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Cancer evolution is mathematical

Cancer is not a uniform disease. Rather, cancer is a disease of phenotypic plasticity, meaning tumor cells can change from one form or function to another. This includes reverting to less mature states and losing their normal function, which can result in treatment resistance, or changing their cell type altogether, which facilitates metastasis.

In addition to direct changes in your DNA in cancer, a key driver of cancer progression is where and when your DNA is activated. If your DNA contains the “words” that spell out individual genes, then epigenetics is the “grammar” of your genome, telling those genes whether they should be turned on or off in a given tissue. Even though all tissues in the body have almost exactly the same DNA sequence, they can all carry out different functions because of chemical and structural modifications that change which genes are activated and how. This “epigenome” can be influenced by environmental exposures such as diet, adding a dimension to how researchers understand drivers of health beyond the DNA code inherited from your parents.

I’m a cancer researcher, and my laboratory at Johns Hopkins University studies how the differences among normal tissues are controlled by an epigenetic code, and how this code is disrupted in cancer. In our recently published review, colleague Andre Levchenko at Yale University and I describe a new approach to understanding cancer plasticity by combining epigenetics with mathematics. Specifically, we propose how the concept of stochasticity can shed light on why cancers metastasize and become resistant to treatments.

Marvelling at the mystery of consciousness through a scientific lens

In the second episode of this 12-part podcast series, Tales of the Synapse, neuroscientist Anil Seth describes his research into consciousness, which he describes as “insurance against falling into a single, disciplinary hole.”

Alongside neuroscientists, Seth’s research group at the University of Sussex in Brighton, UK, also includes string theorists, mathematicians and psychologists. The team also collaborates with academics in the arts and humanities.

His 2021 book Being You: A New Science of Consciousness. begins by challenging the idea that consciousness is beyond the reach of science, and concludes with a look at consciousness in non-human animals, before asking if artificial intelligence will one day become both sentient and conscious.

Significance of mathematical modeling in understanding complex biological processes

Humans and animals detect different stimuli such as light, sound, and odor through nerve cells, which then transmit the information to the brain. Nerve cells must be able to adjust to the wide range of stimuli they receive, which can range from very weak to very strong. To do this, they may become more or less sensitive to stimuli (sensitization and habituation), or they may become more sensitive to weaker stimuli and less sensitive to stronger stimuli for better overall responsiveness (gain control). However, the exact way this happens is not yet understood.

To better understand the process of gain control, a research team led by Professor Kimura at Nagoya City University in Japan studied the roundworm C. elegans. They found that, when the worm first smells an unpleasant odor, its nerve cells exhibit a large, quickly increasing, and continuous response to both weak and strong stimuli. However, after exposure to the odor, the response is smaller and slower to weak stimuli but remains large to strong stimuli, similar to the response to the first exposure to the odor. Because the experience of odor exposure causes more efficient movement of worms away from the odor, the nerve cells have changed their response to better adapt to the stimulus using gain control.

Then the researchers used mathematical modeling to understand this process. Mathematical modeling is a powerful tool that can be used to better understand complex biological processes. They found that the “response to first smell” consists of fast and slow components, while the “response after exposure” only consists of the slow component, meaning that the odor experience inhibits the fast component to achieve gain control. They further found that both responses could be described by a simple differential equation and that the slow and fast components correspond to the leaky integration of a first and second derivative term of the odor concentration that the worm senses, respectively. The results of this study showed that the prior odor experience only appears to inhibit the mechanism required for the fast component.

Unusual atom helps in search for universe’s building blocks

An unusual form of cesium atom is helping a University of Queensland-led research team unmask unknown particles that make up the universe.

Dr. Jacinda Ginges, from UQ’s School of Mathematics and Physics, said the unusual atom—made up of an ordinary cesium atom and an called a muon—may prove essential in better understanding the universe’s fundamental building blocks.

“Our universe is still such a mystery to us,” Dr. Ginges said.

Mind-Blowing AI Breakthroughs in Science (Physics, Astrophysics and Math)!

Now, THIS is useful AI — controlling Nuclear Fusion reactions.


#AI #Deepmind #GTC23
In this Video I discuss Recent AI Breakthroughs in Science — in Physics, Astrophysics… and Math!

This video is sponsored by NVIDIA.

GIVEAWAY
Please follow these steps to win NVIDIA GeForce RTX 4,080 GPU (worth 1400$):
Step-1: Please register for NVIDIA GTC using this link: https://www.nvidia.com/gtc/?ncid=ref-inpa-194623
To qualify, registrants need to have a permanent home address in Europe, Middle East, or Africa.
Step-2: Wait for the GTC to start and join the Keynote livestream.
Step-3: Attend GTC sessions. Prizes will be awarded only to those who register for GTC using the link above and attend at least one session (keynote excluded). This giveaway is exclusive to my community — one winner will be selected from my subscribers. Good luck!

Some of GTC Sessions which I will attend:

Physicists create new model of ringing black holes

When two black holes collide into each other to form a new bigger black hole, they violently roil spacetime around them, sending ripples, called gravitational waves, outward in all directions. Previous studies of black hole collisions modeled the behavior of the gravitational waves using what is known as linear math, which means that the gravitational waves rippling outward did not influence, or interact, with each other. Now, a new analysis has modeled the same collisions in more detail and revealed so-called nonlinear effects.

“Nonlinear effects are what happens when waves on the beach crest and crash,” says Keefe Mitman, a Caltech graduate student who works with Saul Teukolsky (Ph. D. ‘74), the Robinson Professor of Theoretical Astrophysics at Caltech with a joint appointment at Cornell University.

“The waves interact and influence each other rather than ride along by themselves. With something as violent as a black hole merger, we expected these effects but had not seen them in our models until now. New methods for extracting the waveforms from our simulations have made it possible to see the nonlinearities.”

David Hilbert — The Foundations of Geometry

David Hilbert was a great leader and spokesperson for the discipline of mathematics in the early 20th Century. But he was an extremely important and respected mathematician in his own right.

Like so many great German mathematicians before him, Hilbert was another product of the University of Göttingen, at that time the mathematical centre of the world, and he spent most of his working life there. His formative years, though, were spent at the University of Königsberg, where he developed an intense and fruitful scientific exchange with fellow mathematicians Hermann Minkowski and Adolf Hurwitz.

Sociable, democratic and well-loved both as a student and as a teacher, and often seen as bucking the trend of the formal and elitist system of German mathematics, Hilbert’s mathematical genius nevertheless spoke for itself. He has many mathematical terms named after him, including Hilbert space (an infinite dimensional Euclidean space), Hilbert curves, the Hilbert classification and the Hilbert inequality, as well as several theorems, and he gradually established himself as the most famous mathematician of his time.

Andrew Strominger: Black Holes, Quantum Gravity, and Theoretical Physics | Lex Fridman Podcast #359

Andrew Strominger is a theoretical physicist at Harvard. Please support this podcast by checking out our sponsors:
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EPISODE LINKS:
Andrew’s website: https://www.physics.harvard.edu/people/facpages/strominger.
Andrew’s papers:
Soft Hair on Black Holes: https://arxiv.org/abs/1601.00921
Photon Rings Around Warped Black Holes: https://arxiv.org/abs/2211.

PODCAST INFO:
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OUTLINE:
0:00 — Introduction.
1:12 — Black holes.
6:16 — Albert Einstein.
25:44 — Quantum gravity.
29:56 — String theory.
40:44 — Holographic principle.
48:41 — De Sitter space.
53:53 — Speed of light.
1:00:40 — Black hole information paradox.
1:08:20 — Soft particles.
1:17:27 — Physics vs mathematics.
1:26:37 — Theory of everything.
1:41:58 — Time.
1:44:24 — Photon rings.
2:00:05 — Thought experiments.
2:08:26 — Aliens.
2:14:04 — Nuclear weapons.

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Neural Network Models of Mathematical Cognition | Silvester Sabathiel | Numerosity Workshop 2021

Session kindly contributed by Silvester Sabathiel in SEMF’s 2021 Numerous Numerosity Workshop: https://semf.org.es/numerosity/

ABSTRACT
With the rise and advances in the field of artificial intelligence, opportunities to understand the finer-grained mechanisms involved in mathematical cognition have increased. A vast scope of related research has been conducted on machine learning systems that learn solving differential equations, algebraic equations and integrals or proofing complex theorems, all for which the preprocessed symbolic representations form the input and output types. However on the search for cognitive mechanisms that match the scope of humans when it comes to generalizability and applicability of mathematical concepts in the external world, a more grounded approach might be required. This involves starting with fundamental mathematical concepts that are earliest acquired in the human development and learning these within an interactive and multimodal environment. In this talk we are going to examine how artificial neural network systems within such a framework provide a controlled setup to discover possible cognitive mechanisms for intuitive numerosity perception or culturally acquired numerical concepts, such as counting. First we review impactful research results from the past, before I present the contributions of the work myself was involved in. Finally we can discuss the upcoming challenges for the field of numerical cognition and where this research journey could evolve to.

SILVESTER SABATHIEL
NTNU Trondheim.
Personal website: https://silsab.com/
NTNU profile: https://www.ntnu.edu/employees/silvester.sabathiel.
ResearchGate: https://www.researchgate.net/profile/Silvester-Sabathiel-3
LinkedIn: https://www.linkedin.com/in/silvester-sabathiel-03368b117

SEMF NETWORKS
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The Most Realistic Humanoid Robots In The World: How Will Artificial Intelligence Affect Our Future?

https://youtube.com/watch?v=4LevUzfdBtw&feature=share

In this video, I’ll discuss some of the most advanced humanoid robots currently in development and reveal if the future really is bright for Robotics.

► All-New Echo Dot (5th Generation) | Smart Speaker with Clock and Alexa | Cloud Blue: https://amzn.to/3ISUX1u.
► Brilliant: Interactive Science And Math Learning: https://bit.ly/JasperAITechUniNet.

I explain the following ideas on this channel:
* Technology trends, both current and anticipated.
* Popular business technology.
* The Impact of Artificial Intelligence.
* Innovation In Space and New Scientific Discoveries.
* Entrepreneurial and Business Innovation.

Subscribe link.
https://www.youtube.com/channel/UCpaciBakZZlS3mbn9bHqTEw.

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