jkh1

Most of the time in bio-related fields, we need high-throughput computing not high-performance computing.

You can track things in live cells with MINFLUX, one of the recent super-resolution techniques coming from Stefan Hell's lab. Edit: add MINFLUX review: https://arxiv.org/pdf/2410.15902

cell/molecular biology

In my field, trying to reproduce results or conclusions from papers happens on a regular basis especially when the outcome matters for projects in the lab. However, whatever the outcome, it can't be published because either it confirms the previous results and so isn't new or it doesn't and no journal wants to publish negative results. The reproducibility attempts are generally discussed at conferences in the corridors between sessions or at the bar in the evening. This is part of how a scientific consensus is formed in a community.

There's no good alternatives on Linux

Maybe I misunderstand what this refers to but there are RDP software for Linux. I've used remmina [1] on Linux for a few years (now I am using VMWare Horizon at work).

[1] https://remmina.org/

Running locally is sometimes necessary, e.g. you don't want to send sensitive data to any random third party server.

Another benchmark: https://www.biorxiv.org/content/10.1101/2024.04.19.590278v2....

How many workflow management systems do we need? Over 300[1]. If that's not reinventing the wheel.

[1] https://github.com/common-workflow-language/common-workflow-...

The problem is that ISO standards are not open.

What seems to help in the life sciences is the existence of public repositories. These could be replaced by portals that collect info on data hosted elsewhere. But the main advantages are that they provide clear, well known places to start looking and they curate, standardize and organise the metada to make it searchable.

In the life sciences there are dedicated structured repositories. These are searchable by keywords and often crossreference each other. They are the goto places for finding data.

In biology we now routinely produce datasets in the multiple terrabytes range. It can easily be n = 3 x 10 TB such as for example imaging 3 fly embryos by light sheet microscopy.

Can I put my 25 TB microscopy image data set on GitHub? Will they host it for free indefinitely? In the life sciences, there are dedicated public repositories (databases) where the data is hosted (free to the researcher), catalogued, standardized and curated to some extent. These repositories are searchable and often crossreference each other. So you can find a data set even if you didn't know about it before. Putting data all over the internet in dumps like Zenodo, Dryad and the like is just not very useful. Advertizing your work is probably good for your career but this is not what makes your data and work useful to others. It's how easy you make it for others to understand, access and combine your data with their own data. This means providing data and metadata using open community standards (there are already a bunch of these in life sciences even if there are gaps in coverage).

A lot of researchers are bad at writing code, but they can audit it.

Can they really? It seems to me that users of this would be those that can't write the code they need. How would they be in a position to audit what they get?

Historically much of computational biology was driven by people with no wet lab experience and no access to a wet lab so hypotheses went untested because nobody wanted to bet their grant on someone else's work they didn't understand or trust. Now that maths and computation are everywhere in biology there are more hybrid dry/wet labs or dry and wet labs working in long term collaborations. So I think there's been some progress but maybe not as much as one could have hoped.

Of course there are more than that. Look at any comprehensive guide book on edible/foreageable plants and you'll definitely find more than a couple hundreds. For example, the plants for a future database (https://pfaf.org/user/Default.aspx) lists thousands of edible plants that grow in temperate climates. Many plants are edible if only for some parts and many are not particularly tasty and/or need some preparation.

Edit: To answer the question of the composition, the dominant taste comes from Tanacetum balsamita (https://en.wikipedia.org/wiki/Tanacetum_balsamita).

Once published, it's not new anymore. It would cost a few millions just to repeat the work on a few hundred proteins and unless something new or interesting surfaces, it won't be publishable in a high-visibility place. Also it might have to be done/repeated in different cell lines. I don't know much about what pharma/biotech is interested in but from what I see, getting basic quantitative data like this doesn't seem to be a prority though they would probably make use of it once available.

Edit to address the upside question: This is dynamic quantitative data with which you would eventually get at how much of a protein interacts with how much of another, where and when during cell division. Basically, this is getting at the dynamics of protein interactions in live cells. The goal would be to build an dynamic molecular interaction network of cell division.

For those interested, we did something similar a few years ago but for proteins in dividing cells where we could quantify how much of a protein is where in the cell over time during cell division [0]. Data [1], code [2] and web-based visualization [3] are available.

Although technology has improved, building the reagents and acquiring data remains labor-intensive. The value of such work would be in having an exhaustive resource (something like 500-600 relevant proteins for cell division) but once the proof of concept is published, you can't get funding for doing more.

[0] https://www.nature.com/articles/s41586-018-0518-z/

[1] https://idr.openmicroscopy.org/search/?query=Name:idr0041-ca...

[2] https://git.embl.de/grp-ellenberg/mitotic_cell_atlas

[3] https://www.mitocheck.org/mitotic_cell_atlas/index.html

Perl was the first language I used professionally and I used it almost exclusively for years. I don't understand all the negative comments about its use of sigils, I find them useful and I never got the impression that they made the code less readable but maybe that's because I was never taught the gospel of computer science :) I also appreciate the extended backwards compatibility. I still occasionally run decade-old code and it's still doing its job which means that writing code in perl was time well invested.

I've been working from home using a RPi 4B hooked up to a large monitor as a thin client for the last two years, first using Remmina and now mostly running VMWare Horizon client. The only thing I am not using it for is video conferencing.

Works fine for me. Must have been a temporary glitch.

Didn't see this post so posted it also. Also relevant: https://www.embl.org/news/science/alphafold-potential-impact...

See also https://www.embl.org/news/science/alphafold-potential-impact...

This brings back memories. My first Z80 assembly program was a jetpac game for the Sinclair ZX80 around 1984 because I couldn't get a ZX Spectrum to play it. I don't think any written copy still exists but there's still a copy on a cassette tape.

I've been working from home for almost a year using a Pi4B running remmina. The other end is a Dell workstation running x11vnc on Ubuntu 16.04. I've only experienced occasional hiccups that I've narrowed down to things interfering with the Pi wifi reception when it's in a heatsink case.

whether there are also potential users who would have been perfectly happy with javascript/python/R bindings and a good tutorial

If you think people in some communities would benefit then you should be proactive in advertising there and in particular providing bindings for their favourite languages. This would enhance discoverability. In my corner of the tech and science world, people mostly use python and/or R but few know about Rust and fewer have knowingly used it.

There are tools to generate realistic patient data, for example Synthea: https://synthetichealth.github.io/synthea/

Readers of scientific papers never had to pay. Before papers were available in digital form, you could request a paper copy from the authors. Of course the authors usually paid for some extra copies for this but when those ran out, you'd get a photocopy. Then people started requesting a copy and would get one by email. Now it's just faster to simply find that copy on the web than wait for someone to reply to your email.

In the linked study[1] the effect is still there after controlling for age and sex.

[1] https://doi.org/10.32388/WPP19W.3

This is prompted by the observation that in hospitals in Paris, the proportion of smokers among COVID-19 patients is ~5 times lower than the proportion of smokers in the population. The effect is big enough to merit some attention. The challenge will be to disentangle a protective effect from the increased vulnerability caused by smoking. The answer may be just a few nicotine patches away.