Hacker News: jkh1

New comment by jkh1 in "30 Years of HPC: many hardware advances, little adoption of new languages"

jkh1 — Fri, 17 Apr 2026 09:43:27 +0000

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

Worker cooperative for open source tech related to scientific image data

jkh1 — Sat, 21 Mar 2026 09:32:52 +0000

Article URL: https://image.coop/blog/posts/2026/03/16/why-image-cooperative/

Comments URL: https://news.ycombinator.com/item?id=47465520

Points: 1

# Comments: 0

New comment by jkh1 in "Super-resolution microscopes reveal new details of cells and disease"

jkh1 — Mon, 21 Jul 2025 15:47:02 +0000

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

New comment by jkh1 in "Reproducibility project fails to validate dozens of biomedical studies"

jkh1 — Sat, 26 Apr 2025 06:16:14 +0000

cell/molecular biology

New comment by jkh1 in "Reproducibility project fails to validate dozens of biomedical studies"

jkh1 — Fri, 25 Apr 2025 18:17:32 +0000

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.

New comment by jkh1 in "Ask HN: How do you sync ongoing browser work between desktop and laptop?"

jkh1 — Wed, 11 Sep 2024 06:41:30 +0000

> 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/

New comment by jkh1 in "Ollama v0.1.33 with Llama 3, Phi 3, and Qwen 110B"

jkh1 — Mon, 29 Apr 2024 05:47:01 +0000

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

New comment by jkh1 in "Ollama v0.1.33 with Llama 3, Phi 3, and Qwen 110B"

jkh1 — Mon, 29 Apr 2024 05:42:48 +0000

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

Benchmarking Large Language Models for Bio-Image Analysis Code Generation

jkh1 — Fri, 26 Apr 2024 05:33:44 +0000

Article URL: https://www.biorxiv.org/content/10.1101/2024.04.19.590278v2

Comments URL: https://news.ycombinator.com/item?id=40166228

Points: 1

# Comments: 0

New comment by jkh1 in "Nextflow: Data-Driven Computational Pipelines"

jkh1 — Fri, 11 Aug 2023 06:12:45 +0000

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-...

New comment by jkh1 in "How to make your scientific data accessible, discoverable and useful"

jkh1 — Thu, 29 Jun 2023 07:22:17 +0000

The problem is that ISO standards are not open.

New comment by jkh1 in "How to make your scientific data accessible, discoverable and useful"

jkh1 — Thu, 29 Jun 2023 07:11:59 +0000

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.

New comment by jkh1 in "How to make your scientific data accessible, discoverable and useful"

jkh1 — Thu, 29 Jun 2023 07:05:53 +0000

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.

New comment by jkh1 in "How to make your scientific data accessible, discoverable and useful"

jkh1 — Thu, 29 Jun 2023 07:02:48 +0000

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.

New comment by jkh1 in "How to make your scientific data accessible, discoverable and useful"

jkh1 — Thu, 29 Jun 2023 06:57:54 +0000

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).

New comment by jkh1 in "ResearchGPT: Automated Data Analysis and Interpretation"

jkh1 — Tue, 25 Apr 2023 18:27:19 +0000

> 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?

New comment by jkh1 in "BioModels"

jkh1 — Sun, 05 Mar 2023 09:18:02 +0000

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.

New comment by jkh1 in "Where has all the Chartreuse gone?"

jkh1 — Tue, 28 Feb 2023 13:48:04 +0000

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).

New comment by jkh1 in "Database of 200k cell images yields new mathematical framework"

jkh1 — Fri, 06 Jan 2023 11:01:49 +0000

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.

New comment by jkh1 in "Database of 200k cell images yields new mathematical framework"

jkh1 — Fri, 06 Jan 2023 10:38:48 +0000

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