@thatmanmatt That… works!
Recent Activities
Actually I think I prefer this version. Makes a good wallpaper.
@aegir
That's all, folks!
@silvermoon82 Right? It really looks like it!
Actually I think I prefer this version. Makes a good wallpaper.
Tunnel vision! Put away the portable aircon, and this is the outlet tube.
The rest of the tree is still green, but this branch caught the sun and glowed. #LeafScrolling
@joannaholman I used to watch a few of them, they had a weird fascination for me. Australia was the first one I saw, but NZ, UK, Canada and the US had them. I got the impression you don’t have many rights about refusing to be filmed while in customs.
@joannaholman Is the Australian one still full of people from China claiming their suitcases of food aren’t food?
Is shows filming people inside the secure areas of airports getting busted with lots of illegal stuff a global thing or just in Australia. Seems like something a lot of countries laws wouldn’t be cool with
@joannaholman I used to watch a few of them, they had a weird fascination for me. Australia was the first one I saw, but NZ, UK, Canada and the US had them. I got the impression you don’t have many rights about refusing to be filmed while in customs.
I wonder how they convince people to be on those border security shows. How does that convo go “you’ve just been busted with enough contraband for serious criminal consequences. Can we put you on TV?”
Is shows filming people inside the secure areas of airports getting busted with lots of illegal stuff a global thing or just in Australia. Seems like something a lot of countries laws wouldn’t be cool with
I wonder how they convince people to be on those border security shows. How does that convo go “you’ve just been busted with enough contraband for serious criminal consequences. Can we put you on TV?”
After a really awful end to last week, it’s official, I have some variety of cold virus. Been asleep for most of the day. Looking forward to going back to bed.
Feeling very sorry for myself.
@Janet_52square Hope you get well soon.
@MikeInTheGarden that's SE.
@druid @MikeInTheGarden Isn’t that the Sweden tld?
As someone who played plenty of GTA: San Andreas back when it was new - I'm sorry, but it's not very good. It tries to do way too many different things and all of them end up kinda naff.
Vice City is the perfect middle ground.
@Flamekebab I never got on with San Andreas, it’s the first one I never completed and didn’t really want to. I completely agree it was trying to do too much. GTA V has been a lot of fun though, and despite the company being an arse I’m looking forward to the next one.
After a really awful end to last week, it’s official, I have some variety of cold virus. Been asleep for most of the day. Looking forward to going back to bed.
Feeling very sorry for myself.
@druid Si = stupid eeejit! ☺️
@MikeInTheGarden that's SE.
No Hostinger, only one single deranged orange idiot is calling it that. Everyone else thinks the idea is shit.
@druid Si = stupid eeejit! ☺️
No Hostinger, only one single deranged orange idiot is calling it that. Everyone else thinks the idea is shit.
As someone who played plenty of GTA: San Andreas back when it was new - I'm sorry, but it's not very good. It tries to do way too many different things and all of them end up kinda naff.
Vice City is the perfect middle ground.
I am very happy with our publication "A human epithelial cell cycle dataset" which was published in Scientific Data.
nature.com/articles/s41597-0...
In this study, we introduce a large-scale, high-resolution dataset consisting of 100,000 single-cell images of human HEp-2 cells. By combining expert manual annotations with deep learning (CNN) techniques, we have captured and classified the different phases of the cell cycle (from interphase to telophase).
The identification of mitotic figures is one of the most critical tasks in autoimmune diagnostics, particularly regarding the assessment of Antinuclear Antibodies (ANA). Because manual interpretation is often subject to human bias and morphological variability, our dataset provides a good resource for developing robust and smart algorithms. We think is a useful contribution toward standardizing and improving the precision of computer-aided diagnosis (CAD) of autoimmune diseases.
While we celebrate this scientific milestone, it is also a moment of deep gratitude and remembrance. We recently lost Karsten Conrad, who passed away at the age of 70. Privatdozent Dr. med. Karsten Conrad was until his retirement head of the Laboratory for Autoimmune Diagnostics at the Institute of Immunology, Carl Gustav Carus Faculty of
Medicine, Technical University Dresden.
Karsten was a mentor and a constant companion on my scientific journey. I remember clearly the early stages of my career in 2007/2008, when he supported me by publishing some my (first?) scientific documents in his book series. His guidance during those formative years was important to me.
I perceived Karsten as an incredibly active and prolific author, contributing numerous significant publications that have shaped the field. Beyond his research, he was a highly respected by everybody I know. I was happy to have at my conference, the International Biotech Innovation Days (IBID), later in career.
He was the driving force behind Association for the Advancement of Immune Diagnostics (GFID) (est. 2002) the world-renowned Dresden Symposium on Autoantibodies, which has served as a vital international platform for scientific exchange for 35 years.
I can certainly say that Karsten’s legacy is immense. His extensive body of work, his prolific writing and his dedication to research will surely continue to guide and benefit generations of scientists, physicians, and, most importantly, the patients who rely on this expertise.
Dear Karsten, thank you for your passion, your profound expertise, and your unwavering support.
#Immunology #AutoimmuneDiseases #ArtificialIntelligence #DeepLearning #Diagnostics #Biotechnology #ScientificData #InMemoriam
OriginalPaper
Scientific Data
A human epithelial cell cycle dataset
Rico
Hiemann,
Dirk
Reinhold,
Karsten
Conrad,
Stefan
Rödiger,
Peter
Schierack,
Dirk
Roggenbuck
September 1, 2026
#Science, multidisciplinary
#Science, Humanities and Social Sciences, multidisciplinary
We introduce a large-scale single cell dataset for cell cycle phases. The dataset includes single cell images of 4′,6-diamidino-2-phenylindole-stained human epithelioma-2 (HEp-2) cells grown on microscopic glass slides. Greyscale images of adherent HEp-2 cells were acquired at 20x magnification by an automated, inverse microscope system. Cell images were automatically segmented with 256 × 256 pixel patch size including surrounding area. Initially, 10,000 images were manually classified by experts into 5 cell-cycle groups (interphase, prophase, metaphase, anaphase, telophase) and two additional groups (artefact, triple). Based on these initial single cell-image classifications, a convolutional neural network (CNN) model (VGG16) was trained. Subsequently, additional segmented single cell images were pre-classified by the CNN and double-blinded reviewed as well as corrected by two experts to build an algorithm-aided dataset with 100,000 images. The dataset provides a resource for development of biomedical and deep learning applications and, thus, enables training and assessment of cell-cycle detection algorithms.
