A new type of vaccine developed by researchers at the University of Chicago’s Pritzker School of Molecular Engineering (PME) has shown in the lab setting that it can completely reverse autoimmune diseases like multiple sclerosis and type 1 diabetes— all without shutting down the rest of the immune system.

A typical vaccine teaches the human immune system to recognize a virus or bacteria as an enemy that should be attacked. The new “inverse vaccine” does just the opposite: it removes the immune system’s memory of one molecule. While such immune memory erasure would be unwanted for infectious diseases, it can stop autoimmune reactions like those seen in multiple sclerosis, type I diabetes, or rheumatoid arthritis, in which the immune system attacks a person’s healthy tissues.

The inverse vaccine, described in Nature Biomedical Engineering, takes advantage of how the liver naturally marks molecules from broken-down cells with “do not attack” flags to prevent autoimmune reactions to cells that die by natural processes. PME researchers coupled an antigen — a molecule being attacked by the immune system— with a molecule resembling a fragment of an aged cell that the liver would recognize as friend, rather than foe. The team showed how the vaccine could successfully stop the autoimmune reaction associated with a multiple-sclerosis-like disease.

“In the past, we showed that we could use this approach to prevent autoimmunity,” said Jeffrey Hubbell, the Eugene Bell Professor in Tissue Engineering and lead author of the new paper. “But what is so exciting about this work is that we have shown that we can treat diseases like multiple sclerosis after there is already ongoing inflammation, which is more useful in a real-world context.”

Introducing Robotic Transformer 2 (RT-2), a novel vision-language-action (VLA) model that learns from both web and robotics data, and translates this knowledge into generalized instructions for robotic control, while retaining web-scale capabilities. This work builds upon Robotic Transformer 1 (RT-1), a model trained on multi-task demonstrations which can learn combinations of tasks and objects seen in the robotic data. RT-2 shows improved generalisation capabilities and semantic and visual understanding, beyond the robotic data it was exposed to. This includes interpreting new commands and responding to user commands by performing rudimentary reasoning, such as reasoning about object categories or high-level descriptions.

Unidentified anomalous phenomena (UAP), popularly known as UFOs, are probably just balloons, planes, or solar glints and not extraterrestrial life, as per a recent report by NASA, Business Insider reported. NASA assembled an independent panel last year to study UAPs and compile data. The report, released Thursday, suggests new methods to study UAPs and confirms the lack of evidence for alien visitors on Earth.

According to David Spergel, the chair of the UAP Independent Study Team, unusual sightings can often be explained by planes, balloons, drones, weather phenomena, and instrument features. He cited a Chinese spy balloon mistaken for a UFO as an example.

The report also recommends the use of AI and machine learning to analyze data collected by NASA’s instruments, like the James Webb Space Telescope. Nicola Fox, head of the science directorate at NASA, affirmed that AI could help find patterns in complex data faster than humans.

NASA has also created a new position, Director of UAP Research, to lead these investigations. The appointed official, whose identity hasn’t been disclosed due to harassment concerns, will focus on determining the cause of these phenomena. NASA still maintains the possibility of extraterrestrial life somewhere in the universe. 

“If you ask me: Do I believe there is life in a universe that is so vast like ours… My personal answer is yes,” said Bill Nelson, NASA Administrator. He added that such life would have to belong to a highly advanced civilization due to the vast distances involved.

The poisonous birds inhabit one of Earth’s most pristine rainforests, a place as exotic as no other in the world. Hearing the words poisonous and bird coupled will be an eye-opener for most. But poisonous birds actually exist. And now, more species have been discovered in New Guinea’s jungles.

"We managed to identify two new species of poisonous birds on our most recent trip. These birds contain a neurotoxin that they can both tolerate and store in their feathers," says Knud Jønsson of the Natural History Museum of Denmark.

Jønsson and fellow UCPH researcher, Kasun Bodawatta have been on an Indiana Jones-like research trip, risking life and limb to regularly warring tribespeople and ex-cannibals amidst the jaw-dropping biodiversity of New Guinea’s rainforest. Here they captured two new bird species, each of which has developed the ability to consume toxic food and turn that into a poison of their own.

The other new poisonous bird discovered is the The regent whistler (Pachycephala schlegelii). The two birds that the researchers discovered to be poisonous are the regent whistler (Pachycephala schlegelii), a species that belongs to a family of birds with a wide distribution and easily recognizable song well-known from across the Indo-Pacific region, and the rufous-naped bellbird (Aleadryas rufinucha).

"We were really surprised to find these birds to be poisonous as no new poisonous bird species has been discovered in over two decades. Particularly, because these two bird species are so common in this part of the world," says Knud Jønsson.

Neurotoxin causes muscle spasms
Most people are familiar with South and Central America’s iconic poison dart frogs – especially the golden poison frog. These small, brightly colored amphibians can kill a human at the slightest touch. The discovery of the two new poisonous bird species in New Guinea, which carry the same type of toxin in their skin and feathers, demonstrates that the frog toxin is more widespread than once believed.

The name Batrachotoxin comes from the Greek word for frog – Batrachos – and therefore means frog toxin. It earned its name after being discovered in poison dart frogs. It is one of the most powerful known neurotoxins and 250 times more toxic than strychnine.

Batrachotoxin works by forcing sodium channels in skeletal muscles to lock in an open position. This causes muscles to go into extreme spasm and can lead to death.

The new UCPH research demonstrates that animals which adapt to the toxin probably require changes in the voltage-gated sodium (NaV) channels where the toxin binds. The birds have mutations in the same areas as golden poison frog, but the specific changes are different.

The poison in these birds’ bodies and plumage is called Batrachotoxin. It is an incredibly potent neurotoxin that, in higher concentrations, such as those found in the skin of golden poison frogs, leads to muscle cramps and cardiac arrest nearly immediately after contact.

"The bird’s toxin is the same type as that found in frogs, which is a neurotoxin that, by forcing sodium channels in skeletal muscle tissue to remain open, can cause violent convulsions and ultimately death," explains Kasun Bodawatta.

With the world’s first exascale supercomputing system now open to full user operations, research teams are harnessing Frontier’s power and speed to tackle some of the most challenging problems in modern science.

The HPE Cray EX system at the Department of Energy’s Oak Ridge National Laboratory debuted in May 2022 as the fastest computer on the planet and first machine to break the exascale barrier at 1.1 exaflops, or 1.1 quintillion calculations per second. That’s more calculations per second than every human on Earth could perform in four years.

Frontier remains in the number one spot on the May 2023 TOP500 rankings, with an updated HPL, or high-performance Linpack, score of 1.194 exaflops. The increase of .092 exaflops, or 92 petaflops, is equivalent to the eighth most powerful supercomputer in the world on the TOP500 List. Engineers at the Oak Ridge Leadership Computing Facility, which houses Frontier and its predecessor Summit, expect that Frontier’s speeds could ultimately top 1.4 exaflops, or 1.4 quintillion calculations per second.

In addition to the updated HPL number, the Frontier team has improved the High-Performance Linpack-Mixed Precision Benchmark, HPL-MxP, to nearly 10 EF. Frontier’s HPL-MxP performance is now 9.950 EF, or 9.95 quintillion flops per second improved from 7.9 exaflops in November 2022. “Frontier represents the culmination of more than a decade of hard work by dedicated professionals from across academia, private business and the national laboratory complex through the Exascale Computing Initiative to realize a goal that once seemed barely possible,” said Doug Kothe, ORNL’s associate laboratory director for computing and computational sciences. “This machine will shrink the timeline for discoveries that will change the world for the better and touch everyone on Earth.”

Solar technologies have become increasingly advanced over the years, with the discovery of new photovoltaic materials and designs. While solar cells based on a mixture of organic and inorganic halide perovskite materials have been the topic of numerous research studies and achieved promising performances, these cells are often difficult to fabricate on a large-scale.

Researchers at Chonnam University in South Korea recently introduced an alternative solar cell design fully based on inorganic perovskites. Their solar cells, introduced in Nature Energy, could be easier to fabricate on a large-scale, while nonetheless achieving promising power conversion efficiencies (PCEs). "Previous efforts in the perovskite community mostly used single junction and single phase for the fabrication of perovskite solar cells using hazardous antisolvents," Dr. Sawanta S. Mali, lead author of the paper, told Tech Xplore.

"Instead, we introduced an anti-solvent free hot-air method for fabrication of beta (β)-CsPbI3 phase in ambient condition and gamma (γ)-CsPbI3 phase has been deposited on to β-CsPbI3 bottom layer using simple thermal evaporation method. Both these two phases playing key role in charge extraction process which results in >21.5 % power conversion efficiency."

The key objective of the recent work by Dr. Sawanta and his colleagues was to create new solar cells fully based on inorganic perovskites using a developed method that could be easy to up-scale. Ultimately, they fabricated their solar cells using hot-air and thermal evaporation deposition techniques that work at ambient conditions without requiring polar solvents (i.e., liquids containing both positive and negative charges).

We introduced an anti-solvent free hot-air method for fabrication of bottom β-CsPbI3 phase in ambient condition and then γ-CsPbI3 phase has been deposited on to β-CsPbI3 bottom layer using simple thermal evaporation method," Dr. Sawanta explained. "Both these two phases playing key role in charge extraction process. The unique advantage of our approach is that both layers are deposited via an anti-solvent free deposition technique."

Circulating proteins have important functions in inflammation and a broad range of diseases. To identify genetic influences on inflammation-related proteins, researchers conducted a genome-wide protein quantitative trait locus (pQTL) study of 91 plasma proteins measured using the Olink Target platform in 14,824 participants. We identified 180 pQTLs (59 cis, 121 trans).

Integration of pQTL data with eQTL and disease genome-wide association studies provided insight into pathogenesis, implicating lymphotoxin-α in multiple sclerosis. Using Mendelian randomization (MR) to assess causality in disease etiology, they identified both shared and distinct effects of specific proteins across immune-mediated diseases, including directionally discordant effects of CD40 on risk of rheumatoid arthritis versus multiple sclerosis and inflammatory bowel disease. MR implicated CXCL5 in the etiology of ulcerative colitis (UC) and they show elevated gut CXCL5 transcript expression in patients with UC.

These results identify targets of existing drugs and provide a powerful resource to facilitate future drug target prioritization.

Two recent papers have demonstrated that during a critical early period of brain development, the gut’s microbiome — the assortment of bacteria that grow within in it — helps to mold a brain system that’s important for social skills later in life. Scientists found this influence in fish, but molecular and neurological evidence plausibly suggests that some form of it could also occur in mammals, including humans.

In a recent paper published in PLOS Biology, researchers found that zebra fish who grew up lacking a gut microbiome were far less social than their peers with colonized colons, and the structure of their brains reflected the difference. In a related article in BMC Genomics in late September, they described molecular characteristics of the neurons affected by the gut bacteria. Equivalents of those neurons appear in rodents, and scientists can now look for them in other species, including humans.

The people at Google/DeepMind study how vision-language models trained on Internet-scale data can be incorporated directly into end-to-end robotic control to boost generalization and enable emergent semantic reasoning. Their goal is to enable a single end-to-end trained model to both learn to map robot observations to actions and enjoy the benefits of large-scale pre-training on language and vision-language data from the web. To this end, they propose to co-fine-tune state-of-the-art vision-language models on both robotic trajectory data and Internet-scale vision-language tasks, such as visual question answering. In contrast to other approaches, they propose a simple, general recipe to achieve this goal: in order to fit both natural language responses and robotic actions into the same format, they express the actions as text tokens and incorporate them directly into the training set of the model in the same way as natural language tokens. They refer to such category of models as vision-language-action models (VLA) and instantiate an example of such a model, which they call RT-2. Extensive evaluation (6k evaluation trials) shows that this approach leads to performant robotic policies and enables RT-2 to obtain a range of emergent capabilities from Internet-scale training. This includes significantly improved generalization to novel objects, the ability to interpret commands not present in the robot training data (such as placing an object onto a particular number or icon), and the ability to perform rudimentary reasoning in response to user commands (such as picking up the smallest or largest object, or the one closest to another object). The team further shows that incorporating chain of thought reasoning allows RT-2 to perform multi-stage semantic reasoning, for example figuring out which object to pick up for use as an improvised hammer (a rock), or which type of drink is best suited for someone who is too sleepy (an energy drink).

Blue laser specialist NUBURU is quickly increasing its foothold in the additive manufacturing (AM) space. In addition to receiving a series of patents and establishing a partnership for copper 3D printing with Essentium, the Colorado-based business has just been awarded an Small Business Innovation Research (SBIR) Phase II contract from the U.S. Air Force to develop a blue laser-based 3D printing solution with area printing technology.

“NUBURU has already pioneered metal welding applications within batteries, e-mobility and consumer electronics, and we are excited to continue expanding our capabilities into metal 3D printing, all with the same powerful blue laser technology,” said Ron Nicol, executive chairman of NUBURU.“ This project will help to bring area printing, with its high throughput capabilities and cost advantages, to key markets such as aerospace, automotive and more.”

With funding from the Air Force’s innovation arm, AFWERX, the company aims to develop a new 3D printer relying on NUBURU’s blue laser technology to increase part size and metal density while speeding up builds by 100 times. Moreover, the technology could potentially achieve micron-level resolution, as well as “zero post-processing and part shrinkage.” Those are obviously bold claims, but NUBURU suggests that the ability of metals to absorb blue laser more efficiently, combined with “area printing” would make it possible.

The concept of “area printing” is a new and ill-defined one, with really only one company publicly pursuing it. Boston-area startup Seurat is using “2 million points of light” to 3D print metal powder one large segment of a build bed at a time. EOS was, at one time, exploring what seemed to be a similar technique, but we haven’t seen the results of that work. 3DPrint.com Executive Editor Joris Peels has theorized that Trumpf is capable of its own version of the process. With this military project, NUBURU would be able to achieve further implementation of its blue laser technology in AM but also applied to a method that is unique in itself.

“We are honored to bring the power of blue laser technology and next-generation 3D printing capabilities to the United States military through this contract,” said Mark Zediker PhD, CEO and co-founder of NUBURU. “By combining the absorption advantages of blue lasers with area printing technology, we aim to create larger scale 3D printers that can offer up to100x the printing speed of an infrared laser-based printer with full metal density.  If we are successful, this could allow the military to build replacement parts for older aircraft that have been obsoleted by the original suppliers and can otherwise take months to procure.  This would greatly diminish the time required to build and replace critical components and would allow aircraft to return to operational readiness more quickly.”

Despite being larger than the original Starlink satellites, the new "Mini" version is fainter, meeting astronomers' recommendations.

An autonomous drone has competed against human drone-racing champions — and won. The victory can be attributed to savvy engineering and a type of artificial intelligence that learns mostly through trial and error.

First-person view (FPV) drone racing is a televised sport in which professional competitors pilot high-speed aircraft through a 3D circuit. Each pilot sees the environment from the perspective of their drone by means of video streamed from an onboard camera. Reaching the level of professional pilots with an autonomous drone is challenging because the robot needs to fly at its physical limits while estimating its speed and location in the circuit exclusively from onboard sensors.

Here the authors of this paper introduce Swift, an autonomous system that can race physical vehicles at the level of the human world champions. The system combines deep reinforcement learning (RL) in simulation with data collected in the physical world. Swift competed against three human champions, including the world champions of two international leagues, in real-world head-to-head races. Swift won several races against each of the human champions and demonstrated the fastest recorded race time.

This work represents a milestone for mobile robotics and machine intelligence, which may inspire the deployment of hybrid learning-based solutions in other physical systems. An autonomous system is described that combines deep reinforcement learning with onboard sensors collecting data from the physical world, enabling it to fly faster than human world champion drone pilots around a race track.

National Supercomputing Center in Wuxi builds a yet another supercomputer that claims exascale performance. After the U.S. government imposed crippling sanctions against select Chinese high-tech and supercomputer companies through 2019 and 2020, firms like Huawei had to halt chip development; it is impossible to build competitive processors without access to leading-edge nodes. But Jiangnan Computing Lab, which develops Sunway processors, and National Supercomputing Center in Wuxi kept building new supercomputers and recently even submitted results of their latest machine for the Association for Computing Machinery's Gordon Bell prize.

The new Sunway supercomputer built by the National Supercomputing Center in Wuxi (an entity blacklisted in the U.S.) employs around feature approximately 19.2 million cores across 49,230 nodes, reports Supercomputing.org. To put the number into context, Frontier, the world's highest-performing supercomputer, uses 9472 nodes and consumes 21 MW of power. Meanwhile, the National Supercomputing Center in Wuxi does not disclose power consumption of its latest system.

Interestingly, the new supercomputer seems to be based on the already known 390-core Sunway processor that derive from the Sunway SW26010 CPUs and have been around since 2021. Therefore, the new system increased the number of processors, but not their architectural efficiency, so its power consumption is likely to be gargantuan. Meanwhile, actual performance of the machine is unknown, since scaling out has its limits even in the supercomputer world.

The National Supercomputing Center in Wuxi has not disclosed performance numbers of its new supercomputer, and it is hard to make any estimations about its performance at this point. The reason why we called it ‘exascale’ is because its predecessor, the Sunway Oceanlite from 2021, was estimated to offer compute performance of around 1 ExaFLOPS.

Meanwhile, engineers revealed the workload that it used the machine for. Apparently, the the group created a new code for large whirlpool simulations to address compressible currents in turbo machinery. They applied it to NASA’s grand challenge problem using an advanced unstructured solver for a high-pressure turbine sequence with 1.69 billion mesh components and 865 billion degrees of freedom (judged by the number of variables). Given how complex the simulation is, it is likely that the machine is indeed quite powerful. Meanwhile, there is no word whether the simulation was conducted with FP64 precision, or precision was sacrificed for the sake of performance.

One Las Vegas company is incorporating AI and ChatGPT into the private jet sector. Jet.AI, previously known as Jet Token, has just launched a next level AI booking tool.

Jet.AI Inc., an innovative private aviation and artificial intelligence (“AI”) company, today announced its AI-powered charter booking application, CharterGPT, has been approved for release by both Apple and Jet.AI and is now live in the iOS store. The CharterGPT app communicates with both consumers and charter operators to reduce the time spent by a broker as the “human-in-the-loop,” with the assistance of natural language processing.

“Customers now have the power and convenience of an impressive, real-world application of transformative AI technology that is efficient, knowledgeable and fun to use,” said Mike Winston, Founder and Executive Chairman of Jet.AI. “We’re at the vanguard of bringing AI to the aviation industry. Private aviation is a great place to start where we can control more, understand more, establish credibility and scalability before we expand to broader applications in aviation.”

The CharterGPT app is now available for download on the iOS store and is expected to release on the Android store in the coming weeks. For more information on Jet.AI and CharterGPT, please visit www.jet.ai.The next iteration of the app, due in September, is expected to integrate with Stripe for payments. 

A stroke survivor is learning to walk independently again thanks to high-tech trousers powered by AI. Julie Lloyd, 65, is part of the UK's first trial of the "smart garment" that she described as a breakthrough for fellow stroke patients.

The "NeuroSkin" trousers stimulate her paralysed leg using electrodes controlled by artificial intelligence. The Stroke Association said new technologies are giving hope to the UK's 1.3 million stroke survivors.

The developers of NeuroSkin said the invention is already revolutionizing stroke care in France, but Ms Lloyd is one of the first involved in the UK's own trial. "My leg is almost feeling as if it's being guided," she said. After first experiencing an uncomfortable "tingling feeling", she said that within a few minutes she was walking unaided for the first time in six months.

"My leg was suddenly propelled up from the floor and made me feel safe walking, and that's the part that I've honestly not felt at all with all the physiotherapy I've had," she said. Julie needs a cane to walk her dogs near her home. "I've never felt since my stroke as elated as I feel this moment."

The businesswoman, from Penarth in Vale of Glamorgan, had a minor stroke in January which resulted in her left arm and left leg being partially paralyzed. She currently relies on a cane to walk. "Life before was very energetic, very active," she said, explaining how she ran the Cardiff half marathon before her stroke. "It has taken away a lifestyle I had and that's been terribly tragic."

Her rehabilitation involves hours of repetitive exercises aimed at "teaching" her brain to work around the area damaged by the stroke and make new connections to control her left side. The progress has been slow and grueling. But when she got to about 3,000 steps a day with a cane, her physiotherapist recommended she enter the trial of the AI-powered tech.

The OncoNPC machine-learning model may help identify sites of origin for cancers whose origins are unknown, which could enable targeted tumor treatments.

For a small percentage of cancer patients, doctors are unable to determine where their cancer originated. This makes it much more difficult to choose a treatment for those patients, because many cancer drugs are typically developed for specific cancer types.

A new approach developed by researchers at MIT and Dana-Farber Cancer Institute may make it easier to identify the sites of origin for those enigmatic cancers. Using machine learning, the researchers created a computational model that can analyze the sequence of about 400 genes and use that information to predict where a given tumor originated in the body.

Using this model, the researchers showed that they could accurately classify at least 40 percent of tumors of unknown origin with high confidence, in a dataset of about 900 patients. This approach enabled a 2.2-fold increase in the number of patients who could have been eligible for a genomically guided, targeted treatment, based on where their cancer originated.

“That was the most important finding in our paper, that this model could be potentially used to aid treatment decisions, guiding doctors toward personalized treatments for patients with cancers of unknown primary origin,” says Intae Moon, an MIT graduate student in electrical engineering and computer science who is the lead author of the new study. Alexander Gusev, an associate professor of medicine at Harvard Medical School and Dana-Farber Cancer Institute, is the senior author of the paper, which appears today in Nature Medicine.

A recent study published in the journal Geology attempts to interpret the patterns of sand dunes, which are mounds frequently formed by aeolian (wind) processes and range in size from small ripples observed on beaches to massive structures observed in the desert. Specifically, the researchers focused on patterns of dune crestlines. Different dune crestline patterns might appear as mundane features, but their formations are often the result of a myriad of influences, including climate change, surface processes, and atmospheric phenomena.

However, questions pertaining to the processes responsible for the different crestline patterns have baffled scientists. But the findings from this recent study could provide researchers insights into environmental variances not only on Earth, but other dune-harboring planetary worlds in our own solar system. These currently include three of the four terrestrial planets, Venus, Earth, and Mars; smaller bodies such as Jupiter’s volcanic moon, Io; Saturn’s largest moon, Titan; and even dwarf planet Pluto.

“When you look at other planets, all you have is pictures taken from hundreds to thousands of kilometers away from the surface,” said Dr. Mathieu Lapôtre, who is an assistant professor of Earth and planetary sciences in the Stanford Doerr School of Sustainability, and a co-author on the study. “You can see dunes – but that’s it. You don’t have access to the surface. These findings offer a really exciting new tool to decipher the environmental history of these other planets where we have no data.”

Dune interactions are defined as when their crestlines are near one another, and it’s these interactions result in the dunes establishing a balance, or equilibrium, with their surrounding environment. Therefore, the researchers hypothesized that a large amount of dune interactions could be interpreted as recent or nearby changes regarding those confined conditions.

For this study, the researchers analyzed changes in specific known environmental conditions, including sand quantity and wind direction, using orbital images of dune field sites numbering 30 and 16 on Earth and Mars, respectively. Examples of Earth dune field sites included Rice Valley, White Sands, the Namib Desert, and the Tengger Desert. Examples of Martian dune field sites included Nili Patera, Kaiser Crater, Rabe Crater, and Hargraves Crater.

Believable proxies of human behavior can empower interactive applications ranging from immersive environments to rehearsal spaces for interpersonal communication to prototyping tools. AI scientists now introduce generative agents–computational software agents that simulate believable human behavior.

Generative agents wake up, cook breakfast, and head to work; artists paint, while authors write; they form opinions, notice each other, and initiate conversations; they remember and reflect on days past as they plan the next day. To enable generative agents, they were able to describe an architecture that extends a large language model to store a complete record of the agent’s experiences using natural language, synthesize those memories over time into higher-level reflections, and retrieve them dynamically to plan behavior. They instantiate generative agents to populate an interactive sandbox environment inspired by "The Sims", where end users can interact with a small town of twenty five agents using natural language. In an evaluation, these generative agents produce believable individual and emergent social behaviors: for example, starting with only a single user-specified notion that one agent wants to throw a Valentine’s Day party, the agents autonomously spread invitations to the party over the next two days, make new acquaintances, ask each other out on dates to the party, and coordinate to show up for the party together at the right time. The researchers demonstrate through ablation that the components of this agent architecture–observation, planning, and reflection–each contribute critically to the believability of agent behavior. By fusing large language models with computational, interactive agents, this work introduces architectural and interaction patterns for enabling believable simulations of human behavior.