This is just a quick tip as it took me a little while to sort this out. In the lab we have two QNAP TS-869 Pro NAS devices. Each was set up with a single RAID6 storage pool and I ran them as a primary and replicant via rsync. We recently bought a bigger server and so the plan was to repurpose one of the NAS boxes to be a Time Machine for all the computers in the lab.

In the lab we have been doing quite a bit of analysis of cell migration in 2D. Typically RPE1 cells migrating on fibronectin-coated glass. There are quite a few tools out there to track cell movements and to analyse their migration. Naturally, none of these did quite what we wanted and none fitted nicely into our analysis workflow. This meant writing something from scratch in IgorPro. You can access the code from my GitHub pages.

I’ve previously written about analysing my iTunes library and about generating Smart Playlists in iTunes. This post takes things a bit further by generating a “perfect playlist” outside of iTunes… it is exclusively for nerds . How can you put together a perfect playlist? What are your favourite songs? How can you tell what they are?

I’m putting this up here in case it is useful for somebody. We capture Z-stacks on a Perkin Elmer Spinning Disk microscope system. I wanted to turn each stack into a single image so that we could quickly compare them. This simple macro does the job. We import the images straight from the *.mvd2 library using the wonderful BioFormats import tool. We open all files as composite hyperstacks.

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I was searching for the excitation and emission spectra for mNeonGreen. I was able to find an image, but no values for the spectra.

I put a recent code snippet put up on the IgorExchange. It’s a simple procedure for averaging a set of 1D waves and putting the results in a new wave. The difference between this code and Average Waves.ipf (which ships with Igor) is that this function takes the average of all points in the wave and places this single value in a new wave. You can specify whether the mean or median is used for the average.

This is a long post about Journal Impact Factors. Thanks to Stephen Curry for encouraging me to post this.

Following on from the last post about publication lag times at cell biology journals, I went ahead and crunched the numbers for all journals in PubMed for one year (2013). Before we dive into the numbers, a couple of points about this kind of information. Some journals “reset the clock” on the received date with manuscripts that are resubmitted. This makes comparisons difficult.

My interest in publication lag times continues. Previous posts have looked at how long it takes my lab to publish our work, how often trainees publish and I also looked at very long lag times at Oncogene. I recently read a blog post on automated calculation of publication lag times for Bioinformatics journals. I thought it would be great to do this for Cell Biology journals too.