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Facing the Intelligence Explosion: There is Plenty of Room Above

Facing the Intelligence Explosion: There is Plenty of Room Above | Amazing Science | Scoop.it

Why are AIs in movies so often of roughly human-level intelligenceOne reason is that we almost always fail to see non-humans as non-human. We anthropomorphize. That’s why aliens and robots in fiction are basically just humans with big eyes or green skin or some special power. Another reason is that it’s hard for a writer to write characters that are smarter than the writer. How exactly would a superintelligent machine solve problem X?


The human capacity for efficient cross-domain optimization is not a natural plateau for intelligence. It’s a narrow, accidental, temporary marker created by evolution due to things like the slow rate of neuronal firing and how large a skull can fit through a primate’s birth canal. Einstein may seem vastly more intelligent than a village idiot, but this difference is dwarfed by the difference between the village idiot and a mouse.


As Vernor Vinge put it: The best answer to the question, “Will computers ever be as smart as humans?” is probably “Yes, but only briefly.”[1 How could an AI surpass human abilities? Let us count the ways:


  • Speed. Our axons carry signals at seventy-five meters per second or slower. A machine can pass signals along about four million times more quickly.
  • Serial depth. The human brain can’t rapidly perform any computation that requires more than one hundred sequential steps; thus, it relies on massively parallel computation.[2More is possible when both parallel and deep serial computations can be performed.
  • Computational resources. The brain’s size and neuron count are constrained by skull size, metabolism, and other factors. AIs could be built on the scale of buildings or cities or larger. When we can make circuits no smaller, we can just add more of them.
  • Rationality. As we explored earlier, human brains do nothing like optimal belief formation or goal achievement. Machines can be built from the ground up using (computable approximations of) optimal Bayesian decision networks, and indeed this is already a leading paradigm in artificial agent design.
  • Introspective access/editability. We humans have almost no introspective access to our cognitive algorithms, and cannot easily edit and improve them. Machines can already do this (read about EURISKO and metaheuristics). A limited hack like the method of loci greatly improves human memory; machines can do this kind of thing in spades.


REFERENCES:

1Vernor Vinge, “Signs of the Singularity,” IEEE Spectrum, June 2008, http://spectrum.ieee.org/biomedical/ethics/signs-of-the-singularity.

2J. A. Feldman and Dana H. Ballard, “Connectionist Models and Their Properties,” Cognitive Science 6 (3 1982): 205–254, doi: 10.1207/s15516709cog0603_1.

Steffi Tan's curator insight, March 24, 2015 5:43 AM

Vernor Vinge answered the question, "Will computers ever be as smart as humans?" with the simple sentence of "Yes, but only briefly."

 

For only a short period of time as technology ever develops, will technology be on the same intellectual playing field before it is able to surpass and exponentially grow in its capabilities. Emphasis again on how controlled setting need to be taken if an intelligence explosion were to occur. However, even if everyone agrees on the priority of safety, it only requires a single group of people to blindly walk into such circumstance for the event to cause issues for everyone.

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Unlimited, at-home coronavirus testing for your organization

Unlimited, at-home coronavirus testing for your organization | Amazing Science | Scoop.it

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Segregation Distorter (SD): Selfish ‘Supergene’ wreaks havoc in a genome

Segregation Distorter (SD): Selfish ‘Supergene’ wreaks havoc in a genome | Amazing Science | Scoop.it

The human genome is littered with "selfish genetic elements," which do not seem to benefit their hosts, but instead seek only to propagate themselves. Selfish genetic elements can wreak havoc by, for instance, distorting sex ratios, impairing fertility, causing harmful mutations, and even potentially causing population extinction.

 

Biologists at the University of Rochester, including Amanda Larracuente, an associate professor of biology, and Daven Presgraves, a University Dean's Professor of Biology, have for the first time used population genomics to shed light on the evolution and consequences of a selfish genetic element known as Segregation Distorter (SD). In a paper published in the journal eLife, the researchers report that SD has caused dramatic changes in chromosome organization and genetic diversity.

 

A genome-sequencing first

The researchers used fruit flies as model organisms to study SD, a selfish genetic element that skews the rules of fair genetic transmission. Fruit flies share about 70 percent of the same genes that cause human diseases, and because they have such short reproductive cycles -- less than two weeks -- scientists are able to create generations of the flies in a relatively short amount of time.

Female flies transmit SD-infected chromosomes to about 50 percent of their offspring, as expected under Mendel's laws of inheritance. Males, however, transmit SD chromosomes to nearly 100 percent of their offspring, because SD kills any sperm that do not carry the selfish genetic element.

 

How does SD do this?

Because it has evolved into what researchers refer to as a "supergene" -- a cluster of selfish genes on the same chromosome that are inherited together. Researchers have known for decades that SD evolved to form a supergene. But this is the first time they have used what is known as population genomics -- examining genome-wide patterns of DNA sequence variations among individuals in a population -- to study the dynamics, evolution, and long-term effects of SD on a genome's evolution. "This is the first time anyone has sequenced the whole genomes of SD chromosomes and therefore been able to make inferences about both the history and the genomic consequences of being a supergene," Presgraves says.

 

An evolutionary downfall on the horizon

The advantage of being a supergene is that multiple genes can act together to cause SD's near-perfect transmission to offspring. As the researchers found, however, there are major drawbacks to being a supergene. In sexual reproduction, chromosomes from the mother and the father swap genetic material to produce new genetic combinations unique to each offspring. In most cases, the chromosomes line up properly and crossover. Scientists have long recognized that the exchange of genetic material by crossing over -- known as recombination -- is vital because it empowers natural selection to eliminate deleterious mutations and enable the spread of beneficial mutations.

 

As the researchers showed, however, one of the major costs of SD's near-perfect transmission is that it does not undergo recombination. The selfish genetic element gains a short-term transmission advantage by shutting down recombination to ensure it gets passed on to all of its offspring. But SD is not forward-looking: preventing recombination has led to SD accumulating many more deleterious mutations compared to normal chromosomes. "Without recombination, natural selection can't purge deleterious mutations effectively, so they can accumulate on SD chromosomes," Larracuente says. "These mutations might be ones that disrupt the function or regulation of genes."

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Gecko feet are coated in an ultra-thin layer of lipids that help them stay sticky

Gecko feet are coated in an ultra-thin layer of lipids that help them stay sticky | Amazing Science | Scoop.it

Geckos are famous for having grippy feet that allow them to scale vertical surfaces with ease. They get this seeming superpower from millions of microscopic, hairlike structures on their toes.

Now, scientists have zoomed in for an even closer look at those structures, called setae, and found that they are coated in an ultra-thin film of water-repelling lipid molecules only one nanometer, or billionths of a meter, thick.

 

Researchers from the National Institute of Standards and Technology (NIST) analyzed the surface of the setae using high-energy X-rays thrown off by a type of particle accelerator called a synchrotron. The synchrotron microscope showed that the lipid molecules line the surface of the setae in dense, orderly arrays.

Lipids can play a role in this process because they are hydrophobic, meaning they repel water.

 

"The lipids might function to push away any water beneath the spatulae, allowing them to make closer contact with the surface," said physicist and co-author Tobias Weidner of Aarhus University in Denmark. "This would help geckos maintain their grip on wet surfaces." The setae and spatulae are made of a type of keratin protein similar to that found in human hair and fingernails. They are extremely delicate. The researchers showed that the keratin fibers are aligned in the direction of the setae, which might help them resist abrasion.

 

"The most exciting thing for me about this biological system is that everything is perfectly optimized on every scale, from the macro to the micro to the molecular," said biologist and co-author Stanislav Gorb of Kiel University in Germany. "This can help biomimetic engineers know what to do next. You can imagine gecko boots that don't slip on wet surfaces, or gecko gloves for holding tools that are wet," said NIST physicist and co-author Dan Fischer. "Or a vehicle that can run up walls, or a robot that can run along power lines and inspect them."

 

The NIST synchrotron microscope that the researchers used to analyze the setae is unique in its ability to identify molecules on the surface of a three-dimensional object, measure their orientation and map their position. It is located at the U.S. Department of Energy's Brookhaven National Laboratory, where the National Synchrotron Light Source II, a half-mile-long particle accelerator, provides a source of high-energy X-rays for illumination. This microscope is typically used to understand the physics of advanced industrial materials, including batteries, semiconductors, solar panels and medical devices.

 

"But it is fascinating to figure out how gecko feet work," Fischer said, "and we can learn a lot from nature when it comes to improving our own technology."

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Scientists create first ever VX neurotoxin detector

Scientists create first ever VX neurotoxin detector | Amazing Science | Scoop.it

VX is classified as a neurotoxin and an incredibly deadly chemical warfare agent that has been used in assassinations by some nations. It can cause permanent brain damage in those who survive exposure. These potentially life-saving findings are published in the July 2022 edition of Science Advances, with lab member Jim McCann serving as the paper's primary author. It outlines the design of two proteins that detect the neurotoxin by changing their shape in the presence of VX.

 

In collaboration with Douglas Pike and Vikas Nanda at Rutgers University, the CCNY team used a protein design program called ProtCAD to design 20 different proteins. According to Koder, the computer code was new and unlike anything the team had previously worked with, so it came as a bit of a surprise that two of their protein designs worked rather quickly.

 

"Having the first thing we tried with a small molecule actually just work was pretty great," Koder said. "In that absence of VX, all of the negative charges repel each other and then the protein unfolds. And it really extends, almost like a stick. When the protein binds VX it wraps all the way around the molecule becoming much more compact."

 

Previous detectors for this type of molecule often produced false positives from chemicals like insecticides. This new design can help prevent those misleading results, by scanning the entire molecular surface down to one hundred-millionth of a centimeter.

"We get this remarkable specificity because we're making contact with the whole molecule," said Koder. This work adds to a rapidly advancing field of biosensing technology used to detect the presence of incredibly small molecules called biomarkers.

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Researchers achieve fusion energy record

Researchers have exceeded the current record for generating energy from a nuclear fusion reaction. It’s a big step toward solving the world power consumption crisis. Nuclear fusion is called the holy grail of energy production — because it could lead to a virtually unlimited source of safe and sustainable power.

 

The nuclear test happened at the world’s most powerful fusion plant — the JET Fusion facility in the UK. The record-breaking nuclear fusion reaction had a temperature of more than 150 million degrees Celsius, 10 times hotter than the heart of the sun. The research team explained that the breakthrough is a landmark for this technology, and a key step toward developing practical nuclear fusion.

 

The difference between fusion and fission

Most nuclear reactors use fission — that’s when big unstable atoms like uranium are split in two, releasing lots of energy and radiation. Fission is the current technique used around the globe in nuclear power plants. But fusion is different. It involves forcing atoms of hydrogen together — fusing them to create: helium, lots of energy, and just a tiny bit of short-lived radiation.

 

Using fusion to create mini-suns inside reactors like this, is one of the greatest tech challenges humanity has ever faced. It holds the potential for producing almost unlimited supplies of energy, forever. The essential part of the test fusion reaction only lasted for 5 seconds — and only generated enough power to boil about 60 kettles of water — 11 mega-watts of power. But it’s an important proof of the science of fusion. The power output was more than double what was achieved in similar tests in year 1997.

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The end of art or a new beginning? DALL·E 2 Will Disrupt Art Deeper Than Photography Did

The end of art or a new beginning? DALL·E 2 Will Disrupt Art Deeper Than Photography Did | Amazing Science | Scoop.it

Are we going to soon witness the creative genius of a virtual Picasso or Leonardo da Vinci? We could be on the way with the latest trend in the AI field: visual generative models. While systems like GPT-3 create text from text, others like DALL·E 2 — a wordplay between Spanish painter Salvador Dalí and Pixar’s cute robot WALL·E — can create visual art from just words and sentences. As you can see from the cover image of this article, this tech gives a new meaning to the idiom “an image is worth more than a thousand words.”

 

OpenAI, the company behind DALL·E 2, announced the model in April. In just a couple of months, it has sounded all the alarms that AI is getting too close to human-level visual creativity. It can create anything you can imagine — and anything you can’t — from a single sentence. 

 

And OpenAI isn’t alone. Just a month later, Google Brain announced Imagen, a similar — and even slightly better — model. And now, another team from Google Brain released yet another model called Parti. And that’s without mentioning all the projects coming from low-budget research groups that don’t count on the resources of the Googles and OpenAIs of the world. Some examples are brand new research labs like Midjourney, free apps like Dream, open-source alternatives like Hugging Face’s DALL·E mini (now called Craiyon), or the collab notebooks (e.g. disco diffusion) that originate from the work of a few independent researchers like Katherine Crowson and Ryan Murdock, who started the movement among digital artists.

 

Now that the tech is proven and the uncertainty removed, the whole AI community is in a race to build the next best text-to-image model. And they aren’t stopping anytime soon. This will have important consequences. Today, I’ll focus on how this technology will disrupt the visual arts. DALL·E 2, the main star of today’s issue, will accompany us on this journey.

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AI and cryo-electron tomography: Towards Visual Proteomics at High Resolution

AI and cryo-electron tomography: Towards Visual Proteomics at High Resolution | Amazing Science | Scoop.it

Traditionally, structural biologists approach the complexity of cellular proteomes in a reductionist manner. Proteomes are fractionated, their molecular components purified and studied one-by-one using the experimental methods for structure determination at their disposal. Visual proteomics aims at obtaining a holistic picture of cellular proteomes by studying them in situ, ideally in unperturbed cellular environments. The method that enables doing this at highest resolution is cryo-electron tomography. It allows to visualize cellular landscapes with molecular resolution generating maps or atlases revealing the interaction networks which underlie cellular functions in health and in disease states. Current implementations of cryo-ET do not yet realize the full potential of the method in terms of resolution and interpretability. To this end, further improvements in technology and methodology are needed. This review describes the state of the art as well as measures which we expect will help overcoming current limitations.

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NASA Reconnaissance Orbiter Spots Rocket Impact Site of Unknown Origin on Moon

NASA Reconnaissance Orbiter Spots Rocket Impact Site of Unknown Origin on Moon | Amazing Science | Scoop.it

Astronomers discovered a rocket body heading toward a lunar collision late last year. Impact occurred March 4, with NASA's Lunar Reconnaissance Orbiter later spotting the resulting crater. Surprisingly the crater is actually two craters, an eastern crater (18-meter diameter, about 19.5 yards) superimposed on a western crater (16-meter diameter, about 17.5 yards). A rocket body impacted the Moon on March 4, 2022, near Hertzsprung crater, creating a double crater roughly 28 meters wide in the longest dimension.

 

The double crater was completely unexpected and may indicate that the rocket body had large masses at each end. Typically a spent rocket has mass concentrated at the motor end; the rest of the rocket stage mainly consists of an empty fuel tank. Since the origin of the rocket body remains uncertain, the double nature of the crater may indicate its identity. The crater formed (5.226 degrees north, 234.486 degrees east, 1,863 meters elevation) in a complex area where the impact of ejecta from the Orientale basin event overlies the degraded northeast rim of Hertzsprung basin (536 kilometers diameter). The new crater is not visible in this view, but its location is indicated by the white arrow. LROC WAC mosaic, 110 kilometers width.

 

No other rocket body so far that crashed on the Moon has created double craters. The four Apollo SIV-B craters were somewhat irregular in outline (Apollos 13, 14, 15, 17) and were substantially larger (greater than 35 meters, about 38 yards) than each of the double craters. The maximum width (29 meters, about 31.7 yards) of the double crater of the mystery rocket body was near that of the S-IVBs.

LRO is managed by NASA's Goddard Space Flight Center in Greenbelt, Maryland, for the Science Mission Directorate at NASA Headquarters in Washington. Launched on June 18, 2009, LRO has collected a treasure trove of data with its seven powerful instruments, making an invaluable contribution to our knowledge about the Moon. NASA is returning to the Moon with commercial and international partners to expand human presence in space and bring back new knowledge and opportunities.

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Endothelial cell heterogeneity based on pig cell landscape at single-cell level

Endothelial cell heterogeneity based on pig cell landscape at single-cell level | Amazing Science | Scoop.it

Pigs are valuable large animal models for biomedical and genetic research, but insights into the tissue- and cell-type-specific transcriptome and heterogeneity remain limited. By leveraging single-cell RNA sequencing, scientists now generated a multiple-organ single-cell transcriptomic map containing over 200,000 pig cells from 20 tissues/organs. They were able to comprehensively characterize the heterogeneity of cells in tissues and to identify 234 cell clusters, representing 58 major cell types. In-depth integrative analysis of endothelial cells reveals a high degree of heterogeneity. They also identified several functionally distinct endothelial cell phenotypes, including an endothelial to mesenchymal transition subtype in adipose tissues. Intercellular communication analysis predicts tissue- and cell type-specific crosstalk between endothelial cells and other cell types through the VEGF, PDGF, TGF-β, and BMP pathways. Regulon analysis of single-cell transcriptome of microglia in pig and 12 other species further identifies MEF2C as an evolutionally conserved regulon in the microglia.

 

This important work describes the landscape of single-cell transcriptomes within diverse pig organs and identifies the heterogeneity of endothelial cells and evolutionally conserved regulon in microglia.

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Intelligent Toilet Offers Remote Patient Monitoring for Gastrointestinal Health

Researchers at Duke University are developing an artificial intelligence tool for toilets that would help providers improve care management for patients with gastrointestinal issues through remote patient monitoring.  The tool, which can be installed in the pipes of a toilet and analyzes stool samples, has the potential to improve treatment of chronic gastrointestinal issues like inflammatory bowel disease or irritable bowel syndrome, according to a press release. When a patient flushes the toilet, the mHealth platform photographs the stool as it moves through the pipes. That data is sent to a gastroenterologist, who can analyze the data for evidence of chronic issues.

 

A study conducted by Duke University researchers found that the platform had an 85.1 percent accuracy rate on stool form classification and a 76.3 percent accuracy rate on detection of gross blood. “Typically, gastroenterologists have to rely on patient self-reported information about their stool to help determine the cause of their gastrointestinal health issues, which can be very unreliable,” Deborah Fisher, MD, an associate professor of medicine at Duke and one of the study’s lead authors, said in the press release.

 

“Patients often can’t remember what their stool looks like or how often they have a bowel movement, which is part of the standard monitoring process,” she said. “The smart toilet technology will allow us to gather the long-term information needed to make a more accurate and timely diagnosis of chronic gastrointestinal problems.”

 

This remote patient monitoring platform has the potential to benefit both patients and healthcare providers. Patients won’t need to do anything out of the ordinary, and gastroenterologists won’t have to try and diagnose based on a patient’s description or recollection. “An IBD flare-up could be diagnosed using the smart toilet and the patient’s response to treatment could be monitored with the technology,” Sonia Grego, PhD, a lead researcher on the study and founding director of the Duke Smart Toilet Lab, said in the release. “This could be especially useful for patients who live in long-term care facilities who may not be able to report their conditions and could help improve initial diagnosis of acute conditions,”  The program makes use of something that nearly every American has at home. 

 

It’s not the first study to target the toilet. Back in 2019, researchers at the Rochester Institute of Technology developed a  sensor-embedded toilet seat. The toilet seat was designed to allow care providers to remotely monitor a patient’s weight, heart rate, blood pressure, blood oxygenation levels, and stroke volume.  Similar to the AI tool for toilet pipes, RIT’s smart toilet seat presents the ability to catch patients’ symptoms early enough before they could lead to something more severe.

 

With gastroenterology appearing on Doximity’s list of specialties that are least engaged in telehealth, this artificial intelligence software, once available to the public, could change minds. Gastroenterology usually requires in-person and hands-on interaction but using remote monitoring in this way might prove to be even more beneficial.

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Giant Filamentous Bacteria of 1 Millimeter Length Found in Mangrove Swamps Challenging Traditional Concepts of Biology

Giant Filamentous Bacteria of 1 Millimeter Length Found in Mangrove Swamps Challenging Traditional Concepts of Biology | Amazing Science | Scoop.it
Researchers describe a “’macro’ microbe” – a giant filamentous bacterium composed of a single cell discovered in the mangroves of Guadeloupe.

 

At first glance, the slightly murky waters in the tube look like a scoop of stormwater, complete with leaves, debris, and even lighter threads in the mix. But in the Petri dish, the thin vermicelli-like threads floating delicately above the leaf debris are revealed to be single bacterial cells, visible to the naked eye. The unusual size is notable because bacteria aren't usually visible without the assistance of microscope. "It's 5,000 times bigger than most bacteria. To put it into context, it would be like a human encountering another human as tall as Mount Everest," said Jean-Marie Volland, a scientist with joint appointments at the U.S. Department of Energy (DOE) Joint Genome Institute (JGI), a DOE Office of Science User Facility located at Lawrence Berkeley National Laboratory (Berkeley Lab) and the Laboratory for Research in Complex Systems (LRC) in Menlo Park, Calif. In the June 24, 2022, issue of the journal Science, Volland and colleagues, including researchers at the JGI and Berkeley Lab, LRC, and at the Université des Antilles in Guadeloupe, described the morphological and genomic features of this giant filamentous bacterium, along with its life cycle.

 

For most bacteria, their DNA floats freely within the cytoplasm of their cells. This newly discovered species of bacteria keeps its DNA more organized. "The big surprise of the project was to realize that these genome copies that are spread throughout the whole cell are actually contained within a structure that has a membrane," Volland said. "And this is very unexpected for a bacterium."

 

Strange Encounters in the Mangroves

The bacterium itself was discovered by Olivier Gros, a marine biology professor at the Université des Antilles in Guadeloupe, in 2009. Gros' research focuses on marine mangrove systems, and he was looking for sulfur-oxidizing symbionts in sulfur-rich mangrove sediments not far from his lab when he first encountered the bacteria. "When I saw them, I thought, 'Strange,'" he said. "In the beginning I thought it was just something curious, some white filaments that needed to be attached to something in the sediment like a leaf." The lab conducted some microscopy studies over the next couple of years, and realized it was a sulfur-oxidizing prokaryote.

 

Silvina Gonzalez-Rizzo, an associate professor of molecular biology at the Université des Antilles and a co-first author on the study, performed the 16S rRNA gene sequencing to identify and classify the prokaryote. "I thought they were eukaryotes; I didn't think they were bacteria because they were so big with seemingly a lot of filaments," she recalled of her first impression. "We realized they were unique because it looked like a single cell. The fact that they were a 'macro' microbe was fascinating!"

 

"She understood that it was a bacterium belonging to the genus Thiomargarita," Gros noted. She named it Ca. Thiomargarita magnifica. "Magnifica because magnus in Latin means big and I think it's gorgeous like the French word magnifique," Gonzalez-Rizzo explained. "This kind of discovery opens new questions about bacterial morphotypes that have never been studied before."

 

Characterizing the Giant Bacterium

Volland got involved with the giant Thiomargarita bacteria when he returned to the Gros lab as a postdoctoral fellow. When he applied to the discovery-based position at the LRC that would see him working at the JGI, Gros allowed him to continue research on the project.

 

At the JGI, Volland began studying Ca. T. magnifica in Tanja Woyke's Single Cells Group to better understand what this sulfur-oxidizing, carbon fixing bacterium was doing in the mangroves. "Mangroves and their microbiomes are important ecosystems for carbon cycling. If you look at the space that they occupy on a global scale, it's less than 1% of the coastal area worldwide. But when you then look at carbon storage, you'll find that they contribute 10-15% of the carbon stored in coastal sediments," said Woyke, who also heads the JGI's Microbial Program and is one of the article's senior authors. The team was also compelled to study these large bacteria in light of their potential interactions with other microorganisms. "We started this project under the JGI's strategic thrust of inter-organismal interactions, because large sulfur bacteria have been shown to be hot spots for symbionts," Woyke said. "Yet the project took us into a very different direction," she added.

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The octopus’ brain and the human brain share the same 'jumping genes' - LINE elements

The octopus’ brain and the human brain share the same 'jumping genes' - LINE elements | Amazing Science | Scoop.it

The octopus is an exceptional organism with an extremely complex brain and cognitive abilities that are unique among invertebrates. So much so that in some ways it has more in common with vertebrates than with invertebrates. The neural and cognitive complexity of these animals could originate from a molecular analogy with the human brain, as discovered by a research paper recently published in BMC Biology and coordinated by Remo Sanges from SISSA of Trieste and by Graziano Fiorito from Stazione Zoologica Anton Dohrn of Naples. The research shows that the same 'jumping genes' are active both in the human brain and in the brain of two species, Octopus vulgaris, the common octopus, and Octopus bimaculoides, the Californian octopus. A discovery that could help us understand the secret of the intelligence of these fascinating organisms.

 

Sequencing the human genome revealed as early as 2001 that over 45% of it is composed by sequences called transposons, so-called 'jumping genes' that, through molecular copy-and-paste or cut-and-paste mechanisms, can 'move' from one point to another of an individual's genome, shuffling or duplicating. In most cases, these mobile elements remain silent: they have no visible effects and have lost their ability to move. Some are inactive because they have, over generations, accumulated mutations; others are intact, but blocked by cellular defense mechanisms. From an evolutionary point of view even these fragments and broken copies of transposons can still be useful, as 'raw matter' that evolution can sculpt.

 

Among these mobile elements, the most relevant are those belonging to the so-called LINE (Long Interspersed Nuclear Elements) family, found in a hundred copies in the human genome and still potentially active. It has been traditionally though that LINEs' activity was just a vestige of the past, a remnant of the evolutionary processes that involved these mobile elements, but in recent years new evidence emerged showing that their activity is finely regulated in the brain. There are many scientists who believe that LINE transposons are associated with cognitive abilities such as learning and memory: they are particularly active in the hippocampus, the most important structure of our brain for the neural control of learning processes.

 

The octopus' genome, like ours, is rich in 'jumping genes', most of which are inactive. Focusing on the transposons still capable of copy-and-paste, the researchers identified an element of the LINE family in parts of the brain crucial for the cognitive abilities of these animals. The discovery, the result of the collaboration between Scuola Internazionale Superiore di Studi Avanzati, Stazione Zoologica Anton Dohrn and Istituto Italiano di Tecnologia, was made possible thanks to next generation sequencing techniques, which were used to analyze the molecular composition of the genes active in the nervous system of the octopus.

 

"The discovery of an element of the LINE family, active in the brain of the two octopuses species, is very significant because it adds support to the idea that these elements have a specific function that goes beyond copy-and-paste," explains Remo Sanges, director of the Computational Genomics laboratory at SISSA, who started working at this project when he was a researcher at Stazione Zoologica Anton Dohrn of Naples. The study, published in BMC Biology, was carried out by an international team with more than twenty researchers from all over the world.

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Rapid, scalable assessment of SARS-CoV-2 (CoVid19) cellular immunity by whole-blood PCR

Rapid, scalable assessment of SARS-CoV-2 (CoVid19) cellular immunity by whole-blood PCR | Amazing Science | Scoop.it

Fast, high-throughput methods for measuring the level and duration of protective immune responses to SARS-CoV-2 are needed to anticipate the risk of breakthrough infections. A research team now reports the development of two quantitative PCR assays for SARS-CoV-2-specific T cell activation. The assays are rapid, internally normalized and probe-based: qTACT requires RNA extraction and dqTACT avoids sample preparation steps. Both assays rely on the quantification of CXCL10 messenger RNA, a chemokine whose expression is strongly correlated with activation of antigen-specific T cells. On re-stimulation of whole-blood cells with SARS-CoV-2 viral antigens, viral-specific T cells secrete IFN-γ, which stimulates monocytes to produce CXCL10. CXCL10 mRNA can thus serve as a proxy to quantify cellular immunity. These types of assays may allow large-scale monitoring of the magnitude and duration of functional T cell immunity to SARS-CoV-2, thus helping to prioritize revaccination strategies in vulnerable populations. The T cell response to SARS-CoV-2 is detected using a PCR assay on whole blood.

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Global Impact of the First Year of COVID-19 Vaccination: a Mathematical Modelling Study

Global Impact of the First Year of COVID-19 Vaccination: a Mathematical Modelling Study | Amazing Science | Scoop.it
The first COVID-19 vaccine outside a clinical trial setting was administered on Dec 8, 2020. To ensure global vaccine equity, vaccine targets were set by the COVID-19 Vaccines Global Access (COVAX) Facility and WHO. However, due to vaccine shortfalls, these targets were not achieved by the end of 2021. We aimed to quantify the global impact of the first year of COVID-19 vaccination programs.
 
A mathematical model of COVID-19 transmission and vaccination was separately fit to reported COVID-19 mortality and all-cause excess mortality in 185 countries and territories. The impact of thr COVID-19 vaccination initiative was determined by estimating the additional lives lost if no vaccines had been distributed. We also estimated the additional deaths that would have been averted had the vaccination coverage targets of 20% set by COVAX and 40% set by WHO been achieved by the end of 2021.

Findings

Based on official reported COVID-19 deaths, we estimated that vaccinations prevented 14·4 million (95% credible interval [Crl] 13·7–15·9) deaths from COVID-19 in 185 countries and territories between Dec 8, 2020, and Dec 8, 2021. This estimate rose to 19·8 million (95% Crl 19·1–20·4) deaths from COVID-19 averted when we used excess deaths as an estimate of the true extent of the pandemic, representing a global reduction of 63% in total deaths (19·8 million of 31·4 million) during the first year of COVID-19 vaccination. In COVAX Advance Market Commitment countries, we estimated that 41% of excess mortality (7·4 million [95% Crl 6·8–7·7] of 17·9 million deaths) was averted. In low-income countries, we estimated that an additional 45% (95% CrI 42–49) of deaths could have been averted had the 20% vaccination coverage target set by COVAX been met by each country, and that an additional 111% (105–118) of deaths could have been averted had the 40% target set by WHO been met by each country by the end of 2021.

Interpretation

COVID-19 vaccination has substantially altered the course of the pandemic, saving tens of millions of lives globally. However, inadequate access to vaccines in low-income countries has limited the impact in these settings, reinforcing the need for global vaccine equity and coverage.
 
Published in The Lancet Infectious Diseases (June 23, 2022):

Via Juan Lama
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NASA's DAVINCI Mission To Take the Plunge Through the Massive Atmosphere of Venus

NASA's DAVINCI Mission To Take the Plunge Through the Massive Atmosphere of Venus | Amazing Science | Scoop.it

In a recently published paper, NASA scientists and engineers give new details about the agency’s Deep Atmosphere Venus Investigation of Noble gases, Chemistry, and Imaging (DAVINCI) mission, which will descend through the layered Venus atmosphere to the surface of the planet in mid-2031. DAVINCI is the first mission to study Venus using both spacecraft flybys and a descent probe.

 

DAVINCI, a flying analytical chemistry laboratory, will measure critical aspects of Venus’ massive atmosphere-climate system for the first time, many of which have been measurement goals for Venus since the early 1980s. It will also provide the first descent imaging of the mountainous highlands of Venus while mapping their rock composition and surface relief at scales not possible from orbit. The mission supports measurements of undiscovered gases present in small amounts and the deepest atmosphere, including the key ratio of hydrogen isotopes – components of water that help reveal the history of water, either as liquid water oceans or steam within the early atmosphere.

 

The mission’s carrier, relay and imaging spacecraft (CRIS) has two onboard instruments that will study the planet’s clouds and map its highland areas during flybys of Venus and will also drop a small descent probe with five instruments that will provide a medley of new measurements at very high precision during its descent to the hellish Venus surface.

 

“This ensemble of chemistry, environmental, and descent imaging data will paint a picture of the layered Venus atmosphere and how it interacts with the surface in the mountains of Alpha Regio, which is twice the size of Texas,” said Jim Garvin, lead author of the paper in the Planetary Science Journal and DAVINCI principal investigator from NASA’s Goddard Space Flight Center in Greenbelt, Maryland. “These measurements will allow us to evaluate historical aspects of the atmosphere as well as detect special rock types at the surface such as granites while also looking for tell-tale landscape features that could tell us about erosion or other formational processes.”

 

DAVINCI will make use of three Venus gravity assists, which save fuel by using the planet’s gravity to change the speed and/or direction of the CRIS flight system. The first two gravity assists will set CRIS up for a Venus flyby to perform remote sensing in the ultraviolet and the near infrared light, acquiring over 60 gigabits of new data about the atmosphere and surface. The third Venus gravity assist will set up the spacecraft to release the probe for entry, descent, science, and touchdown, plus follow-on transmission to Earth.

 

The first flyby of Venus will be six and half months after launch and it will take two years to get the probe into position for entry into the atmosphere over Alpha Regio under ideal lighting at “high noon,” with the goal of measuring the landscapes of Venus at scales ranging from 328 feet (100 meters) down to finer than one meter. Such scales enable lander style geologic studies in the mountains of Venus without requiring landing.  

 

Once the CRIS system is about two days away from Venus, the probe flight system will be released along with the titanium three foot (one meter) diameter probe safely encased inside. The probe will begin to interact with the Venus upper atmosphere at about 75 miles (120 kilometers) above the surface. The science probe will commence science observations after jettisoning its heat shield around 42 miles (67 kilometers) above the surface. With the heatshield jettisoned, the probe’s inlets will ingest atmospheric gas samples for detailed chemistry measurements of the sort that have been made on Mars with the Curiosity rover. During its hour-long descent to the surface, the probe will also acquire hundreds of images as soon as it emerges under the clouds at around 100,000 feet (30,500 meters) above the local surface.

 

“The probe will touch-down in the Alpha Regio mountains but is not required to operate once it lands, as all of the required science data will be taken before reaching the surface.” said Stephanie Getty, deputy principal investigator from Goddard. “If we survive the touchdown at about 25 miles per hour (12 meters/second), we could have up to 17-18 minutes of operations on the surface under ideal conditions.”

 

DAVINCI is tentatively scheduled to launch June 2029 and enter the Venusian atmosphere in June 2031. “No previous mission within the Venus atmosphere has measured the chemistry or environments at the detail that DAVINCI’s probe can do,” said Garvin. “Furthermore, no previous Venus mission has descended over the tesserae highlands of Venus, and none have conducted descent imaging of the Venus surface. DAVINCI will build on what Huygens probe did at Titan and improve on what previous in situ Venus missions have done, but with 21st century capabilities and sensors.” Discovery Program class missions like DAVINCI complement NASA's larger “flagship” planetary science explorations, with the goal of achieving outstanding results by launching more smaller missions using fewer resources and shorter development times. They are managed for NASA’s Planetary Science Division by the Planetary Missions Program Office at Marshall Space Flight Center in Huntsville, Alabama.

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8,000 kilometers per second: Star with the shortest orbital period around a black hole discovered

8,000 kilometers per second: Star with the shortest orbital period around a black hole discovered | Amazing Science | Scoop.it

Researchers at the University of Cologne and Masaryk University in Brno (Czech Republic) have discovered the fastest known star, which travels around a black hole in record time. The star, S4716, orbits Sagittarius A*, the black hole in the center of our Milky Way, in four years and reaches a speed of around 8,000 kilometers per second. S4716 comes as close as 100 AU (astronomical unit) to the black hole—a small distance by astronomical standards. One AU corresponds to 149,597,870 kilometers. The study has been published in The Astrophysical Journal.

 

In the vicinity of the black hole at the center of our galaxy is a densely packed cluster of stars. This cluster, called S cluster, is home to well over a hundred stars that differ in their brightness and mass. S stars move particularly fast. "One prominent member, S2, behaves like a large person sitting in front of you in a movie theater: it blocks your view of what's important," said Dr. Florian Peissker, lead author of the new study. "The view into the center of our galaxy is therefore often obscured by S2. However, in brief moments we can observe the surroundings of the central black hole."

 

By means of continuously refining methods of analysis, together with observations covering almost twenty years, the scientist now identified without a doubt a star that travels around the central supermassive black hole in just four years. A total of five telescopes observed the star, with four of these five being combined into one large telescope to allow even more accurate and detailed observations. "For a star to be in a stable orbit so close and fast in the vicinity of a supermassive black hole was completely unexpected and marks the limit that can be observed with traditional telescopes," said Peissker.

 

Moreover, the discovery sheds new light on the origin and evolution of the orbit of fast-moving stars in the heart of the Milky Way. "The short-period, compact orbit of S4716 is quite puzzling," Michael Zajaček, an astrophysicist at Masaryk University in Brno who was involved in the study, said. "Stars cannot form so easily near the black hole. S4716 had to move inwards, for example by approaching other stars and objects in the S cluster, which caused its orbit to shrink significantly."

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Scientists invent 'quantum flute' that can make particles of light move together

Scientists invent 'quantum flute' that can make particles of light move together | Amazing Science | Scoop.it
University of Chicago physicists have invented a "quantum flute" that, like the Pied Piper, can coerce particles of light to move together in a way that's never been seen before.

 

Described in two studies published in Physical Review Letters and Nature Physics, the breakthrough could point the way towards realizing quantum memories or new forms of error correction in quantum computers, and observing quantum phenomena that cannot be seen in nature.

 

Assoc. Prof. David Schuster's lab works on quantum bits—the quantum equivalent of a computer bit—which tap the strange properties of particles at the atomic and sub-atomic level to do things that are otherwise impossible. In this experiment, they were working with particles of light, known as photons, in the microwave spectrum.The system they devised consists of a long  cavity made in a single block of metal, designed to trap photons at microwave frequencies. The cavity is made by drilling offset holes— like holes in a flute.

 

"Just like in the musical instrument," Schuster said, "you can send one or several wavelengths of photons across the whole thing, and each wavelength creates a 'note' that can be used to encode quantum information." The researchers can then control the interactions of the "notes" using a master quantum bit, a superconducting electrical circuit. But their oddest discovery was the way the photons behaved together.

 

In nature, photons hardly ever interact—they simply pass through each other. With painstaking preparation, scientists can sometimes prompt two photons to react to each other's presence.

"Here we do something even weirder," Schuster said. "At first the photons don't interact at all, but when the total energy in the system reaches a tipping point, all of a sudden, they're all talking to each other."

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Cough is mediated by a complex set of neurobiological processes involving the peripheral nervous system, brainstem and higher brain

Cough is mediated by a complex set of neurobiological processes involving the peripheral nervous system, brainstem and higher brain | Amazing Science | Scoop.it

Chronic cough is globally prevalent across all age groups. This disorder is challenging to treat because many pulmonary and extra-pulmonary conditions can present with chronic cough, and cough can also be present without any identifiable underlying cause or be refractory to therapies that improve associated conditions. Most patients with chronic cough have cough hypersensitivity, which is characterized by increased neural responsiveness to a range of stimuli that affect the airways and lungs, and other tissues innervated by common nerve supplies.

 

Cough hypersensitivity presents as excessive coughing often in response to relatively innocuous stimuli, causing significant psychophysical morbidity and affecting patients’ quality of life. Understanding the mechanism(s) that contribute to cough hypersensitivity and excessive coughing in different patient populations and across the lifespan is advancing and has contributed to the development of new therapies for chronic cough in adults. Owing to differences in the pathology, the organs involved and individual patient factors, treatment of chronic cough is progressing towards a personalized approach, and, in the future, novel ways to endotype patients with cough may prove valuable in management.

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Minerva: Solving Math and Physics Problems with Language Models

Minerva: Solving Math and Physics Problems with Language Models | Amazing Science | Scoop.it

Language models have demonstrated remarkable performance on a variety of natural language tasks — indeed, a general lesson from many works, including BERTGPT-3Gopher, and PaLM, has been that neural networks trained on diverse data at large scale in an unsupervised way can perform well on a variety of tasks.

Quantitative reasoning is one area in which language models still fall far short of human-level performance.

 

Solving mathematical and scientific questions requires a combination of skills, including correctly parsing a question with natural language and mathematical notation, recalling relevant formulas and constants, and generating step-by-step solutions involving numerical calculations and symbolic manipulation. Due to these challenges, it is often believed that solving quantitative reasoning problems using machine learning will require significant advancements in model architecture and training techniques, granting models access to external tools such as Python interpreters, or possibly a more profound paradigm shift.

 

In “Solving Quantitative Reasoning Problems With Language Models” (to be released soon on the arXiv), we present Minerva, a language model capable of solving mathematical and scientific questions using step-by-step reasoning. We show that by focusing on collecting training data that is relevant for quantitative reasoning problems, training models at scale, and employing best-in-class inference techniques, we achieve significant performance gains on a variety of difficult quantitative reasoning tasks.

 

Minerva solves such problems by generating solutions that include numerical calculations and symbolic manipulation without relying on external tools such as a calculator. The model parses and answers mathematical questions using a mix of natural language and mathematical notation. Minerva combines several techniques, including few-shot promptingchain of thought or scratchpad prompting, and majority voting, to achieve state-of-the-art performance on STEM reasoning tasks. You can explore Minerva’s output with our interactive sample explorer!

 

A Model Built for Multi-step Quantitative Reasoning

To promote quantitative reasoning, Minerva builds on the Pathways Language Model (PaLM), with further training on a 118GB dataset of scientific papers from the arXiv preprint server and web pages that contain mathematical expressions using LaTeXMathJax, or other mathematical typesetting formats. Standard text cleaning procedures often remove symbols and formatting that are essential to the semantic meaning of mathematical expressions. By maintaining this information in the training data, the model learns to converse using standard mathematical notation.
 
Minerva also incorporates recent prompting and evaluation techniques to better solve mathematical questions. These include chain of thought or scratchpad prompting — where Minerva is prompted with several step-by-step solutions to existing questions before being presented with a new question — and majority voting. Like most language models, Minerva assigns probabilities to different possible outputs. When answering a question, rather than taking the single solution Minerva scores as most likely, multiple solutions are generated by sampling stochastically from all possible outputs. These solutions are different (e.g., the steps are not identical), but often arrive at the same final answer. Minerva uses majority voting on these sampled solutions, taking the most common result as the conclusive final answer.
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Grey wolf genomic history reveals a dual ancestry of dogs

Grey wolf genomic history reveals a dual ancestry of dogs | Amazing Science | Scoop.it

The grey wolf (Canis lupus) has been present across most of the northern hemisphere for the last few hundred thousand years and, unlike many other large mammals, did not go extinct in the Late Pleistocene. Studies of present-day genomes have found that current population structure formed mostly in the last ~30,000–20,000 years9,10,11, or roughly since the Last Glacial Maximum (LGM; ~28–23 thousand years ago (ka)12). Siberian wolves predating the LGM have ancestries that are largely basal to present-day diversity, which has led to suggestions that many pre-LGM wolf lineages went extinct13,14. Among the central questions is thus to what extent the global wolf population was subject to extinction processes or responded to climate change with new adaptations.

 

While it is clear that grey wolves gave rise to dogs, there is no consensus regarding when, where and how this happened1,2,3,4,5,6,7,8. Skeletal remains attributable to the present-day dog lineage appear archaeologically by 14 ka15, and genetic estimates of when the ancestors of dogs and modern wolves diverged range from 40–14 ka9,13,16. However, genetic data from modern and ancient dogs coupled with modern wolves, to which previous studies were largely restricted, may not be able to resolve the origin of dogs. Genetic diversity within dogs is affected by their dynamic history and is unable to confidently pinpoint an origin.

 

Relationships to modern wolves can likewise be affected by local extinction and gene flow since domestication6,9. Regions where early dogs have been found do not necessarily imply places of origin either, as the existence of earlier dogs elsewhere cannot be excluded. Instead, the origin of dogs could be resolved if wolf genetic diversity across space and time was exhaustively characterized and it could be determined which populations were closest to the ancestors of dogs.

 

A research team now sequenced 66 new ancient wolf genomes from Europe, Siberia and north-western North America to a median of 1× coverage (range, 0.02–13×) and incorporated five previously sequenced ancient wolf genomes14,17 and increased coverage for one13. They also sequenced an ancient dhole genome from the Caucasus, contextually dated to >70 ka, to serve as an outgroup. Fractions of X-chromosome DNA showed that 69% of the wolves were male (95% confidence interval (CI), 57–80%; P = 0.0013, binomial test), mirroring male over-representation among ancient genomes from woolly mammoths18, bison19, brown bears19 and domestic dogs8. The total dataset spans the last 100,000 years.

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Novel Biofinder instrument could advance detection of extraterrestrial life

Novel Biofinder instrument could advance detection of extraterrestrial life | Amazing Science | Scoop.it

An innovative scientific instrument, the Compact Color Biofinder, developed by a team of University of Hawai‘i at Mānoa researchers, may change the game in the search for signs of extraterrestrial life.

 

Most biological materials, for example, amino acids, fossils, sedimentary rocks, plants, microbes, proteins and lipids, have strong organic fluorescence signals that can be detected by specialized scanning cameras. In a study published in Nature Scientific Reports recently, the research team reported that the Biofinder is so sensitive that it can accurately detect the bio-residue in fish fossils from the 34-56 million year-old Green River formation.

 

"The Biofinder is the first system of its kind," said Anupam Misra, lead instrument developer and researcher at the Hawai'i Institute of Geophysics and Planetology at the UH Manoa School of Ocean and Earth Science and Technology (SOEST). "At present, there is no other equipment that can detect minute amounts of bio-residue on a rock during the daytime. Additional strengths of the Biofinder are that it works from a distance of several meters, takes video and can quickly scan a large area."

 

Though the Biofinder was first developed in 2012 by Misra, advances supported by the NASA PICASSO program culminated in the latest color version of the compact Biofinder. Finding evidence of biological residue in a vast planetary landscape is an enormous challenge. So, the team tested the Biofinder's detection abilities on the ancient Green River fish fossils and corroborated the results through laboratory spectroscopy analysis, scanning electron microscopy and fluorescence lifetime imaging microscopy.

 

"There are some unknowns regarding how quickly bio-residues are replaced by minerals in the fossilization process," said Misra. "However, our findings confirm once more that biological residues can survive millions of years, and that using biofluorescence imaging effectively detects these trace residues in real time."

 

The search for life -- which may be existing or extinct -- on planetary bodies is one of the major goals of planetary exploration missions conducted by NASA and other international space agencies. "If the Biofinder were mounted on a rover on Mars or another planet, we would be able to rapidly scan large areas quickly to detect evidence of past life, even if the organism was small, not easy to see with our eyes, and dead for many millions of years," said Misra. "We anticipate that fluorescence imaging will be critical in future NASA missions to detect organics and the existence of life on other planetary bodies."

 

"The Biofinder's capabilities would be critical for NASA's Planetary Protection program, for the accurate and no-invasive detection of contaminants such as microbes or extraterrestrial biohazards to or from planet Earth," said Sonia J. Rowley, the team biologist and co-author on the study. Misra and colleagues are applying to have the opportunity to send the Biofinder on a future NASA mission. "The detection of such biomarkers would constitute groundbreaking evidence for life outside of planet Earth," said Misra.

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Gaia continues its quest for the ultimate sky map and has already analyzed the positions of 2 billion objects

Gaia continues its quest for the ultimate sky map and has already analyzed the positions of 2 billion objects | Amazing Science | Scoop.it
Europe's Gaia space camera tracks everything that shines or moves in our Milky Way galaxy.

 

Europe's Gaia telescope has dropped its latest batch of data as it seeks to assemble the largest catalog of light sources in the sky. 

 

It is becoming a discovery machine like no other. Stars, asteroids and distant, bright galaxies - anything that can be visibly pinpointed is having its vital statistics measured by the observatory. Gaia has already mapped the positions of nearly two billion objects. Now, it can reveal more about their make-up.

"Essentially, previously, we could say very precisely where they are; now we can say what they are," Prof Nick Walton, from Cambridge University and a member of the Gaia science team, told BBC News. The European Space Agency's (Esa) Gaia satellite was launched in 2013 and placed a million miles from Earth.

 
 

It looks a bit like a spinning top hat. And as it rotates, the telescope uses its British-built billion-pixel camera to track everything that shines or moves - with astonishing accuracy. This is especially important when trying to measure distances to objects, which Gaia achieves by tracking how these targets wobble ever so slightly on the sky as it circles the Sun - a neat form of trigonometry that has now been practiced on 1.8 billion stars in, or very near, our Milky Way galaxy.

  • As the Earth goes around the Sun, relatively nearby stars appear to move against the "fixed" stars that are even further away
  • Because we know the Sun-Earth distance, we can use the parallax angle to work out the distance to the target star
  • But such angles are very small - less than one arcsecond for the nearest stars, or 0.05% of the full Moon's diameter
  • Gaia is making repeat observations to reduce measurement errors down to seven micro-arcseconds for the very brightest stars
  • Parallaxes are used to anchor other, more indirect techniques on the 'ladder' deployed to measure the most far-flung distances

In the previous release of data, in December 2020, Gaia also revealed basic brightness and color information on these stars.

The new data dump reveals spectroscopy information as well.

Spectroscopy slices the light coming from stars into its constituent colors, to reveal the chemistry, temperature, mass, age and velocity of the targets under study.

And for an important subset of stars - some 33 million - it has allowed Gaia scientists to determine how quickly these objects are moving towards or away from Earth.

 

Combined with their previously established movement across the sky, this means we now have their full three-dimensional behavior. Such information will give researchers even keener insights on how the Milky Way galaxy is structured and is evolving - from the past, into the future.

 

Gaia's data haul now includes:

  • two billion light sources - mostly stars but also many Solar System objects and some beyond the Milky Way
  • spectroscopic detail revealing temperature, chemistry, mass and age for 100s of millions of objects
  • 1.9 million quasars - distant galaxies where a voracious central black hole is powering light emission
  • 156,000 asteroids - critical for understanding their origin and possibility of them passing close to Earth
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How AI creates photorealistic images from simple text

How AI creates photorealistic images from simple text | Amazing Science | Scoop.it

Have you ever seen a puppy in a nest emerging from a cracked egg? What about a photo that’s overlooking a steampunk city with airships? Or a picture of two robots having a romantic evening at the movies? These might sound far-fetched, but a novel type of machine learning technology called text-to-image generation makes them possible. These models can generate high-quality, photorealistic images from a simple text prompt.

 

Within Google Research, our scientists and engineers have been exploring text-to-image generation using a variety of AI techniques. After a lot of testing we recently announced two new text-to-image models — Imagen and Parti. Both have the ability to generate photorealistic images but use different approaches. We want to share a little more about how these models work and their potential.

How text-to-image models work

With text-to-image models, people provide a text description and the models produce images matching the description as closely as possible. This can be something as simple as “an apple” or “a cat sitting on a couch” to more complex details, interactions and descriptive indicators like “a cute sloth holding a small treasure chest. A bright golden glow is coming from the chest. In the past few years, ML models have been trained on large image datasets with corresponding textual descriptions, resulting in higher quality images and a broader range of descriptions. This has sparked major breakthroughs in this area, including Open AI’s DALL-E 2.

How Imagen and Parti work

Imagen and Parti build on previous models. Transformer models are able to process words in relationship to one another in a sentence. They are foundational to how we represent text in our text-to-image models. Both models also use a new technique that helps generate images that more closely match the text description. While Imagen and Parti use similar technology, they pursue different, but complementary strategies.

 

Imagen is a Diffusion model, which learns to convert a pattern of random dots to images. These images first start as low resolution and then progressively increase in resolution. Recently, Diffusion models have seen success in both image and audio tasks like enhancing image resolution, recoloring black and white photos, editing regions of an image, uncropping images, and text-to-speech synthesis.

 

Parti’s approach first converts a collection of images into a sequence of code entries, similar to puzzle pieces. A given text prompt is then translated into these code entries and a new image is created. This approach takes advantage of existing research and infrastructure for large language models such as PaLM and is critical for handling long, complex text prompts and producing high-quality images.

 

These models have many limitations. For example, neither can reliably produce specific counts of objects (e.g. “ten apples”), nor place them correctly based on specific spatial descriptions (e.g. “a red sphere to the left of a blue block with a yellow triangle on it”). Also, as prompts become more complex, the models begin to falter, either missing details or introducing details that were not provided in the prompt. These behaviors are a result of several shortcomings, including lack of explicit training material, limited data representation, and lack of 3D awareness. We hope to address these gaps through broader representations and more effective integration into the text-to-image generation process.

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Exercise benefits from a pill? Science is closer to that goal

Exercise benefits from a pill? Science is closer to that goal | Amazing Science | Scoop.it

Researchers at Baylor College of Medicine, Stanford School of Medicine and collaborating institutions report today in the journal Nature that they have identified a molecule in the blood that is produced during exercise and can effectively reduce food intake and obesity in mice. The findings improve our understanding of the physiological processes that underlie the interplay between exercise and hunger.

 

"Regular exercise has been proven to help weight loss, regulate appetite and improve the metabolic profile, especially for people who are overweight and obese," said co-corresponding author Dr. Yong Xu, professor of pediatrics- nutrition and molecular and cellular biology at Baylor. "If we can understand the mechanism by which exercise triggers these benefits, then we are closer to helping many people improve their health."

 

"We wanted to understand how exercise works at the molecular level to be able to capture some of its benefits," said co-corresponding author Jonathan Long, MD, assistant professor of pathology at Stanford Medicine and an Institute Scholar of Stanford ChEM-H (Chemistry, Engineering & Medicine for Human Health). "For example, older or frail people who cannot exercise enough, may one day benefit from taking a medication that can help slow down osteoporosis, heart disease or other conditions."

 

Xu, Long and their colleagues conducted comprehensive analyses of blood plasma compounds from mice following intense treadmill running. The most significantly induced molecule was a modified amino acid called Lac-Phe. It is synthesized from lactate (a byproduct of strenuous exercise that is responsible for the burning sensation in muscles) and phenylalanine, an amino acid that is one of the regular building blocks of proteins.

 

In mice with diet-induced obesity (fed a high-fat diet), a high dose of Lac-Phe suppressed food intake by about 50% compared to control mice over a period of 12 hours without affecting their movement or energy expenditure. When administered to the mice for 10 days, Lac-Phe reduced cumulative food intake and body weight (owing to loss of body fat) and improved glucose tolerance.

The researchers also identified an enzyme called CNDP2 that is involved in the production of Lac-Phe and showed that mice lacking this enzyme did not lose as much weight on an exercise regime as a control group on the same exercise plan.

 

Interestingly, the team also found robust elevations in plasma Lac-Phe levels following physical activity in racehorses and humans. Data from a human exercise cohort showed that sprint exercise induced the most dramatic increase in plasma Lac-Phe, followed by resistance training and then endurance training. "This suggests that Lac-Phe is an ancient and conserved system that regulates feeding and is associated with physical activity in many animal species," Long said.

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The Gaia space telescope rocks the science of asteroids

The Gaia space telescope rocks the science of asteroids | Amazing Science | Scoop.it

The Gaia space mission of the European Space Agency ESA is constructing an ultra-precise three-dimensional map of our Milky Way galaxy, observing almost two billion stars or roughly one percent of all the stars in our galaxy. Gaia was launched in December 2013 and has collected science data from July 2014.

 

Recenlty, ESA released Gaia data in Data Release 3 (DR3). Gaia data allows for the derivation of asteroid and exoplanet orbits and physical properties. The data helps unveil the origin and future evolution of the Solar System and the Milky Way and helps understand stellar and planetary-system evolution and our place in the cosmos.

 

Gaia revolves about its axis slowly in about six hours and is composed of two optical space telescopes. Three science instruments allow for accurate determination of stellar positions and velocities as well as the spectral properties. Gaia resides at about 1,5 million kilometers from the Earth in the anti-Sun direction, where it orbits the Sun together with the Earth in the proximity of the so-called Sun-Earth Lagrange L2-point.

 

Gaia DR3 on June 13, 2022 was significant across astronomy. Some 50 scientific articles are being published with DR3, of which nine articles have been devoted to underscoring the exceptionally significant potential of DR3 for future research. The new DR3 data comprises, for example, the chemical compositions, temperatures, colors, masses, brightnesses, ages, and radial velocities of stars. DR3 includes the largest ever binary star catalog for the Milky Way, more than 150 000 Solar System objects, largely asteroids but also planetary satellites, as well as millions of galaxies and quasars beyond the Milky Way.

 

There are so many revolutionary advances that it is difficult to pinpoint a single most significant advance. Based on Gaia DR3, Finnish researchers will change the conception of asteroids in our Solar System, exoplanets and stars in our Milky Way galaxy, as well as galaxies themselves, including the Milky Way and its surrounding satellite galaxies. Returning to our home planet, Gaia will produce an ultraprecise reference frame for navigation and positioning, says Academy Professor Karri Muinonen from the University of Helsinki.

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The dark secret behind those cute AI-generated animal images

The dark secret behind those cute AI-generated animal images | Amazing Science | Scoop.it

Another month, another flood of weird and wonderful images generated by an artificial intelligence. In April 2022, OpenAI showed off its new picture-making neural network, DALL-E 2, which could produce remarkable high-res images of almost anything it was asked to. It outstripped the original DALL-E in almost every way.

 

Now, just a few weeks later, Google Brain has revealed its own image-making AI, called Imagen. And it performs even better than DALL-E 2: it scores higher on a standard measure for rating the quality of computer-generated images, and the pictures it produced were preferred by a group of human judges.

“We’re living through the AI space race!” one Twitter user commented. “The stock image industry is officially toast,” tweeted another.

 

Many of Imagen’s images are indeed jaw-dropping. At a glance, some of its outdoor scenes could have been lifted from the pages of National Geographic. Marketing teams could use Imagen to produce billboard-ready advertisements with just a few clicks.

 

But as OpenAI did with DALL-E, Google is going all in on cuteness. Both firms promote their tools with pictures of anthropomorphic animals doing adorable things: a fuzzy panda dressed as a chef making dough, a corgi sitting in a house made of sushi, a teddy bear swimming the 400-meter butterfly at the Olympics—and it goes on.

 

There’s a technical, as well as PR, reason for this. Mixing concepts like “fuzzy panda” and “making dough” forces the neural network to learn how to manipulate those concepts in a way that makes sense. But the cuteness hides a darker side to these tools, one that the public doesn’t get to see because it would reveal the ugly truth about how they are created.

 

Most of the images that OpenAI and Google make public are cherry-picked. We only see cute images that match their prompts with uncanny accuracy—that’s to be expected. But we also see no images that contain hateful stereotypes, racism, or misogyny. There is no violent, sexist imagery. There is no panda porn. And from what we know about how these tools are built—there should be. So, what's going on?

 

For now, the solution is to keep them caged up. OpenAI is making DALL-E 2 available only to a handful of trusted users; Google has no plans to release Imagen. That’s fine if these were simply proprietary tools. But these firms are pushing the boundaries of what AI can do and their work shapes the kind of AI that all of us live with. They are creating new marvels, but also new horrors— and moving on with a shrug. When Google’s in-house ethics team raised problems with the large language models, in 2020 it sparked a fight that ended with two of its leading researchers being fired.

 

Large language models and image-making AIs have the potential to be world-changing technologies, but only if their toxicity is tamed. This will require a lot more research. There are small steps to open these kinds of neural network up for widespread study. A few weeks ago Meta released a large language model to researchers, warts and all. And Hugging Face is set to release its open-source version of GPT-3 in the next couple of months. 

 

For now, enjoy the cute teddies.

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