Tootfinder

Biden's record on covid
Saw a comment along the lines of: "seems unfair to criticise Biden's record on covid, while the situation now under Trump is so much worse".
But I don't think of them as separate. Biden's government _contributed_ to how covid stands under Trump.
If Biden's lot had taken the opportunity to educate people (at least the ones open to considering science findings) that
- it's airborne like smoke
- an empty room can hold infectious virus
- air filters, UV and fresh air reduce the levels of it
- masks work better the better they fit
- you can be infectious without/before symptoms
- you're fairly likely still to be infectious for 10 days, a few people longer
- current vaccines don't stop you catching it or transmitting it
- it can mess with your immune system so you're more likely to catch other things
- vaccinated people can still get Long Covid
- it's not "mild", it's just that the damage is quiet
then even if they hadn't done anything more to address the problem, people would be in a far better position to deploy their own common sense.
And unlike funding or laws, that investment in _knowledge_ is something it would be difficult for Trump's lot to roll back.
But Biden & co chose instead to play down the risks, and explicitly or implicitly mislead people (e.g. the 5-day quarantine, which contradicts the real infectious period).
So, yes it's worse now, but they _contributed_ to how it is now. They chose to encourage misapprehensions and confusion, and the effects of that choice are still playing out now.
Not letting them off the hook on the grounds of being comparatively "less bad", when they themselves laid some of the foundations of the current state of play.
#covid #Biden #USPol #CovidIsntOver

ChromFound: Towards A Universal Foundation Model for Single-Cell Chromatin Accessibility Data
Yifeng Jiao, Yuchen Liu, Yu Zhang, Xin Guo, Yushuai Wu, Chen Jiang, Jiyang Li, Hongwei Zhang, Limei Han, Xin Gao, Yuan Qi, Yuan Cheng
https://arxiv.org/abs/2505.12638

ChromFound: Towards A Universal Foundation Model for Single-Cell Chromatin Accessibility Data
The advent of single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) offers an innovative perspective for deciphering regulatory mechanisms by assembling a vast repository of single-cell chromatin accessibility data. While foundation models have achieved significant success in single-cell transcriptomics, there is currently no foundation model for scATAC-seq that supports zero-shot high-quality cell identification and comprehensive multi-omics analysis simultaneous…

BMFM-RNA: An Open Framework for Building and Evaluating Transcriptomic Foundation Models
Bharath Dandala, Michael M. Danziger, Ella Barkan, Tanwi Biswas, Viatcheslav Gurev, Jianying Hu, Matthew Madgwick, Akira Koseki, Tal Kozlovski, Michal Rosen-Zvi, Yishai Shimoni, Ching-Huei Tsou
https://arxiv.org/abs/2506.14861

BMFM-RNA: An Open Framework for Building and Evaluating Transcriptomic Foundation Models
Transcriptomic foundation models (TFMs) have recently emerged as powerful tools for analyzing gene expression in cells and tissues, supporting key tasks such as cell-type annotation, batch correction, and perturbation prediction. However, the diversity of model implementations and training strategies across recent TFMs, though promising, makes it challenging to isolate the contribution of individual design choices or evaluate their potential synergies. This hinders the field's ability to conver…

Aligning Proteins and Language: A Foundation Model for Protein Retrieval
Qifeng Wu, Zhengzhe Liu, Han Zhu, Yizhou Zhao, Daisuke Kihara, Min Xu
https://arxiv.org/abs/2506.08023

Aligning Proteins and Language: A Foundation Model for Protein Retrieval
This paper aims to retrieve proteins with similar structures and semantics from large-scale protein dataset, facilitating the functional interpretation of protein structures derived by structural determination methods like cryo-Electron Microscopy (cryo-EM). Motivated by the recent progress of vision-language models (VLMs), we propose a CLIP-style framework for aligning 3D protein structures with functional annotations using contrastive learning. For model training, we propose a large-scale dat…

When you want to evaluate a new framework do you have sort of a “petclinic” project that you are using as a foundation? If yes, could you share what the project does?