For the new kids in back: If you hate statistics, you'll love my free lectures. Putting science before statistics, from basics of inference & causal modeling to multilevel models & dynamic state space models. It's all free, made with love and sympathy. https://t.co/GnOYGex9Yg
@Jeff_Mold @HarmitMalik Some people put their addresses in their bio, you can search for that by searching for people and different ways of writing bsky
@sailorrooscout Most GPs don‘t do covid at all because the BioNTech one most accessible in Germany still comes in 4/5-dose vials and that’s too much trouble for them as they have to find multiple people coming in within 2 days.
@sailorrooscout Yes, it’s available to doctors but only given to those that for some reason don’t have 3 shots or infections yet. There is no way for me to get the new one without cheating or finding a doctor willing to prescribe it despite lack of indication.
Excited to have published our work on #SEQURNA, a next-generation RNase inhibitor transforming #scRNAseq & other applications.
➡️Introducing synthetic thermostable RNase inhibitors to single-cell RNA-seq 🚀 🌕
https://t.co/qfCNvXm8Kk
🧵…
So... Preprint #2.
In which InstanSeg is extended to output nucleus & cell boundaries - and also to handle arbitrary combinations of image input channels.
https://t.co/Fk3Vljt5sd
Let me introduce: InstanSeg 🦠🔬💻👩🔬
This *would* have been a short thread about Thibaut Goldsborough’s PhD work… but he solved too many problems.
Now it's a long thread about 2 preprints, a whole new approach to cell segmentation & #opensource software to make it easy to use
I am often invited to review papers on deep learning for medical images. Unfortunately many papers do the same mistake; they split data into training/validation/test on the slice/image/patch level instead of on the patient level. This will lead to inflated test scores, as images from the same patient then can appear in both training and test sets. Since these images can be very similar, the network will perform extremely well on the test images.
If you use 2D networks on 3D data, the 2D slices have to be split on the patient level, not on slice level.
If you use 2D networks on a dataset where each patient has several 2D images, the images have to be split on the patient level, not on image level.
If you use deep learning for digital pathology, the patches have to be split on the patient level, not on patch level.
https://t.co/rir0yLXXFb
https://t.co/3RddGoeIox
https://t.co/pqx3j2uVN1
@KRHornberger Is this also the case for people younger than yourself (meaning less ‚relevant‘ positions worked)? working a kitchen is a high stress environment that the right person would surely be able to transfer skills from. And isn‘t showing that you worked from a young age also good?
We report many proteins not predicted by the genetic code.
They are stable & abundant O( 10³ ) copies / cell.
Generative mechanisms include codon-anticodon mismatches & RNA modifications.
Their abundance depends on codon frequency & protein stability.
https://t.co/lAxvulC4zx
@Jeff_Mold @theHumanBorch How many individuals per group? I keep seeing the same thing in DE with unbalanced groups regarding sex. My guess is that with a small n the intercept won't be able to catch the sex differences sufficiently.