In the spirit of “if it took you a while to find out how to do something, write about it”, I will detail a method to approximate the surface area of a 3D shape. Our application here was finding the surface area of a cell but it can be used on any shape. We start with a 3D point set, specified by points of interest in a single cell imaged by 3D confocal microscopy.

In a previous post, I looked at how Google Scholar ranks co-authors. While I had the data available I wondered whether paper authorship could be used in other ways. A few months back, John Cook posted about using Jaccard index and jazz albums. The idea is to look at the players on two jazz albums and examine the overlap.

We have a new paper out describing how vesicles move inside cells. The paper in a nutshell In science-speak We analysed how small vesicles are transported in cells. In contrast to large vesicles and organelles, which move using motors inside cells, our analysis revealed that passive diffusion is the main mode of small vesicle transport. In normal language Inside cells, molecules are moved in tiny transport packets called vesicles.

On a scientist’s Google Scholar page, there is a list of co-authors in the sidebar. I’ve often wondered how Google determines in what order these co-authors appear. The list of co-authors on a primary author’s page is not exhaustive. It only lists co-authors who also have a Google Scholar profile. They also have to be suggested to the primary author and they need to accept the co-author to list them on the page.

Hopefully I will soon break out of this funk of posting about either Mastodon or Twitter. But not yet! This post is to say that: I made a static archive of tweets for @quantixed and for @clathrin. There, you can read all my posts, which ended in 2022. Edit: removed the archives in October 2024. How did I do it and why? I made the archive with this excellent tool written by Darius Kazemi.

Earlier this year I set up a bot on Mastodon. The bot, AlbumsX3, posts an album suggestion twice-a-day. Performance has been good. It has only missed a few posts due – I think – to server glitches. However, I have made a couple of tweaks to upgrade the bot since my last post, so I thought I would detail them here. Preventing duplicate posts In the last post I wrote: Well, it wasn’t long before I needed to revisit this issue.

I migrated my personal Mastodon account from mastodon.social to biologists.social recently. If you’d like to do the same, I found this guide very useful. Note that, once you move, all your previous posts are left behind on the old instance. Before I migrated, I downloaded all of my data from the old instance. I thought I’d take a look at what I had posted to see if anything was worth reposting on biologists.social.

This is a brief review of macOS Mastodon clients that I’ve tried. It is unashamedly incomplete/non-exhaustive, but since the ones I found online from computing magazines literally look at one app, I am ahead of the pack here! tl;dr I prefer Ivory on macOS and prior to that, Mastonaut was OK. For clarity: I have not been asked or received payment to promote any software. Why use an app on desktop/laptop?

We have an analysis routine for proteomics data written for IgorPro. One output is a volcano plot . These plots show the fold change in one sample compared to another and plot that against a p-value to estimate how reproducible any changes observed are. This post is not about that software, but on the topic of how we can recreate this plot in R . What steps need to be considered?

I’ve posted in the past about analysing race results in R (most recently here). I ran the 2023 MK Marathon and wanted to have a look at the finishing times. The days of race results being made available as a csv or xls for easy analysis seem to be behind us. Instead they tend to be served up on multiple webpages of 50 athletes’ results at a time. Oh no, 29 pages of results and now Download option…. let’s scrape the data!

I’m not a big movie person. Nonetheless I have a media library with quite a few films in and I wondered how many “films to see before you die”-type movies I had in the collection, and how many were missing. I used R to find the answers. I’ve described previously how to get a plain text dump of a Plex database using WebTools-NG. I did that for the Movies library of my Plex Media Server.