Scientists in the UK have launched a six-year project to create the first map of babies' brain in a critical period of growth in the womb and after birth.
By the time a baby takes its first breath many of the key pathways between nerves have already been made. And some of these will help determine how a baby thinks or sees the world, and may have a role to play in the development of conditions such as autism, scientists say.
But how this rich neural network assembles in the baby before birth is relatively unchartered territory.
Researchers from Guy's and St Thomas' Hospital, King's College London, Imperial College and Oxford University aim to produce a dynamic wiring diagram of how the brain grows, at a level of detail that they say has been impossible until now.
They hope that by charting the journeys of bundles of nerves in the final three months of pregnancy, doctors will be able to understand more about how they can help in situations when this process goes wrong.
Prof David Edwards, director of the Centre for the Developing Brain, who is leading the research, says: "There is a distressing number of children in our society who grow up with problems because of things that happen to them around the time of birth or just before birth.
"It is very important to be able to scan babies before they are born, because we can capture a period when an awful lot is changing inside the brain, and it is a time when a great many of the things that might be going wrong do seem to be going wrong."
The study - known as the Developing Human Connectome Project - hopes to look at more than 1,500 babies, studying many aspects of their neurological development.
By examining the brains of babies while they are still growing in the womb, as well as those born prematurely and at full term, the scientists will try to define baselines of normal development and investigate how these may be affected by problems around birth.
Researchers aim to understand more about how the brain is affected by prematurity and they plan to share their map with the wider research community.
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Machine-learning system trained on millions of human audio clips shows promise for detecting COVID-19 and tuberculosis. A team led by Google scientists has developed a machine-learning tool that can detect and monitor health conditions by evaluating noises such as coughing and breathing. The artificial intelligence (AI) system, trained on millions of audio clips of human sounds, might one day be used by physicians to diagnose diseases including COVID-19 and tuberculosis and to assess how well a person’s lungs are functioning. This is not the first time a research group has explored using sound as a biomarker for disease. The concept gained traction during the COVID-19 pandemic, when scientists discovered that it was possible to detect the respiratory disease through a person’s cough.
What’s new about the Google system — called Health Acoustic Representations (HeAR) — is the massive data set that it was trained on, and the fact that it can be fine-tuned to perform multiple tasks. The researchers, who reported the tool earlier this month in a preprint1 that has not yet been peer reviewed, say it’s too early to tell whether HeAR will become a commercial product. For now, the plan is to give interested researchers access to the model so that they can use it in their own investigations. “Our goal as part of Google Research is to spur innovation in this nascent field,” says Sujay Kakarmath, a product manager at Google in New York City who worked on the project....
The COVID-19 pandemic has affected hundreds of millions of individuals and caused more than six million deaths. The prolonged pandemic duration and the continual inter-individual transmissibility have contributed to the emergence of a wide variety of SARS-CoV-2 variants. Genomic surveillance and phylogenetic studies have shown that substantial mutations in crucial supersites of spike glycoprotein modulate the binding affinity of the evolved SARS-COV-2 lineages to ACE2 receptors and modify the binding of spike protein with neutralizing antibodies.
The immunological spike mutations have been associated with differential transmissibility, infectivity, and therapeutic efficacy of the vaccines and the immunological therapies among the new variants. This review highlights the diverse genetic mutations assimilated in various SARS-CoV-2 variants. The implications of the acquired mutations related to viral transmission, infectivity, and COVID-19 severity are discussed. This review also addresses the effectiveness of human neutralizing antibodies induced by SARS-CoV-2 infection or immunization and the therapeutic antibodies against the ascended variants.
Using a virus-like delivery particle made from DNA, researchers from MIT and the Ragon Institute of MGH, MIT, and Harvard have created a vaccine that can induce a strong antibody response against SARS-CoV-2.
The vaccine, which has been tested in mice, consists of a DNA scaffold that carries many copies of a viral antigen. This type of vaccine, known as a particulate vaccine, mimics the structure of a virus. Most previous work on particulate vaccines has relied on protein scaffolds, but the proteins used in those vaccines tend to generate an unnecessary immune response that can distract the immune system from the target.
In the mouse study, the researchers found that the DNA scaffold does not induce an immune response, allowing the immune system to focus its antibody response on the target antigen. “DNA, we found in this work, does not elicit antibodies that may distract away from the protein of interest,” says Mark Bathe, an MIT professor of biological engineering. “What you can imagine is that your B cells and immune system are being fully trained by that target antigen, and that’s what you want — for your immune system to be laser-focused on the antigen of interest.”
Viral infection can lead to acute respiratory distress syndrome that damages the lungs and is often lethal. Zhao et al. show that the TGF-β receptor 2 (TGF-βR2) is important for lung endothelial cells to recover after injury due to influenza virus or SARS-CoV-2 infection. The researchers used mice, human organoids, and human endothelial cells to demonstrate how TGF-βR2 interacts with VEGFA specifically in endothelial lung cells to promote tissue regeneration after viral injury. They also developed lung-targeted nanoparticles to deliver Vegfa mRNA to promote lung healing in mice lacking TGF-βR2. This study highlights the potential for targeting TGF-βR2 signaling in lung endothelial cells as a treatment for acute respiratory distress syndrome.
Disruption of pulmonary vascular homeostasis is a central feature of viral pneumonia, wherein endothelial cell (EC) death and subsequent angiogenic responses are critical determinants of the outcome of severe lung injury. A more granular understanding of the fundamental mechanisms driving reconstitution of lung endothelium is necessary to facilitate therapeutic vascular repair.
Now a team of scientists demonstrated that TGF-β signaling through TGF-βR2 (transforming growth factor–β receptor 2) is activated in pulmonary ECs upon influenza infection, and mice deficient in endothelial Tgfbr2 exhibited prolonged injury and diminished vascular repair. Loss of endothelial Tgfbr2 prevented autocrine Vegfa (vascular endothelial growth factor α) expression, reduced endothelial proliferation, and impaired renewal of aerocytes thought to be critical for alveolar gas exchange.
Angiogenic responses through TGF-βR2 were attributable to leucine-rich α-2-glycoprotein 1, a proangiogenic factor that counterbalances canonical angiostatic TGF-β signaling. Further, the scientists developed a lipid nanoparticle that targets the pulmonary endothelium, Lung-LNP (LuLNP). Delivery of Vegfa mRNA, a critical TGF-βR2 downstream effector, by LuLNPs improved the impaired regeneration phenotype of EC Tgfbr2 deficiency during influenza injury.
These studies defined a role for TGF-βR2 in lung endothelial repair and demonstrated efficacy of an efficient and safe endothelial-targeted LNP capable of delivering therapeutic mRNA cargo for vascular repair in influenza infection.
Armed with a catalog of hundreds of thousands of DNA and RNA virus species in the world's oceans, scientists are now zeroing in on the viruses most likely to combat climate change by helping trap carbon dioxide in seawater or, using similar techniques, different viruses that may prevent methane's escape from thawing Arctic soil.
By combining genomic sequencing data with artificial intelligence analysis, researchers have identified ocean-based viruses and assessed their genomes to find that they "steal" genes from other microbes or cells that process carbon in the sea. Mapping microbial metabolism genes, including those for underwater carbon metabolism, revealed 340 known metabolic pathways throughout the global oceans. Of these, 128 were also found in the genomes of ocean viruses.
"I was shocked that the number was that high," said Matthew Sullivan, professor of microbiology and director of the Center of Microbiome Science at The Ohio State University. Having mined this massive trove of data via advances in computation, the team has now revealed which viruses have a role in carbon metabolism and are using this information in newly developed community metabolic models to help predict how using viruses to engineer the ocean microbiome toward better carbon capture would look.
"The modeling is about how viruses may dial up or dial down microbial activity in the system," Sullivan said. "Community metabolic modeling is telling me the dream data point: which viruses are targeting the most important metabolic pathways, and that matters because it means they're good levers to pull on."
If fully successful, the IM-1 cargo mission would be the first U.S. lunar landing in more than 50 years.
Intuitive Machines’ Nova-C lunar lander launched from Florida on SpaceX’s Falcon 9 rocket, beginning the IM-1 mission.
If fully successful, the IM-1 cargo mission would be the first U.S. lunar landing in more than 50 years.
The Intuitive Machines lander is expected to spend about eight days traveling to the moon before descending to the surface.
Intuitive Machines, Inc. is a diversified space company focused on space exploration. It is a provider and supplier of space products and services that enable sustained robotic and human exploration to the Moon, Mars, and beyond. Its products and services are offered through its four business units: Lunar Access Services, Orbital Services, Lunar Data Services and Space Products and Infrastructure. Its Orbital Services segment is designed to support satellites and stations in Earth and lunar orbits. Orbital Services consists of leveraging its technologies and government funds to establish a foothold in capturing the growing orbital services market. Lunar Data Services is designed to allow it to provide lunar network services to National Aeronautics and Space Administration and commercial clients. Space Products and Infrastructure includes propulsion systems, navigation systems, engineering services contracts, lunar mobility vehicles, power infrastructure, and human habitation systems.
Graphene has properties almost as if out of a picture book. It is simultaneously strong and flexible, it permeates water but filters gases, it is transparent like glass but at the same time conducts current better than a metal – and on top of it all, it is as thin as can be. Graphene consists just of a single layer of atoms. No surprise that interest in this wonder material (Nobel Prize 2010) explodes. Unfortunately, graphene displays all of these properties only when it is essentially defect free, and this is exactly what limits commercial production today.
Recently, highest-quality graphene films could be produced at copper catalysts. The gist was to melt the copper above 1100° degrees, so that the film growth occurs above the liquid metal surface. Yet, why these extreme conditions favor the formation of defect-free graphene is largely unknown. Not least the high temperatures and gas flows during growth hamper gaining the necessary atomic-level insights. Much hope is therefore placed on methods of AI/machine learning that can accelerate highest-accuracy quantum mechanical predictions to a level that allows so-called molecular dynamics simulations to correctly account for the high mobility of atoms at the liquid copper surface. Up to now, there is very little experience as to the reliability of these AI approaches though.
As an important benchmark, scientists at the Fritz Haber Institute (FHI) predicted the height of a growing graphene layer above the liquid copper catalyst using these novel approaches, and compared it against measurements from high-level synchrotron experiments achieved within a European consortium. The result is striking. The machines were right down to 10-11 m, or within less than one millionth the width of a hair. This high precision, called “sub-Ångstrøm“ in the jargon of the field, demonstrates the impressive capabilities of the novel AI approaches to acquire detailed insights into the microscopic growth process. “Our results nicely show the unprecedented possibilities”, summarizes the leader of the FHI-team Dr. Heenen proudly. “Intriguingly, the data on adsorption height suggest an essentially identical chemical interaction of graphene with solid and liquid copper. This renders the superior synthesis above the liquid metal surface even more mysterious.”
The Hubble Space Telescope identified its most distant single star to date, whose light has taken 12.9 billion years to reach Earth.
The most distant single star seen yet dates back to less than 1 billion years after the universe's birth in the Big Bang, and may shed light on the earliest stars in the cosmos, a new study finds.
The scientists nicknamed the star "Earendel," from an Old English word meaning "morning star" or "rising light." Earendel, whose technical designation is WHL0137-LS, is at least 50 times the mass of the sun and millions of times as bright.
This newfound star, detected by NASA's Hubble Space Telescope, is so far away that its light has taken 12.9 billion years to reach Earth, appearing to us as it was when the universe was about 900 million years old, just 7% of its current age. Until now, the most distant single star detected, discovered by Hubble in 2018, existed when the universe was about 4 billion years old, or 30% of its current age.
Normally, even a star as brilliant as Earendel would be impossible to see from Earth given the vast divide between the two. Previously, the smallest objects seen at such a great distance were clusters of stars embedded inside early galaxies.
Scientists detected Earendel with the help of a huge galaxy cluster, WHL0137-08, sitting between Earth and the newfound star. The gravitational pull of this enormous galaxy cluster warped the fabric of space and time, resulting in a powerful natural magnifying glass that greatly amplified the light from distant objects behind the galaxy, such as Earendel. This gravitational lensing has distorted the light from the galaxy hosting Earendel into a long crescent the researchers named the Sunrise Arc.
The rare way in which Earendel aligned with WHL0137-08 meant that the star appeared directly on, or extremely close to, a curve in spacetime that provided maximum brightening, causing Earendel to stand out from the general glow of its home galaxy. This effect is analogous to the rippled surface of a swimming pool creating patterns of bright light on the bottom of the pool on a sunny day — the ripples on the surface act as lenses and focus sunlight to maximum brightness on the pool floor. Welch emphasized this is not the most distant object scientists have ever discovered. "Hubble has observed galaxies at greater distances," he explained. "However, we see the light from their millions of stars all blended together. This is the most distant object where we can identify light from an individual star."
He also noted this star was distant, but not old. "We see the star as it was 12.8 billion years ago, but that does not mean the star is 12.8 billion years old," Welch said. Instead, it's probably just a few million years old and never reached old age. "Given its mass, it almost certainly has not survived to today, as more massive stars tend to burn through their fuel faster and thus explode, or collapse into black holes, sooner," he added of Earendel. "The oldest stars known would have formed at a similar time, but they are much less massive, so they have continued to shine until today."
Many details about Earendel remain uncertain, such as its mass, brightness, temperature and type. Scientists are not even sure yet if Earendel is one star or two — most stars of Earendel's mass usually do have a smaller, dimmer companion, and it's possible that Earendel is outshining its partner. Scientists intend to conduct followup observations with NASA's recently launched James Webb Space Telescope to analyze Earendel's infrared light and pin down many of its features. Such information in turn could help shed light on the first stars in the universe, which formed before the universe was filled with the heavy elements produced by successive generations of massive stars.
The exponential accumulation of digital data is expected to outstrip magnetic and optical storage media.
DNA is an incredibly dense (up to 455 exabytes per gram, 6 orders of magnitude denser than magnetic or optical media) and stable (readable over millennia) digital storage medium (1–3). Storage and retrieval of up to gigabytes of digital information in the form of text, images, and movies have been successfully demonstrated. DNA’s essential biological role ensures that the technology for manipulating DNA will never succumb to obsolescence. However, performing computation on the stored data typically involves sequencing the DNA, electronically computing the desired transformation, and synthesizing new DNA. This expensive and slow loop limits the applicability of DNA storage to rarely accessed data (cold storage).
In contrast to traditional (passive) DNA storage, schemes for dynamic DNA storage allow access and modification of data without sequencing and/or synthesis. Upon binding to molecular probes, files can be accessed selectively (4) and modified through PCR amplification (5). Introducing or inhibiting binding of molecular probes with existing data barcodes can rename or delete files (6). Information encoded in the hybridization pattern of DNA can be written and erased (7) and can even undergo basic logic operations such as AND and OR (8) using strand displacement. By encoding information in the nicks of naturally occurring DNA [a.k.a. native DNA (9)], data can be modified through ligation or cleavage (10). With image similarities encoded in the binding affinities of DNA query probes and data, similarity searches on databases can be performed through DNA hybridization (11, 12). Although these advances allow information to be directly accessed and edited within the storage medium, they nevertheless demonstrate limited or no capacity for computation in DNA.
Conveniently, beyond its role as a storage medium, DNA has proved to be a programmable medium for computation, primarily via “strand displacement” reactions. With the understanding of the kinetics and thermodynamics of DNA strand displacement (13–15), a variety of rationally designed molecular computing devices have been engineered. These include molecular implementations of logic circuits (16–18), neural networks (19, 20), consensus algorithms (21), dynamical systems including oscillators (22), and cargo-sorting robots (23). Given the achievements of strand displacement systems and their inherent molecular parallelism, DNA computation schemes appear well suited to directly carry out computation on big data stored in DNA.
A research team now proposes a paradigm called SIMD||||DNA* (Single Instruction Multiple Data† computation with DNA) which integrates DNA storage with in-memory computation by strand displacement. A preliminary version of the theoretical results appeared as a conference paper (25). Inspired by methods of storing information in the positions of nicks in double-stranded DNA (9, 10), SIMD||||DNA encodes information in a multi-stranded DNA complex with a unique pattern of nicks and exposed single-stranded regions (a register). Although storage density is somewhat reduced (approximately a factor of 30), encoding information in nicks still achieves orders of magnitude higher density than magnetic and optical technologies. To manipulate information, an instruction (a set of strands) is applied in parallel to all registers which all share the same sequence space. The strand composition of a register changes when the applied instruction strands trigger strand displacement reactions within that register. Non-reacted instruction strands and reaction waste products are washed away via magnetic bead separation to prepare for the next instruction. Each instruction can change every bit on every register, yielding a high level of parallelism. These experiments routinely manipulated 10^101010 registers in parallel. DNA storage recovery studies suggest that, in principle, 10^8108 distinct register values can be stored in one such sample.
Scientists are training a gargantuan one-trillion-parameter generative AI system dubbed 'ScienceGPT' based on scientific data from the newly established Aurora supercomputer.
The AuroraGPT AI model, which is being trained by researchers at the Argonne National Lab (ALN) in Illinois, USA, is powered by Intel's Ponte Vecchio GPUs which provide the main computing power, and is being backed by the US government. Training could take months to complete, according to HPC Wire, with training currently limited to 256 of the roughly 10,000 nodes of the Aurora supercomputer, before this is scaled up over time. Even given this limitation, Intel and ANL are only testing the model training on a string of 64 nodes, with caution due to Aurora's unique design as a supercomputer.
At one trillion parameters, ScienceGPT will be one of the largest LLMs out there. While it won't quite hit the size of the reported 1.7-trillion-parameter GPT-4, developed by OpenAI, it'll be almost twice as large as the 560-billion-parameter Pathways Language Model, which powers Google's Bard. “It combines all the text, codes, specific scientific results, papers, into the model that science can use to speed up research,” said Ogi Brkic, vice president and general manager for data center and HPC solutions, in a press briefing. It'll operate like ChatGPT, but it's yet unclear at the moment whether it will be multimodal, in that it will generate different kinds of media like text, images, and video.
Aurora – which will be the second exascale supercomputer in US history – has just established itself on the Top500 list of the most powerful supercomputers after years of being developed. It's the second-most powerful supercomputer after Froniter, and is powered by 60,000 Intel GPUs while boating 10,000 computing nodes over 166 racks, alongside more than 80,000 networking nodes. It is still being finished, however, and will likely exceed Frontier's performance when it's fully up to speed, and all testing and finetuning is complete, said Top500.
The landmark decision could transform the treatment of sickle-cell disease and β-thalassaemia — but the technology is expensive.
In a world first, the UK medicines regulator has approved a therapy that uses the CRISPR–Cas9 gene-editing tool as a treatment. The decision marks another high point for a biotechnology that has been lauded as revolutionary in the decade since its discovery.
The therapy, called Casgevy, will treat the blood conditions sickle-cell disease and β-thalassaemia. Sickle-cell disease, also known as sickle-cell anaemia, can cause debilitating pain, and people with β-thalassaemia often require regular blood transfusions.
“This is a landmark approval which opens the door for further applications of CRISPR therapies in the future for the potential cure of many genetic diseases,” said Kay Davies, a geneticist at the University of Oxford, UK, in comments to the UK Science Media Centre (SMC).
Nature magazine explains the research behind the treatment and explores what’s next.
What research led to the approval?
The approval by the Medicines and Healthcare products Regulatory Agency (MHRA) follows promising results from clinical trials that tested a one-time treatment, which is administered by intravenous infusion. The therapy was developed by the pharmaceutical company Vertex Pharmaceuticals in Boston, Massachusetts, and biotechnology company CRISPR Therapeutics in Zug, Switzerland.
The trial for sickle-cell disease has followed 29 out of 45 participants long enough to draw interim results. Casgevy completely relieved 28 of those people of debilitating episodes of pain for at least one year after treatment. Researchers also tested the treatment for a severe form of β-thalassaemia, which is conventionally treated with blood transfusions roughly once a month. In this trial, 54 people received Casgevy, of which 42 participated for long enough to provide interim results. Among those 42 participants, 39 did not need a red-blood-cell transfusion for at least one year. The remaining three had their need for blood transfusions reduced by more than a 70%.
Proving geometric theorems constitutes a hallmark of visual reasoning combining both intuitive and logical skills. Therefore, automated theorem proving of Olympiad-level geometry problems is considered a notable milestone in human-level automated reasoning. The introduction of AlphaGeometry, a neuro-symbolic model trained with 100 million synthetic samples, marked a major breakthrough. It solved 25 of 30 International Mathematical Olympiad (IMO) problems whereas the reported baseline based on Wu’s method solved only ten.
In this paper, the IMO-AG-30 Challenge introduced with AlphaGeometry was revisited, and the researchers found that Wu’s method is surprisingly strong. Wu’s method alone can solve 15 problems, and some of them are not solved by any of the other methods. This leads to two key findings: (i) Combining Wu’s method with the classic synthetic methods of deductive databases and angle, ratio, and distance chasing solves 21 out of 30 methods by just using a CPU-only laptop with a time limit of 5 minutes per problem. Essentially, this classic method solves just 4 problems less than AlphaGeometry and establishes the first fully symbolic baseline, strong enough to rival the performance of an IMO silver medalist. (ii) Wu’s method even solves 2 of the 5 problems that AlphaGeometry failed to solve. Thus, by combining AlphaGeometry with Wu’s method a new state-of-the-art for automated theorem proving on IMO-AG-30, solving 27 out of 30 problems, the first AI method which outperforms an IMO gold medalist is finally achieved.
The COVID-19 pandemic was initiated by the rapid spread of a SARS-CoV-2 strain. Though mainly classified as a respiratory disease, SARS-CoV-2 infects multiple tissues throughout the human body, leading to a wide range of symptoms in patients. To better understand how SARS-CoV-2 affects the proteome from cells with different ontologies, this work generated an infectome atlas of 9 cell models, including cells from brain, blood, digestive system, and adipocyte tissue.
Our data shows that SARS-CoV-2 infection mainly trigger dysregulations on proteins related to cellular structure and energy metabolism. Despite these pivotal processes, heterogeneity of infection was also observed, highlighting many proteins and pathways uniquely dysregulated in one cell type or ontological group. These data have been made searchable online via a tool that will permit future submissions of proteomic data (https://reisdeoliveira.shinyapps.io/Infectome_App/) to enrich and expand this knowledgebase.
NVIDIA recently announced GeForce RTX™ SUPER desktop GPUs for supercharged generative AI performance, new AI laptops from every top manufacturer, and new NVIDIA RTX™-accelerated AI software and tools for both developers and consumers.
Building on decades of PC leadership, with over 100 million of its RTX GPUs driving the AI PC era, NVIDIA is now offering these tools to enhance PC experiences with generative AI: NVIDIA TensorRT™ acceleration of the popular Stable Diffusion XL model for text-to-image workflows, NVIDIA RTX Remix with generative AI texture tools, NVIDIA ACE microservices and more games that use DLSS 3 technology with Frame Generation.
AI Workbench, a unified, easy-to-use toolkit for AI developers, will be available in beta later this month. In addition, NVIDIA TensorRT-LLM (TRT-LLM), an open-source library that accelerates and optimizes inference performance of the latest large language models (LLMs), now supports more pre-optimized models for PCs. Accelerated by TRT-LLM, Chat with RTX, an NVIDIA tech demo also releasing this month, allows AI enthusiasts to interact with their notes, documents and other content.
“Generative AI is the single most significant platform transition in computing history and will transform every industry, including gaming,” said Jensen Huang, founder and CEO of NVIDIA. “With over 100 million RTX AI PCs and workstations, NVIDIA is a massive installed base for developers and gamers to enjoy the magic of generative AI.”
Running generative AI locally on a PC is critical for privacy, latency and cost-sensitive applications. It requires a large installed base of AI-ready systems, as well as the right developer tools to tune and optimize AI models for the PC platform. To meet these needs, NVIDIA is delivering innovations across its full technology stack, driving new experiences and building on the 500+ AI-enabled PC applications and games already accelerated by NVIDIA RTX technology.
This is NVIDIA's first and very important step towards the vision of "LLM as Operating System" - a locally running, heavily optimized AI assistant that can deeply integrate with all your local files, but at the same time preserving privacy. NVIDIA is going local even before OpenAI!
Retroviruses are viruses that multiply by incorporating their genes into the genome of a host cell. If the infected cell is a germ cell, the retrovirus can then be passed on to the next generation as an “endogenous” retrovirus (ERV) and spread as part of the host genome in that host species. In vertebrates, ERVs are ubiquitous and sometimes make up 10 per cent of the host genome. However, most retrovirus integrations are very old, already degraded and therefore inactive – their initial impact on host health has been minimized by millions of years of evolution. A research team led by the Leibniz Institute for Zoo and Wildlife Research (Leibniz-IZW) has now discovered a recent case of retrovirus colonization in a rodent from New Guinea, the white-bellied mosaic-tailed rat. In a PNAS paper they describe this new model of virus integration. The observations on this process will help to improve our understanding how retroviruses rewrite host genomes.
Retroviruses, such as the pathogen responsible for AIDS (HIV-1), integrate into the genome of the host cells they infect during their life cycle. When this happens in the germline (egg cells or cells that produce sperm) of the host, the retrovirus can actually become a gene of the host itself. This process is apparently common, as up to 10 percent of the genomes of most vertebrates consist of the remnants of such ancient infections. One of the best studied models of this process is the koala retrovirus (KoRV), which is currently colonizing the koala genome. „What happens to the virus and the host during this process of genome colonization we do not know, as most such events occurred millions of years ago and we only see the leftover ‘fossils’ of the retrovirus”, says Prof Alex Greenwood, head of the Leibniz-IZW Department of Wildlife Diseases. “Nor do we know what the host suffered health-wise during the infection process. The koala retrovirus (KoRV) is one of the few models of this process that occurs in real time and where we can observe the effects of genome colonization on the host animal.”
There is now some evidence that KoRV-related viruses are circulating in rodents and bats in Papua New Guinea and Indonesia. A group led by Greenwood and Dr Saba Mottaghinia, former PhD student in Greenwood’s department, analyzed 278 samples from seven bat and one rodent family endemic to Australia and New Guinea. They discovered a retrovirus that is currently colonizing the genome of an endemic rodent from New Guinea, the white-bellied mosaic-tailed rat (Melomys leucogaster). This is only the second example from the Australo-Papuan region, after KoRV, of a retrovirus that has colonized a genome while retaining a functional viral life cycle. The gibbon ape leukaemia viruses (GALV), a group of viruses discovered in gibbons and woolly monkeys at a research facility in Thailand in the 1960s, are very closely related to KoRV. This is a curious and surprising relationship, as there is a geographical barrier, known as the Wallace lineage, which separates the fauna of Southeast Asia from Indonesia, Papua New Guinea and Australia's fauna. However, there is evidence that the gibbons and woolly monkeys at the research facility in Thailand have been infected with viruses from Papua New Guinea. „The discovery of GALV-like viruses in rodents and bats in Indonesian and Australian rodents and bats from New Guinea suggests that these viruses, and possibly also KoRV, originated in New Guinea”, says Greenwood, who initiated the research project funded by the German Research Foundation.
The Leibniz-IZW team, together with scientists from the Charité, the Robert Koch Institute, the Max Delbrück Center, the University of Nicosia, California State University Fullerton, the South Australian Museum and Museum Victoria, examined 278 bat and rodent samples from Australia and New Guinea for KoRV and GALV-like viruses. They detected a GALV, the Woolly Monkey Virus (WMV) in a population of the white-bellied mosaic-tailed rat, an endemic rodent from New Guinea. In five of the rats from two New Guinea collection sites, WMV was integrated into the genome at the same location, indicating that it has spread as a gene and not by infection, i.e. it has become part of the genome. However, in other white-bellied mosaic-tailed rat populations the virus was absent – similar to KoRV in koalas, where all koalas in northern Australia have KoRV in their genome, whereas there are koalas in southern Australia that do not have intact KoRV. The virus, now called the “complete Melomys woolly monkey virus” (cMWMV), was able to infect cell lines, produce new viral progeny, was visible by electron microscopy as viral particles that detached from the cell membrane, and was even sensitive to the antiretroviral drug AZT.
„The virus has all the characteristics of an exogenous infectious retrovirus, but is endogenous. It is probably a very recent colonization event, much younger than KoRV", says Dr Saba Mottaghinia, the lead author of the paper. The results suggest that cMWMV is a new model for retroviral colonization of the host genome that occurs in real time, as in KoRV, and they also suggest that GALVs like WMV originated in the diverse fauna of New Guinea. The discoveries in New Guinea have certainly not been exhausted. “There are hundreds of mammalian species from this region that have not yet been studied, suggesting that many more viruses and models exist in this region”, says Greenwood.
The first surgery demonstration in space performed by a robot — controlled by surgeons on Earth — has marked a technological milestone for long-term spaceflight.
The robot, known as spaceMIRA — which stands for Miniaturized In Vivo Robotic Assistant — performed several operations on simulated tissue at the orbiting laboratory while remotely operated by surgeons from approximately 250 miles (400 kilometers) below in Lincoln, Nebraska.
The milestone is a step forward in developing technology that could have implications not just for successful long-term human space travel, where surgical emergencies could happen, but also for establishing access to medical care in remote areas on Earth.
The interior of black holes remains a conundrum for science. In 1916, German physicist Karl Schwarzschild outlined a solution to Albert Einstein's equations of general relativity, in which the center of a black hole consists of a so-called singularity, a point at which space and time no longer exist. Here, the theory goes, all physical laws, including Einstein's general theory of relativity, no longer apply; the principle of causality is suspended.
This constitutes a great nuisance for science—after all, it means that no information can escape from a black hole beyond the so-called event horizon. This could be a reason why Schwarzschild's solution did not attract much attention outside the theoretical realm—that is, until the first candidate for a black hole was discovered in 1971, followed by the discovery of the black hole in the center of our Milky Way in the 2000s, and finally the first image of a black hole, captured by the Event Horizon Telescope Collaboration in 2019.
In 2001, Pawel Mazur and Emil Mottola proposed a different solution to Einstein's field equations that led to objects that they called gravitational condensate stars, or gravastars. Contrary to black holes, gravastars have several advantages from a theoretical astrophysics perspective.
On the one hand, they are almost as compact as black holes and also exhibit a gravity at their surface that is essentially as strong as that of a black hole, hence resembling a black hole for all practical purposes. On the other hand, gravastars do not have an event horizon, that is, a boundary from within which no information can be sent out, and their core does not contain a singularity.
Instead, the center of a gravastar is made up of an exotic (dark) energy that exerts a negative pressure to the enormous gravitational force compressing the star. The surface of a gravastar is represented by a wafer-thin skin of ordinary matter, the thickness of which approaches zero.
Theoretical physicists Daniel Jampolski and Prof. Luciano Rezzolla of Goethe University Frankfurt have now presented a solution to the field equations of general relativity that describes the existence of a gravastar inside another gravastar. They have given this hypothetical celestial object the name "nestar" (from the English "nested"). The study is published in Classical and Quantum Gravity.
As OpenAI and other model developers build more capable AI systems, the potential for both beneficial and harmful uses of AI will grow. One potentially harmful use, highlighted by researchers and policymakers, is the ability for AI systems to assist malicious actors in creating biological threats (e.g., see White House 2023, Lovelace 2022, Sandbrink 2023). In one discussed hypothetical example, a malicious actor might use a highly-capable model to develop a step-by-step protocol, troubleshoot wet-lab procedures, or even autonomously execute steps of the biothreat creation process when given access to tools like cloud labs (see Carter et al., 2023). However, assessing the viability of such hypothetical examples was limited by insufficient evaluations and data.
Following OpenAI's recently shared Preparedness Framework, they are developing methodologies to empirically evaluate these types of risks, in order to understand both where AI models are today and where they might be in the future. Now, OpenAI details a new evaluation which could help serve as one potential “tripwire” signaling the need for caution and further testing of biological misuse potential. This evaluation aims to measure whether models could meaningfully increase malicious actors’ access to dangerous information about biological threat creation, compared to the baseline of existing resources (i.e., the internet).
To evaluate this, OpenAI conducted a study with 100 human participants, comprising (a) 50 biology experts with PhDs and professional wet lab experience and (b) 50 student-level participants, with at least one university-level course in biology. Each group of participants was randomly assigned to either a control group, which only had access to the internet, or a treatment group, which had access to GPT-4 in addition to the internet. Each participant was then asked to complete a set of tasks covering aspects of the end-to-end process for biological threat creation.A[A]
Findings:
This study assessed uplifts in performance for participants with access to GPT-4 across five metrics (accuracy, completeness, innovation, time taken, and self-rated difficulty) and five stages in the biological threat creation process (ideation, acquisition, magnification, formulation, and release). They found mild uplifts in accuracy and completeness for those with access to the language model. Specifically, on a 10-point scale measuring accuracy of responses, they observed a mean score increase of 0.88 for experts and 0.25 for students compared to the internet-only baseline, and similar uplifts for completeness (0.82 for experts and 0.41 for students). However, the obtained effect sizes were not large enough to be statistically significant, and this study highlighted the need for more research around what performance thresholds indicate a meaningful increase in risk. Moreover, OpenAI notes that information access alone is insufficient to create a biological threat, and that this evaluation does not test for success in the physical construction of the threats.
An experimental gene therapy has restored the hearing of a child who was born deaf, the pharmaceutical giant Eli Lilly said in a statement. The child, who was 11 years old at the time the therapy was administered, experienced restored hearing within 30 days of treatment, Eli Lilly said in a release. The child participating in the clinical study was the first individual in the U.S. to receive the therapy for a genetic form of hearing loss, the company said.
The gene therapy, AK-OTOF, is being developed for the treatment of hearing loss due to mutations in the otoferlin gene. added AK-OTOF to its portfolio through its , a genetic-medicine company focused on inner-ear conditions.
Doctors and scientists have been pursuing gene therapy for hearing loss for more than 20 years, and “these initial results show that it may restore hearing better than many thought possible,” Dr. John Germiller, director of clinical research in the otolaryngology department at the Children’s Hospital of Philadelphia and principal investigator in the clinical trial, said in a statement.
The child’s hearing was restored across all tested frequencies and was within a normal range at some frequencies at 30 days after the treatment, Eli Lilly said.
Children with hearing problems due to otoferlin gene mutations are often born with profound hearing loss, but most of them have not had the genetic testing needed for a definitive diagnosis, Dr. Oliver Haag, head of otolaryngology at Sant Joan de Deu Hospital in Barcelona and an Akouos study investigator, said in a statement.
In observations of the planets Kepler-1625b and Kepler-1708b from the Kepler and Hubble space telescopes, researchers had discovered traces of such moons for the first time. A new study now raises doubts about these previous claims. As scientists from the Max Planck Institute for Solar System Research and the Sonnenberg Observatory, both in Germany, report in the journal Nature Astronomy, "planet-only" interpretations of the observations are more conclusive. For their analysis, the researchers used their newly developed computer algorithm Pandora, which facilitates and accelerates the search for exomoons. They also investigated what kind of exomoons can be found in principle in modern space-based astronomical observations. Their answer is quite shocking.
In our Solar System, the fact that a planet is orbited by one or more moons is rather the rule than the exception: apart from Mercury and Venus, all other planets have such companions; in the case of the gas giant Saturn researchers have found 140 natural satellites until today. Scientists therefore consider it likely that planets in distant star systems also harbor moons. So far, however, there has only been evidence of such exomoons in two cases: Kepler-1625b and Kepler-1708b. This low yield is not surprising. After all, distant satellites are naturally much smaller than their home worlds - and therefore much harder to find. And it is extremely time-consuming to comb through the observational data of thousands of exoplanets for evidence of moons.
To make the search easier and faster, the authors of the new study rely on a search algorithm they developed and optimized themselves for the search for exomoons.
They published their method last year and the algorithm is available to all researchers as open source code. When applied to the observational data from Kepler-1625b and Kepler-1708b, the results were astonishing. "We would have liked to confirm the discovery of exomoons around Kepler-1625b and Kepler-1708b," says first author of the new study, MPS scientist Dr. René Heller. "But unfortunately, our analyses show otherwise," he adds.
At age 45, Dr. Lakiea Bailey said, she was the oldest person with sickle cell anemia that she knew. The executive director of the nonprofit patient advocacy group the Sickle Cell Consortium was diagnosed with sickle cell disease at age 3. Because of it, she’s had heart problems, had her hips replaced, and experienced serious pain all her life. Bailey told the US Food and Drug Administration’s independent advisory committee that she believes a cutting-edge therapy that is currently under review offers the sickle cell community something many haven’t ever had before: hope. “Hope is on the horizon, and we are looking toward this hope for a change of the lives that we are living of excruciating pain,” Bailey told the FDA committee.
The independent committee is helping the FDA think through how it should evaluate a treatment called exa-cel that could potentially cure people of sickle cell disease, a painful and deadly disease with no universally successful treatment. This was an ongoing discussion with no vote or decision about the therapy, but the discussion likely moves the US one step closer to approving a groundbreaking new treatment that uses gene editing. If approved, exa-cel, made by Boston-based Vertex Pharmaceuticals and the Swiss company CRISPR Therapeutics, would be the first FDA-approved treatment that uses genetic modification called CRISPR. CRISPR, or clustered regularly interspaced short palindromic repeats, is a technology researchers use to selectively modify DNA, the carrier of genetic information that the body uses to function and develop. The FDA said treatment for severe sickle cell is an “unmet medical need.”
When someone has sickle cell disease their red blood cells don’t function the way they should. Red blood cells are the helper cells that carry oxygen from the lungs to the body’s tissues, which use this oxygen to produce energy. The process also generates waste in the form of carbon dioxide that the red blood cells take to the lungs to be exhaled out.
With sickle cell disease — also called sickle cell anemia — red blood cells take on a folded or sickle shape that can clog tiny blood vessels and cause progressive organ damage and pain, and can lead to organ failure. The sickle cells also die earlier than they should, which means the person is constantly short red blood cells. One person with sickle cell who testified said she had been hospitalized 100 times just last year. Median life expectancy is only 45 years. Sickle cell is rare, and it disproportionately impacts African Americans. About 100,000 people in the US are diagnosed with sickle cell and, of those, 20,000 have what’s considered severe disease. Up until now, the only real treatment has been a stem cell or bone marrow transplant. For stem cells, fewer than 20% of patients have an appropriately matched donor, the FDA said, and the transplants are risky and may not work. Sometimes a transplant can kill the patient. The new exa-cel treatment under FDA consideration can use the patient’s own stem cells. Doctors would alter them with CRISPR to fix the genetic problems that cause sickle cell, and then the altered stem cells are given back to the patient in a one-time infusion. In company studies, the treatment was considered safe, and it had a “highly positive benefit-risk for patients with severe sickle cell disease,” Dr. Stephanie Krogmeier, vice president for global regulatory affairs with Vertex Pharmaceuticals Incorporated, told the panel. Thirty-nine of the 40 people tested with the treatment did not have a single vaso-occlusive crisis, which means the misshapen red blood cells block normal circulation and can cause moderate to severe pain. It’s the top reason patients with sickle cell go to the emergency room or are hospitalized. Before the treatment, patients experienced about four of these painful crises a year, resulting in about two weeks in the hospital. The FDA sought the independent panel’s advice, in part, becausethis would be the first time the FDA would approve a treatment that uses CRISPR technology, but Dr. Fyodor Urnov, a professor in the Department of Molecular and Cell Biology at the University of California, Berkeley, reminded the committee CRISPR has been around for 30 years and, in that time, scientists have learned a lot about how to use it safely. “The technology is, in fact, ready for primetime,” Urnov said. With this kind of genetic editing, scientists could inadvertently make a change to a patient’s DNA that is off-target, and the therapy could harm the patient. The FDA wanted the experts’ advice so it could understand what criteria it should use to evaluate the treatment and determine how to evaluate long-term safety issues.
An FDA presentation to the panel suggested the agency may have some questions about the data. It called a lack of confirmatory testing “concerning.” It also noted the study’s small size. In a discussion of the company’s methodology, several panel experts said that they believed the data the companies have submitted for FDA approval were reasonable. Committee member Dr. Gil Wolfe,adistinguished professor in the department of neurology at University at Buffalo Jacobs School of Medicine and Biomedical Sciences, said he liked that the company promised to monitor patients for 15 years to see if there were any problems down the road. He said, generally, it was “exciting” to see how many patients have been treated and how positive the results have been so far. As far as any concern for what’s called “off-target effects,” meaning the potential unwanted or adverse alterations to the genome that could accidentally happen in this process and cause cancer or other problems down the road, Dr. Daniel Bauer, principal investigator and staff physician at Dana-Farber/Boston Children’s Cancer and Blood Disorders Center, Boston Children’s Hospital, told the panel the risk is “relatively small.” Wolfe thought the depth of analysis the companies used should be good enough to detect any potential problems down the road. “We want to be careful to not let the perfect be the enemy of the good,” Wolfe said. “At some point, you have to just try things out.” “I think, in this case, that there’s a huge unmet need for individuals with sickle cell disease, and it’s important we think about how we can advance therapies that could potentially help them, and I certainly think this is one of them,” Wolfe added. Asked by the committee how he would advise patients how to evaluate the risks with this treatment, Bauer said he would be honest that there is some uncertainty, but most of the human genome is non-coding, meaning it doesn’t provide instructions to the cells to act a certain way. “It might be that many places in the human genome can tolerate an off-target edit and not have a functional consequence,” Bauer told the committee. In other words, if they made an editing error, it might not matter, and may not harm the patient. “My guess is it’s a relatively small risk in the scheme of this risk benefit. But it’s new, it’s unknown,” Bauer said. “We need to be humble and open to learning from these brave patients participating.” The FDA is expected to make an approval decision by December 8, 2023.
Scientists found the cells in mice — and say they could lead to a better understanding of human appetite.
Brain cells that control how quickly mice eat, and when they stop, have been identified. The findings, published in Nature1, could lead to a better understanding of human appetite, the researchers say.
Nerves in the gut, called vagal nerves, had already been shown to sense how much mice have eaten and what nutrients they have consumed2. The vagal nerves use electrical signals to pass this information to a small region in the brainstem that is thought to influence when mice, and humans, stop eating. This region, called the caudal nucleus of the solitary tract, contains prolactin-releasing hormone neurons (PRLH) and GCG neurons. But, until now, studies have involved filling the guts of anaesthetized mice with liquid food, making it unclear how these neurons regulate appetite when mice are awake.
To answer this question, physiologist Zachary Knight at the University of California, San Francisco, and his colleagues implanted a light sensor in the brains of mice that had been genetically modified so that the PRLH neurons released a fluorescent signal when activated by electrical signals transmitted along neurons from elsewhere in the body. Knight and his team infused a liquid food called Ensure — which contains a mixture of fat, protein, sugar, vitamins and minerals — into the guts of these mice. Over a ten-minute period, the neurons became increasingly activated as more of the food was infused. This activity peaked a few minutes after the infusion ended. By contrast, the PRLH neurons did not activate when the team infused saline solution into the mice’s guts.
When the team allowed the mice to freely eat liquid food, the PRLH neurons activated within seconds of the animals starting to lick the food, but deactivated when they stopped licking. This showed that PRLH neurons respond differently, depending on whether signals are coming from the mouth or the gut, and suggests that signals from the mouth override those from the gut, says Knight. By using a laser to activate PRLH neurons in mice that were eating freely, the researchers could reduce how quickly the mice ate.
Further experiments showed that PRLH neurons did not activate during feeding in mice that lacked most of their ability to taste sweetness, suggesting that taste activated the neurons. The researchers also found that GCG neurons are activated by signals from the gut, and control when mice stop eating. “The signals from the mouth are controlling how fast you eat, and the signals from the gut are controlling how much you eat,” says Knight.
“I’m extremely impressed by this paper,” says neuroscientist Chen Ran at Harvard University in Boston, Massachusetts. The work provides original insights on how taste regulates appetite, he says. The findings probably apply to humans, too, Ran adds, because these neural circuits tend to be well conserved across both species.
In Tokyo, engineer Moju Zhao has developed a transformative drone, named DRAGON, that can change its shape mid-flight to navigate tight spaces. The acronym DRAGON stands for "Dual-rotor embedded multilink Robot with the Ability of multi-deGree-of-freedom aerial transformatiON."
Made of multiple modules connected by battery-powered hinged joints, the drone can autonomously decide on its shape formations. Currently, it can stay airborne for three minutes, but the team aims to add more modules and grippers for object manipulation. The long-term vision is for the DRAGON to transition between flying and walking. Its design, reminiscent of serpentine shapes, is inspired by ancient Asian dragon mythology. This aligns with Japan's futuristic drone roadmap which encompasses applications in delivery, farming, and unique solutions such as drone-powered parasols. Meanwhile, MIT has developed insect-sized drones that use soft actuators mimicking the agility of bugs.
These micro-drones are resilient, capable of withstanding collisions, and even perform aerial somersaults. The drones have potential applications ranging from pollinating crops and inspecting machinery to search and rescue missions. The soft actuators are made of thin rubber cylinders coated in carbon nanotubes. When voltage is applied, an electrostatic force is produced, causing the rubber to expand and contract, resulting in wing flapping. MIT's fabrication process involves layering elastomer and electrode, each as thin as a red blood cell, leading to power-efficient and resilient drones.
AI's remarkable abilities, like those seen in ChatGPT, often seem conscious due to their human-like interactions.
The question is whether the language model also perceives our text when we prompt it. Or is it just a zombie, working based on clever pattern-matching algorithms? Based on the text it generates, it is easy to be swayed that the system might be conscious. However, in this new research, Jaan Aru, Matthew Larkum and Mac Shine take a neuroscientific angle to answer this question.
All three being neuroscientists, these authors argue that although the responses of systems like ChatGPT seem conscious, they are most likely not. First, the inputs to language models lack the embodied, embedded information content characteristic of our sensory contact with the world around us. Secondly, the architectures of present-day AI algorithms are missing key features of the thalamocortical system that have been linked to conscious awareness in mammals. Finally, the evolutionary and developmental trajectories that led to the emergence of living conscious organisms arguably have no parallels in artificial systems as envisioned today.
The existence of living organisms depends on their actions and their survival is intricately linked to multi-level cellular, inter-cellular, and organismal processes culminating in agency and consciousness. Thus, while it is tempting to assume that ChatGPT and similar systems might be conscious, this would severely underestimate the complexity of the neural mechanisms that generate consciousness in our brains.
Researchers do not have a consensus on how consciousness rises in our brains. What we know, and what this new paper points out, is that the mechanisms are likely way more complex than the mechanisms underlying current language models. For instance, as pointed out in this work, real neurons are not akin neurons in artificial neural networks. Biological neurons are real physical entities, which can grow and change shape, whereas neurons in large language models are just meaningless pieces of code. We still have a long way to understand consciousness and, hence, a long way to conscious machines.
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