Welcome to me trying to convince myself to revise for my exams by inserting my lecture material into six of crows quotes
Kaz leaned back. “What’s the easiest way to determine a gene’s function?”
“Knock it out?” asked Inej.
“Modify it?” said Jesper.
“Molecular tools?” suggested Nina.
“You’re all horrible,” said Matthias.
Kaz rolled his eyes. “The easiest way to discover a gene’s function is infect it with a virus. You change its behaviour and you can compare it to what it used to do. The Rous virus is going to do that job for us,”
Bacterial toxins can compromise blood vessels' leakiness affecting blood pressure. This study reveals that blood vessel lining cells' membranes, under the control of proteins called caveolin-1 and cavin1, stiffen to regulate the leakiness via tunnels called transendothelial cell macroapertures
Read the published the research article here
Video from work by Camille Morel and colleagues
Institut Pasteur, Université Paris Cité, CNRS UMR6047, Inserm U1306, Unité des Toxines Bactériennes, Département de Microbiologie, Paris, France
Video originally published with a Creative Commons Attribution 4.0 International (CC BY 4.0)
Published in eLife, March 2024
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BioChatter, a new open-source framework developed by Heidelberg University researchers and collaborators, aims to harness the power of large language models (LLMs) for biomedical research while ensuring responsible use. By integrating various functionalities like knowledge retrieval and model chaining, BioChatter simplifies LLM interaction for researchers, making it accessible and inclusive. It also emphasizes user-friendliness and privacy, allowing for the deployment of local LLMs that protect sensitive data. This framework has the potential to revolutionize how we utilize LLMs for biomedical discovery and development.
Understanding complex biological and biomedical systems poses major challenges despite technological advances that allow us to collect vast amounts of data. Even domain experts cannot fully know the implications of every gene, molecule, symptom, or biomarker due to the inherent limitations of human knowledge. Biological events are also highly context-dependent based on factors like cell type or disease.
In contrast, large language models of the current generation, like GPT-3, can access enormous amounts of knowledge encoded in their parameters. If trained correctly, they can recall and combine virtually limitless knowledge from their training set. LLMs have taken the world by storm, and many biomedical researchers already use them in their daily work to assist with both general and research tasks.
Currently reading Andrew Gordon and Calvin Schwab's "The Quick and the Dead: Biomedical Theory in Ancient Egypt" and though I was familiarized with the fact that Djed Pillar and Ankh could have their iconographical origins in bulls' last three lumbar and sacrum vertebrae in dorsal perspective and thoracic vertebra in cephalic perspective respectively, I wasn't ready for the Was sceptre being (or, at least, having it's predecesor in) a dried bull penis, which definitely makes sense due the connotations of power and dominion of both the Was sceptre and wild bulls.
Like, fuck
I think, by the way, this also helps understand and highlight better not only certain iconographical aspects of Set (and from other Gods as well, but concerning different aspects, I'd say) in relation to bulls and their anatomy, but some theological ones as well.
Exercise inhibits bone cancer metastasis as it mechanically-stimulates osteocytes [bone-forming cells] to secrete extracellular vesicles containing tumour suppressing microRNAs
Read the published research article here
Image from work by Jing Xie and colleagues
Guangdong Provincial Key Laboratory of Bone and Joint Degeneration Diseases, Department of Cell Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
Image originally published with a Creative Commons Attribution 4.0 International (CC BY 4.0)
Published in eLife, February 2024
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The research team’s findings have far-reaching implications for the future of 2D materials. Driven by their success with MoS2, the researchers believe that their approach can be extended to other 2D materials and heterostructures, offering a powerful platform for designing and engineering next-generation 2D materials with tailored properties.
Large Language Models (LLMs) have emerged as dynamic tools with the ability to transform the landscape of biological research in a world driven by data and technology. These advanced AI models, including well-known names like BERT and GPT, are not confined to linguistics but have found their way into the complex realm of biology. In this article, let’s explore the potential of LLMs in addressing pressing biological challenges, such as drug discovery, disease diagnosis, genomics, and more.
What are Large Language Models?
ChatGPT, along with similar models, has gained widespread attention and adoption. Many people even integrate it into their daily routines. What makes ChatGPT and similar models remarkable? These LLMs represent a facet of artificial intelligence (AI) specifically formulated to understand, process, and even create “human-like” text. Developers construct them using transformers—a type of neural network—as their architecture; they pre-train these on copious amounts of textual data to assimilate the patterns, structures, and nuances inherent in human language.