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Stretching the skin can alter how we perceive our fingers

When moving around in their surroundings, humans heavily rely on what is known as proprioception, sometimes referred to as the “sixth sense.” This is the body’s subconscious ability to sense its own position, movements and location in space, via many tiny receptors that respond to mechanical strain located in the muscles, tendons and skin.

Researchers at University of Pisa, the Italian Institute of Technology (IIT), and University of Rome Tor Vergata recently carried out a study investigating how artificially stretching the skin while people are moving their fingers influences proprioception. Their findings, published in the Journal of the Royal Society Interface, suggest that precisely deforming the skin while people are flexing their fingers alters how they perceive their hand and finger postures.

“According to common belief, humans have only five senses: vision, hearing, taste, smell, and touch; yet physiology tells us there are more,” Eleonora Fontana, first author of the paper, told Medical Xpress. “An essential, often overlooked ‘sixth sense’ is proprioception—our body’s ability to perceive limb position and movement in space. While much of this information is processed subconsciously, it is central to guiding our everyday movements; in fact, individuals with proprioceptive deficits suffer from severe movement impairments.”

Understanding and targeting erythroid cell metabolism

Red blood cell (RBC) production, or erythropoiesis, serves as a paradigm for studying cellular differentiation in both physiological and pathological contexts. While the transcriptional and epigenetic programs controlling erythropoiesis are well characterized, the metabolic regulation of this complex process remains underexplored. Recent discoveries that pyruvate kinase activators improve outcomes in sickle cell disease and thalassemia underscore the therapeutic potential of targeting metabolism in RBC disorders. However, further progress is limited by an incomplete understanding of the metabolic networks supporting erythropoiesis and RBC function.

Ken Hayworth: Brain Preservation is the Logical Lifeboat

Thirteen years ago, I sat down with Ken Hayworth and asked him a question most people spend their whole lives avoiding.

What happens to the self when the body fails?

Ken is president of the Brain Preservation Foundation. He is also a neuroscientist who refuses to flinch. His answer was not comfort. It was logic.

Brain preservation, he argued, is the logical lifeboat that people have access to today.

Here is the part that has stayed with me ever since. Ken imagines our grandchildren looking back at us. They will see that we had the science. They will see that we understood the brain holds our memories, our skills, our personality. And they will ask why we did nothing.

His verdict is brutal. We were not killed by bad technology. We were killed by bad philosophy. We simply could not accept that we are physical machines.

From Worm to AI: How Control Theory Unlocks Neural Networks

In this video, Dr. Ardavan (Ahmad) Borzou will discuss the control theory in network science and its application in C. elegans \& artificial neural networks. A short history of network science and the basics of control theory will also be reviewed.

Comprehensive Python Checklist (machine learning and more advanced libraries will be covered on a different page):
https://compu-flair.com/blogs/program… Website: www.compu-flair.com Chapters: 00:00 — Introduction 01:52 — Application of control theory in the neural net of worm 03:23 — Networks in Data Science & Seven Bridges of Konigsberg Problem 05:00 — History of network science 06:22 — Basics of control theory 10:23 — Results of applying control theory to the neural net of worm 11:27 — Control theory for artificial neural networks 12:44 — Comprehensive Python checklist for data scientists.

CompuFlair Website:
www.compu-flair.com.

Chapters:
00:00 — Introduction.
01:52 — Application of control theory in the neural net of worm.
03:23 — Networks in Data Science \& Seven Bridges of Konigsberg Problem.
05:00 — History of network science.
06:22 — Basics of control theory.
10:23 — Results of applying control theory to the neural net of worm.
11:27 — Control theory for artificial neural networks.
12:44 — Comprehensive Python checklist for data scientists.

Researchers propose ‘copyleft’ rules for generative AI

The rise of generative artificial intelligence (AI) poses challenges for the free and open-source software (FOSS) community, a global network committed to creating and maintaining publicly available software that anyone can use, modify and share. Many AI models have been built on open-source software but do not reciprocate the transparency that the FOSS community’s principles require, leaving open-source developers uncertain about how these AI tools are using their code.

A study by researchers at Yale’s Digital Ethics Center (DEC) explores a potential solution to this problem based on a concept used in free and open-source software known as “copyleft” licenses—a twist on typical copyright rules that obliges works derived from open-source materials to remain as free and transparent as the original work, rather than relicensing it under more restrictive terms. The study is published in the International Journal Of Law And Information Technology.

The authors propose what they call a Contextual Copyleft AI License (CCAI)—a novel extension of copyleft licensing that would treat generative AI models as derivative works and require AI developers training models on open-source code to make their architecture and training data freely available.

Particle-Simulated Foam In Custom C++ Coastal System

Leonard Saalfrank, also known as OMYOG, has showcased a custom C++ coastal renderer created as a one-week rendering challenge, exploring real-time shoreline rendering, shallow-water simulation, and GPU-driven visual effects.

The project builds on his earlier water-rendering work for Ferocious and expands it with shallow-water waves, GPU-driven breaking waves, and particle-based foam supporting up to 300K GPU particles.

Above is a render handling over 6 million triangles across all passes, using 8K textures at 2K resolution, running at around 250 FPS on an RTX 4,090 Laptop GPU with GPU profiling enabled. Without capture and profiling overhead, performance reportedly increases to around 300 FPS.

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