Calorie restriction, a proven intervention to slow aging in animals, showed evidence of slowing the pace of biological aging in a human randomized trial.
In a first-of-its-kind randomized controlled trial, an international team of researchers led by the Columbia Aging Center at Columbia University.
Columbia University is a private Ivy League research university in New York City that was established in 1754. This makes it the oldest institution of higher education in New York and the fifth-oldest in the United States. It is often just referred to as Columbia, but its official name is Columbia University in the City of New York.
In this episode, my guest is Oded Rechavi, Ph.D., professor of neurobiology at Tel Aviv University and expert in how genes are inherited, how experiences shape genes and remarkably, how some memories of experiences can be passed via genes to offspring. We discuss his research challenging long-held tenets of genetic inheritance and the relevance of those findings to understanding key biological and psychological processes including metabolism, stress and trauma. He describes the history of the scientific exploration of the “heritability of acquired traits” and how epigenetics and RNA biology can account for some of the passage of certain experience-based memories. He discusses the importance of model organisms in scientific research and describes his work on how stressors and memories can be passed through small RNA molecules to multiple generations of offspring in ways that meaningfully affect their behavior. Nature vs. nurture is a commonly debated theme; Dr. Rechavi’s work represents a fundamental shift in our understanding of that debate, as well as genetic inheritance, brain function and evolution.
The Singularity is a technological event horizon beyond which we cannot see – a moment in future history when exponential progress makes the impossible possible. This video discusses the concept of the Singularity, related technologies including AI, synthetic biology, cybernetics and quantum computing, and their potential implications.
What did scientists think about aging in 1931? That’s right. 1931. because that is the year the first biological textbook was published “The Science of Life”. I managed to get my hands on the first edition of this textbook. This was my face when i first received it. As you can see i was quite excited. And this textbook is made up of separate books. I bought book i last year and i read it. Having enjoyed it and discovered that it was part of this massive ensemble piece — well, i’ve read the first “book” — there are, if my roman numerals are correct, 9 books in total. And in this first book, penned “The Living Body”, the authors, most famously, H.G.Wells, Sir Julian Huxley and G.P.Wells, H.G’s son discusses the body as a machine and that.
“For the present it is enough to remember that all animals (including men) are combustion engines of an intricate and curious kind, which live by oxidising their food”
I bought first The Living Body and then discovered it was part of this massive ensemble piece and decided i needed to read it. Now, besides being surprised to find out that H.G.Wells wrote not just non-fiction, but biology non-fiction, i was also surprised to hear how both similar & dissimilar their views were back in 1931 compared to today and i wasnt sure if that was good or terrifying.
So, how did they think of human aging. Well, in the last chapter of this 1st book titled “The wearing out of the machine and its reproduction”, they discuss it.
Many current computational models that aim to simulate cortical and hippocampal modules of the brain depend on artificial neural networks. However, such classical or even deep neural networks are very slow, sometimes taking thousands of trials to obtain the final response with a considerable amount of error. The need for a large number of trials at learning and the inaccurate output responses are due to the complexity of the input cue and the biological processes being simulated. This article proposes a computational model for an intact and a lesioned cortico-hippocampal system using quantum-inspired neural networks. This cortico-hippocampal computational quantum-inspired (CHCQI) model simulates cortical and hippocampal modules by using adaptively updated neural networks entangled with quantum circuits. The proposed model is used to simulate various classical conditioning tasks related to biological processes. The output of the simulated tasks yielded the desired responses quickly and efficiently compared with other computational models, including the recently published Green model.
Several researchers have proposed models that combine artificial neural networks (ANNs) or quantum neural networks (QNNs) with various other ingredients. For example, Haykin (1999) and Bishop (1995) developed multilevel activation function QNNs using the quantum linear superposition feature (Bonnell and Papini, 1997).
The prime factorization algorithm of Shor was used to illustrate the basic workings of QNNs (Shor, 1994). Shor’s algorithm uses quantum computations by quantum gates to provide the potential power for quantum computers (Bocharov et al., 2017; Dridi and Alghassi, 2017; Demirci et al., 2018; Jiang et al., 2018). Meanwhile, the work of Kak (1995) focused on the relationship between quantum mechanics principles and ANNs. Kak introduced the first quantum network based on the principles of neural networks, combining quantum computation with convolutional neural networks to produce quantum neural computation (Kak, 1995; Zhou, 2010). Since then, a myriad of QNN models have been proposed, such as those of Zhou (2010) and Schuld et al. (2014).
The robot tasked with making bricks out of lunar soil will be launched during China’s Chang’e-8 mission around 2028.
With Artemis II set to launch on November 24, it is no surprise that science journals are buzzing with research on lunar regolith, building bases on the moon, and working with moon soil to grow plants… you get the drift.
A recent study in the journal Communications Biology described an experiment in which the moon soil samples collected during the Apollo missions were used to grow plants. And for the first time, an Earth plant, Arabidopsis thaliana, commonly called thale cress, grew and thrived in the lunar soil samples during the experiment.
Mice that received gut microbes from children with epilepsy on the ketogenic diet were protected from seizures. The finding suggests the microbiome is behind the diet’s seizure-reducing effect.
In the last decade, we have witnessed biology bring us some incredible products and technologies: from mushroom-based packaging to animal-free hotdogs and mRNA vaccines that helped curb a global pandemic. The power of synthetic biology to transform our world cannot be overstated: this industry is projected to contribute to as much as a third of the global economic output by 2030, or nearly $30 trillion, and could impact almost every area of our lives, from the food we eat to the medicine we put in our bodies.
The leaders of this unstoppable bio revolution – many of whom you can meet at the SynBioBeta conference in Oakland, CA, on May 23–25 – are bringing the future closer every day through their ambitious vision, long-range strategy, and proactive oversight. These ten powerful women are shaping our world as company leaders, biosecurity experts, policymakers, and philanthropists focused on charting a new course to a more sustainable, equitable, clean, and safe future.
As an early pioneer in the high-throughput synthesis and sequencing of DNA, Emily Leproust has dedicated her life to democratizing gene synthesis to catapult the growth of synthetic biology applications from medicine, food, agriculture, and industrial chemicals to DNA data storage. She was one of the co-founders of Twist Bioscience in 2013 and is still leading the expanding company as CEO. To say that Twist’s silicon platform was a game-changer for the industry is an understatement. And it is no surprise that Leproust was recently honored with the BIO Rosalind Franklin Award for her work in the biobased economy and biotech innovation.
Humongous Fungus, a specimen of Armillaria ostoyae, has claimed the title of world’s largest single organism. Though it features honey mushrooms above ground, the bulk of this creature’s mass arises from its vast subterranean mycelial network of filamentous tendrils. It has spread across more than 2,000 acres of soil and weighs over 30,000 metric tons. Yet I would contend that Humongous Fungus represents a mere microcosm of the world’s true largest organism, a creature that I will call Cyborg Earth. What is Cyborg Earth? Eastern religions have suggested that all life is fundamentally interconnected. Cyborg Earth represents an extension of this concept.
All across the globe, biological life thrives. Quintillions upon quintillions of biomolecular computations happen every second, powering all life. Mycoplasma bacteria. Communities of leafcutter ants. The Humongous Fungus. Beloved beagles. Seasonal influenza viruses. Parasitic roundworms. Families of Canadian elk. Vast blooms of cyanobacteria. Humanity. Life works because of complexity that arises from simplicity that in turn arises from whatever inscrutable quantum mechanical rules lay beneath the molecular scale.
All creatures rearrange atoms in various ways. Termites and beavers rearrange larger bunches of atoms than most organisms. As humans progressed from paleolithic to metalwork to industrialization and then to the space age, information revolution, and era of artificial intelligence, they learned to converse with the atoms around them in an ever more complex fashion. We are actors in an operatic performance, we are subroutines of evolution, we are interwoven matryoshka patterns, an epic chemistry.
Shortly after Max Planck shook the scientific world with ideas about the fundamental quantization of energy, researchers built and leveraged theories of quantum mechanics to resolve physical phenomena that had previously been unexplainable, including the behavior of heat in solids and light absorption on an atomic level. In the 120-plus years since, researchers have looked beyond physics and used quantum theory’s same perplexing — even “spooky,” according to Einstein — laws to solve inexplicable phenomena in a variety of other disciplines.
Today, researchers at the Johns Hopkins Applied Physics Laboratory (APL) in Laurel, Maryland, are applying quantum mechanics to biology to better understand of one of nature’s biggest mysteries — magnetosensitivity, an organism’s ability to sense Earth’s magnetic field and use it as a tool to adjust some biological processes. And they’ve found some surprising results.
In a recent study, APL research engineer and scientist Carlos Martino and his APL colleagues Nam Le, Michael Salerno, Janna Domenico, Christopher Stiles, Megan Hannegan, and Ryan McQuillen, along with Ilia Solov’yov from the Carl von Ossietzky University of Oldenburg in Germany, found that an enzyme that plays a central role in human metabolism has some of the same key features as a magnetically sensitive protein found in birds.
