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Recent therapeutic trials of “classical” psychedelic drugs, such as psilocybin (from magic mushrooms) or LSD, have reported benefits to wellbeing, depression and anxiety. These effects seem to be linked to a sense of “ego dissolution” — a dissolving of the subjective boundaries between the self and the wider world. However, the neurochemistry behind this effect has been unclear. Now a new paper, published in Neuropsychopharmacology, suggests that changes in brain levels of the neurotransmitter glutamate are key to understanding reports of ego dissolution — and perhaps the therapeutic effects of psychedelics.

Natasha Mason at Maastricht University, the Netherlands, and colleagues recruited 60 participants for their study. All had taken a psychedelic drug before, but not in the three months prior to the study. Half received a placebo and the other half were given a low to moderate dose of psilocybin (0.17 mg/kg of body weight).

The team then used a technique called proton magnetic resonance spectroscopy (MRS) to look at concentrations of glutamate (as well as other neurochemicals) in the medial prefrontal cortex (mPFC) and the hippocampus — two regions that have been implicated as key to the psychedelic drug experience. The team also looked at patterns of “functional connectivity” within networks of brain regions, a measure of how closely correlated brain activity is across those regions. Six hours after taking the drug or placebo, the participants reported on their subjective experiences using two surveys: The 5 Dimensions of Altered States of Consciousness and the Ego Dissolution Inventory.

As the researchers expected (based on the findings of earlier research), those given the drug reported increased feelings of ego dissolution, as well as altered states of consciousness. They also showed disruptions in the connectivity of particular networks, including the default mode network, which has also been implicated in past work on the effects of psychedelic drugs…

But, for the first time in humans, the team also observed higher levels of glutamate in the mPFC and lower levels in the hippocampus after taking psilocybin — and they linked these changes to different aspects of ego dissolution. Increases in the mPFC were most strongly linked to unpleasant aspects, such as a loss of control over thoughts and decision-making, and also anxiety. Decreases in the hippocampus, meanwhile, were most strongly linked to more positive aspects, such as feelings of unity with the wider world, and of having undergone a spiritual-type experience.

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Many consumers may have heard of blockchain technology, especially in relation to cryptocurrency. However, they may not be aware of its full potential and impact across industries. Blockchain has the potential to simplify and add greater security to data management, and since its inception, this technology has quietly been changing business processes.

To get further insights, we asked the members of Forbes Technology Council to share some ways blockchain has changed (or will soon change) business. Their best answers are below.

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Last week, I wrote about the diversity problem in AI and why we need to fix it. I asked you to tell us about your experiences as a Black person in AI or share the names of Black colleagues you admire. Thank you to everyone who responded. It was heart-warming to hear from so many of you.

Many of you shared your frustration with the lack of mentors who understand your challenges, the alienation of being the only Black face at professional meetings, and the struggle to overcome economic and social inequalities. Black women, especially, wrote about the difficulties of building a career in AI. Some of you described your efforts to support Black people in science and technology and provide tech resources to underserved communities. Thank you for sharing with us your dreams and also your disappointments.

We will feature some of your stories in our Working AI blog series. Please stay tuned.

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A research team from ETH Zurich and the Max Planck Institute for Solar System Research in Göttingen counted over 136,000 rockfalls on the moon caused by asteroid impacts. Even billions of years old landscapes are still changing.

In October 2015, a spectacular rockfall occurred in the Swiss Alps: in the late morning hours, a large, snow-covered block with a volume of more than 1500 cubic meters suddenly detached from the summit of Mel de la Niva. It fell apart on its way downslope, but a number of continued their journey into the valley. One of the large boulders came to a halt at the foot of the summit next to a mountain hut, after traveling more than 1.4 kilometers and cutting through woods and meadows.

On the moon, time and again boulders and blocks of rock travel downslope, leaving behind impressive tracks, a phenomenon that has been observed since the first unmanned flights to the moon in the 1960s. During the Apollo missions, astronauts examined a few such tracks on site and returned displaced rock block samples to Earth. However, until a few years ago, it remained difficult to gain an overview of how widespread such rock movements are and where exactly they occur.

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Yay I can ride a trex or something: p.

Jurassic Park eat your heart out. The smallest dinosaur on record has been found stuck in amber. When we think about dinosaurs, the creatures we picture are usually quite large, such as the Apatosaurus or the T-Rex. We know others are smaller, such as Velociraptors, but even those were around 180 pounds. Some dinosaurs were a lot smaller than that, a fact which recently demonstrated when scientists recently reported finding the smallest dinosaur ever discovered, trapped in a chunk of amber, according to the BBC. The scientists published their findings in the journal Nature.

The fossil was found in northern Myanmar, in a piece of amber that is approximately 99 million years old. It is the skull of a dinosaur that resembled a bird, and its size suggests that the entire creature would only have been about as large as a bee hummingbird, the smallest bird currently living.

Continue reading “Smallest Dinosaur Ever Discovered Found Perfectly Trapped in Amber” | >

We have heard of the latest advancements in the field of deep learning due to the usage of different neural networks. Most of these achievements are simply astonishing and I find myself amazed after reading every new article on the advancements in this field almost every week. At the most basic level, all such neural networks are made up of artificial neurons that try to mimic the working of biological neurons. I had a curiosity about understanding how these artificial neurons compare to the structure of biological neurons in our brains and if possibly this could lead to a way to improve neural networks further. So if you are curious about this topic too, then let’s embark on a short 5-minute journey to understand this topic in detail…

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Researchers at the Nanoscience Center and at the Faculty of Information Technology at the University of Jyväskylä in Finland have demonstrated that new distance-based machine learning methods developed at the University of Jyväskylä are capable of predicting structures and atomic dynamics of nanoparticles reliably. The new methods are significantly faster than traditional simulation methods used for nanoparticle research and will facilitate more efficient explorations of particle-particle reactions and particles’ functionality in their environment. The study was published in a Special Issue devoted to machine learning in the Journal of Physical Chemistry on May 15, 2020.

The new methods were applied to ligand-stabilized metal , which have been long studied at the Nanoscience Center at the University of Jyväskylä. Last year, the researchers published a method that is able to successfully predict binding sites of the stabilizing ligand molecules on the nanoparticle surface. Now, a new tool was created that can reliably predict based on the atomic structure of the particle, without the need to use numerically heavy electronic structure computations. The tool facilitates Monte Carlo simulations of the atom dynamics of the particles at elevated temperatures.

Potential energy of a system is a fundamental quantity in computational nanoscience, since it allows for quantitative evaluations of system’s stability, rates of chemical reactions and strengths of interatomic bonds. Ligand-stabilized metal nanoparticles have many types of interatomic bonds of varying chemical strength, and traditionally the energy evaluations have been done by using the so-called density functional theory (DFT) that often results in numerically heavy computations requiring the use of supercomputers. This has precluded efficient simulations to understand nanoparticles’ functionalities, e.g., as catalysts, or interactions with biological objects such as proteins, viruses, or DNA. Machine learning methods, once trained to model the systems reliably, can speed up the simulations by several orders of magnitude.

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