been developing Bilt, an open-source Python framework for equation-based modelling, at the University of Cambridge's Aviation Impact Accelerator.

The idea is to define physical relationships without having to specify how the equations should be solved. Bilt works out the dependencies and determines how to calculate the unknowns from the available inputs.

For example, you might have a model relating energy input, output and efficiency. You can use the same model to calculate the output, work backwards to find the required input, or determine the efficiency from measurements, without having to rewrite the equations.

This kind of acausal modelling is well established in tools like Modelica, but there doesn't seem to be much available in the Python ecosystem that takes a similar approach. The advantage of doing this directly in Python is that models can make use of the wider scientific Python ecosystem, including existing numerical libraries, type checking, testing and development tools, without needing a separate modelling language or toolchain.

We've tried to keep Bilt compact and make use of existing Python tooling wherever possible, rather than introducing a new modelling language.

It currently supports physical units and dimensional analysis, named tensors, reusable subsystems, conditional equations and automatic structural analysis of equation systems. We're also working on more general coupled nonlinear solves, differentiation and closer integration with JAX.

Our initial applications have been in aviation and energy systems modelling, but Bilt isn't specific to either.

I'd be interested to hear what people think, particularly if you're working on similar problems or know of other approaches in Python that we should be looking at.

We're still actively developing it, so feedback and contributions would be very welcome.

GitHub: https://github.com/aviation-impact-accelerator/bilt

Documentation: https://aviation-impact-accelerator.github.io/bilt/

Do not trust artificial things because #CannotCompute!

context:- i am a 1st year student majoring in physics. thing is i am interested in computational physics (mainly DFT simulations and ML analysis of data). i want to learn these concepts and get proficient at them, but the thing is, our uni doesnt guve HPC access until the 4th year, so i am settling for a rig of my own. budget isnt an issue, but im confused as to what i wanna buy. should i buy a PC with an NVIDIA GPU (im targetting smth with 8-12 GB VRAM) and a good processor with a hgih core count (i have found one with 16 cores, 32 threads, and it has FP64 at 2.5 TFLOPS) or should i buy a mac? the reason why im so keen on the devuce is bcz i want to learn DFT and ML buy running small-medium scale programs locally before eventually gettung access to HPC via projects where i show competence in programming and thr subject itself. please tell me based on my above reasons, and also tell me the specs of the device i need to buy.

In January 1978 influenza went through an English boarding school (763 boys; the daily bed counts are a classic dataset). I built a tool that asks: given the counts seen so far, which values of R0 are still consistent with the data - and can that be *proved*, not just estimated?

With the first 5 days of counts, SIR models with R0 = 2.52 and R0 = 20 both fit every count (under a stated counting-error allowance of 3*sqrt(c) + 5 boys). Twindex identified the next-day count that would separate them, and the real count (298) ruled out both. With 6 days, the most different pair it finds is 3.19 vs 6.10.

Each claim is an exact certificate checked with rational arithmetic and validated error bounds (maximum-principle barriers for PDEs, a log-norm tube argument for ODEs). You can replay them in your browser: https://arnav-m10.github.io/Twindex/demo/

Caveats: the noise allowance is an assumption, bed counts are not exactly infections, and a twin pair is a lower bound on uncertainty, not the full range.

Code: https://github.com/Arnav-M10/Twindex - pip install twindex

Ramanujan-Computing is an MIT-licensed open-source project that aim to harness idle compute power to move science forward.

It can use both CPU and GPU of device connected to the Ramanujan-Cluster.

The way it is different from BOINC is: for a new kind of simulation, you dont need to create a new binary and get it distributed to the devices. There is one binary per architecture which can run any simulation you want!

Ramanujan-Computing can also help in distributed LLM-inferencing: https://www.youtube.com/watch?v=LmXZAb8XYPc . Qwen-27 B model has successfully run on the Ramanujan-Computing. Just give the GGUF, and it will shard the inferencing across devices on cluster.

Apart from LLM, Ramanujan-Computing has main application in solving problems which can be broken to smaller problems and distributed across device. We have run Lattice Boltzmann Method (LBM) CFD Simulation computation on Ramanujan-computing.

Ramanujan-Computing support all the devices which have arithemetic and network capablity. Currently, we have binaries for Android, Linux, Windows, Mac. We can compile the binary for other architectures as well.

Inpiration: In 1969, humans landed on the moon with the help of Apollo Guidance Computer which has compute power equivalent to scientific calculator. Today we have billions of device with million times more compute lying mostly idle. Why not use it to help science move forward.

Project source: https://gitlab.com/ramanujan-computing/Ramanujan

Ramanujan-Computing portal: https://portal.ramanujan.dev/

This paper includes discussion on software development towards PDEs