- Balanophora genomes display massively convergent evolution with other extreme holoparasites and provide novel insights into parasite–host interactions Nature.com
- Parasitic plant convinces hosts to grow into its own flesh—it’s also an extreme example of genome shrinkage Phys.org
- These parasitic plants force their victims to make them dinner Popular Science
- Extreme parasitism: Balanophora convinces a host to grow into its tissue Earth.com
- Strikingly convergent genome alterations in two independently evolved holoparasites Nature.com
- View Full Coverage on Google News
Tag Archives: interactions
A new method to enable efficient interactions between photons
Photons, particles that represent a quantum of light, have shown great potential for the development of new quantum technologies. More specifically, physicists have been exploring the possibility of creating photonic qubits (quantum units of information) that can be transmitted over long distances using photons.
Despite some promising results, several obstacles still need to be overcome before photonic qubits can be successfully implemented on a large-scale. For instance, photons are known to be susceptible to propagation loss (i.e., a loss of energy, radiation, or signals as it travels from one point to another) and do not interact with one another.
Researchers at University of Copenhagen in Denmark, Instituto de Física Fundamental IFF-CSIC in Spain, and Ruhr-Universität Bochum in Germany have recently devised a strategy that could help to overcome one of these challenges, namely the lack of photon-photon interactions. Their method, presented in a paper published in Nature Physics, could eventually aid the development of more sophisticated quantum devices.
“We have been working on the deterministic interfacing of single quantum emitters (quantum dots) to single photons for over 15 years and have developed a very powerful method based on nanophotonic waveguides,” Peter Lodahl, one of the researchers who carried out the study, told Phys.org. “We generally applied these devices for deterministic single-photon sources and multi-photon entanglement sources, but another possible application would be to induce nonlinear operations on photons.”
Lodahl and his colleagues realized the first proof-of-concept demonstration of nonlinear operations using individual photons back in 2015. When they investigated this effect further, however, they encountered difficulties in thoroughly understanding the fundamental physics underlying this complex, single-photon and nonlinear interaction.
“In our previous work, we found that the physics governing the nonlinear interaction of pulses of light was remarkably rich and gave rise to some novel opportunities for constructing photonic quantum gates and photon sorters,” Lodahl said. “We have carried out the first experimental study of nonlinear quantum pulses undergoing nonlinear interaction due to the coupling with a deterministically coupled quantum emitter.”
In their new experiment, the researchers used the efficient and coherent coupling of a single quantum emitter with a nanophotonic waveguide to enable nonlinear quantum interactions between single-photon wave packets. To do this, they used a single quantum dot, a nm-sized particle that behaves like a two-level atom, which was embedded in a photonic crystal waveguide.
“In such systems, the coupling is deterministic, so that even one photon launched into the waveguide is interacting with the quantum dot,” Lodahl explained. “Sending in pulses containing two or more photons induce quantum correlations since only one photon at a time can interact with the quantum dot. By controlling the duration of the quantum pulse, we can tailor these correlations, and the interaction between the photons.”
Using their experimental method, Lodahl and his colleagues were essentially able to control a photon using a second photon, which was mediated by their quantum emitter. In other words, they successfully realized a nonlinear photon-photon interaction.
“We developed a method to get photons to efficiently interact with each other mediated by the coupling to quantum dots,” Lodahl said. “We think this could open new directions for making photon-photon quantum gates (which is the difficult gate in photonic quantum computing) or deterministic photon sorter devices that are essential, e.g., for quantum repeaters.”
The new strategy introduced by this team of researchers could have important implications for both quantum physics research and the development of quantum technology. For instance, their method could open new possibilities for the development of quantum optical devices, while also allowing physicists to experiment with tailored complex photonic quantum states.
“We have a range of activities that extend the present work,” Hanna Le Jeannic, another researcher involved in the study, told Phys.org. “On a fundamental level, we are looking at understanding more deeply how quantum states of light are affected by traveling through a single quantum dot. But we are also already foreseeing applications of this quantum interaction.”
At the moment, Lodahl, Le Jeannic and their colleagues are trying to exploit the nonlinear photon-photon interaction realized in their recent study to simulate the vibrational dynamics of molecules. This could be achieved by mapping the vibrational dynamics of complex molecules onto the propagation of photons in advanced photonic circuits.
Tailored single photons: Optical control of photons as the key to new technologies
More information:
Hanna Le Jeannic et al, Dynamical photon–photon interaction mediated by a quantum emitter, Nature Physics (2022). DOI: 10.1038/s41567-022-01720-x
Hanna Le Jeannic et al, Dynamical photon–photon interaction mediated by a quantum emitter, Nature Physics (2022). DOI: 10.1038/s41567-022-01720-x
Ravitej Uppu et al, Quantum-dot-based deterministic photon–emitter interfaces for scalable photonic quantum technology, Nature Nanotechnology (2021). DOI: 10.1038/s41565-021-00965-6
A. Javadi et al, Single-photon non-linear optics with a quantum dot in a waveguide, Nature Communications (2015). DOI: 10.1038/ncomms9655
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How Memory of Personal Interactions Declines With Age
Summary: Researchers have identified a new mechanism within neurons that cause memories associated with social interactions to decline with age. Additionally, they were able to reverse the memory loss in mouse models.
Source: University of Maryland
One of the most upsetting aspects of age-related memory decline is not being able to remember the face that accompanies the name of a person you just talked with hours earlier. While researchers don’t understand why this dysfunction occurs, a new study conducted at University of Maryland School of Medicine (UMSOM) has provided some important new clues.
The study was published on September 8 in Aging Cell.
Using aging mice, researchers have identified a new mechanism in neurons that causes memories associated with these social interactions to decline with age. In addition, they were able to reverse this memory loss in the lab.
The researchers report that their findings identified a specific target in the brain that may one day be used to develop therapies that could prevent or reverse memory loss due to typical aging. Aging memory problems are distinct from those caused by diseases like Alzheimer’s or dementia. At this time, there are no medications that can prevent or reverse cognitive decline due to typical aging.
“If an older adult attends a cocktail party, afterwards they would most likely recognize the names or the faces of the other attendees, but they might struggle with remembering which name went with which face,” said the study leader Michy Kelly, Ph.D., Associate Professor of Anatomy and Neurobiology at UMSOM.
These kinds of memories that associate multiple pieces of information within a personal interaction, so-called social associative memories, require an enzyme known as PDE11A in a part of the brain responsible for memory involving life experiences.
Last year, Dr. Kelly published research on PDE11A demonstrating that mice with genetically similar versions of the PDE11 enzyme were more likely to interact than those mice with a different type of PDE11A.
In this new study, Dr. Kelly and her team sought to determine PDE11A’s role in social associative memory in the aging brain and whether manipulating this enzyme could be used to prevent this memory loss.
Researchers can study mouse “social interactions” with their neighbors by seeing whether they will be willing to try a new food, based on their memories of encountering that food on the breath of another mouse.
Mice do not like to eat new foods to avoid getting sick or even dying. When they smell food on another mouse’s breath, mice make an association between the food odor and the smell of the other mouse’s pheromones, the memory of which serves as a safety signal that any food with that odor is safe to eat in the future.
Dr. Kelly and her colleagues found that although old mice could recognize both food odors and social odors separately, they were not able to remember the association between the two, similar to the cognitive decline in older people.
They also discovered that levels of PDE11A increased with age in both people and mice, specifically in a brain region responsible for many types of learning and memory known as the hippocampus.
This extra PDE11A in the hippocampus was not simply found where it was normally located in young mice; instead, it preferentially accumulated as little filaments in compartments of neurons.
The researchers wondered if having too much PDE11A in these filaments was why the older mice forgot their social associative memories and would no longer eat the safe food they smelled on another mouse’s breath. To answer this question, they prevented these age-related increases in PDE11A by genetically deleting the PDE11A gene in mice.
Without PDE11A, the older mice no longer forgot the social associative memory, meaning they ate the safe food smelled on another mouse’s breath. When the researchers added the PDE11A back into the hippocampus of these old mice, the mice once again forgot the social associative memory and would no longer eat the safe food.
One potential pathway to drug development to prevent this memory loss in people lies in an additional finding: The researchers learned that the concentrated filaments of PDE11A had an extra chemical modification in a specific place on the enzyme that the other PDE11 diffused throughout the neuron did not have. When they prevented this chemical modification, it reduced PDE11 levels and also prevented it from accumulating as filaments.
“PDE11 is involved in more things that just memory, including preferences for who you prefer to be around. So, if we are to develop a therapy to help with cognitive decline, we would not want to get rid of it entirely or it could cause other negative side effects,” said Dr. Kelly.
She and her colleagues joke that any drug that eliminated PDE11 would ensure you would remember your friends and family, but you might no longer like them.
“Thus, our goal is to figure out a way to target the bad form of PDE11A specifically, in order to not interfere with the normal, healthy function of the enzyme.”
Dean Mark T. Gladwin, MD, Executive Vice President for Medical Affairs, UM Baltimore, and the John Z. and Akiko K. Bowers Distinguished Professor at UMSOM, said, “We are at the tip of the iceberg when it comes to understanding how the brain ages, so it’s crucial to have basic research studies such as these to help us further our understanding and eventually find ways to prevent cognitive decline.”
Additional authors on the study include students Nicole Gorny, MS, and Siena Petrolle of UMSOM, as well as co-authors from the University of South Carolina.
See also
About this aging and memory research news
Author: Press Office
Source: University of Maryland
Contact: Press Office – University of Maryland
Image: The image is in the public domain
Original Research: Closed access.
“Conserved age‐related increases in hippocampal PDE11A4 cause unexpected proteinopathies and cognitive decline of social associative memories” by Katy Pilarzyk et al. Aging Cell
Abstract
Conserved age‐related increases in hippocampal PDE11A4 cause unexpected proteinopathies and cognitive decline of social associative memories
In humans, associative memories are more susceptible to age-related cognitive decline (ARCD) than are recognition memories. Reduced cAMP/cGMP signaling in the hippocampus may contribute to ARCD.
Here, we found that both aging and traumatic brain injury-associated dementia increased the expression of the cAMP/cGMP-degrading enzyme phosphodiesterase 11A (PDE11A) in the human hippocampus.
Further, age-related increases in hippocampal PDE11A4 mRNA and protein were conserved in mice, as was the increased vulnerability of associative versus recognition memories to ARCD. Interestingly, mouse PDE11A4 protein in the aged ventral hippocampus (VHIPP) ectopically accumulated in the membrane fraction and filamentous structures we term “ghost axons.”
These age-related increases in expression were driven by reduced exoribonuclease-mediated degradation of PDE11A mRNA and increased PDE11A4-pS117/pS124, the latter of which also drove the punctate accumulation of PDE11A4. In contrast, PDE11A4-pS162 caused dispersal.
Importantly, preventing age-related increases in PDE11 expression via genetic deletion protected mice from ARCD of short-term and remote long-term associative memory (aLTM) in the social transmission of food preference assay, albeit at the expense of recent aLTM.
Further, mimicking age-related overexpression of PDE11A4 in CA1 of old KO mice caused aging-like impairments in CREB function and remote social—but not non-social—LTMs. RNA sequencing and phosphoproteomic analyses of VHIPP identified cGMP-PKG—as opposed to cAMP-PKA—as well as circadian entrainment, glutamatergic/cholinergic synapses, calcium signaling, oxytocin, and retrograde endocannabinoid signaling as mechanisms by which PDE11A deletion protects against ARCD.
Together, these data suggest that PDE11A4 proteinopathies acutely impair signaling in the aged brain and contribute to ARCD of social memories.
Our Social Interactions Begin at a Young Age
Summary: Early social interactions allow children to learn quickly how to coordinate with each other’s behavior.
Source: NCCR
What do building pyramids, going to the moon, paddling a two-person canoe or dancing a waltz have in common? All these actions are the result of a common goal between multiple partners and leads to a mutual sense of obligation, known as “joint commitment”. This ability to cooperate is universal in humans and to certain species of animals, like the great apes.
However, humans seem to have a unique predisposition and strong desire for social interaction that may be one of the components of the emergence of language, according to the authors of the study.
How do our social interactions differ from other species? And why?
To answer these questions, an international team analysed the interactions of 31 children between the ages of 2 and 4 in four preschools in the United States (10 hours per child).
“There have been only a few quantitative analyses of the spontaneous social interactions of 2 and 4 year olds while interacting with peers, although it is a critical age for the development of children’s socio-cognitive abilities. And the ones that exist are either not based on extensive video recordings following individual children for several days or simply do not allow an easy comparison with great apes’ social interactions”, adds Federico Rossano, first author of the study and Assistant Professor at the University of California, San Diego.
They then compared their results with similar interactions in adults and great apes
Multiplication of social partners
The researchers analysed the environmental factors (number of partners, types of activities, etc.) surrounding the children.
They found that children have more frequent (an average of 13 distinct social interactions per hour) and shorter (an average of 28 seconds) social interactions with their peers than great apes in comparable studies.
Adrian Bangerter, co-author of the study and professor at the University of Neuchâtel explains why: “By being exposed to many partners, children learn quickly about the need to coordinate with each other’s behaviour.“ The numbers support this quick learning: 4-year-olds already participate in cooperative social interactions more often than 2-year-olds and fight less than 2-year-olds.
“Learning how to coordinate with others and how to communicate towards engaging in joint activities goes hand-in-hand with learning how to minimise conflict” adds Rossano.
Social interactions are usually marked by an entry and an exit phase (when one starts a conversation with eye contact and a “hello” and then signalling that it is ending by repeating “okay, fine” or with a “goodbye”). These signals are also present in 90% of social engagements in bonobos and 69% in chimpanzees.
It appears that young children use these signals only 66-69% of the time, less frequently than bonobos and adults.
“On one hand this might be due to the appreciation that they will interact again with the same children throughout the day, like two passengers sitting next to each other on a plane starting and stopping quick conversations throughout a flight without using greetings each time they resume talking.
“On the other hand, it might reflect the fact that not every social interaction is based on joint commitment to each other, i.e. at times young children might be bulldozing their way in and assume other children will just adapt to them rather than coordinating”, Rossano explains.
More empirical research will be needed to confirm these behaviours, however this study is a first step in the understanding of the role of joint commitment for human social interaction and how it impacted the evolution of language.
Cooperation in Swiss children
A similar study is currently conducted within the framework of The NCCR Evolving Language, a Swiss research centre that aims at unraveling the biological foundations of language, its evolutionary past and the challenges imposed by new technologies.
A team including the co-authors of the University of Neuchâtel is working with the after-school care facilities of Neuchâtel and aims to understand the development of joint action in children by observing how their use of so-called back-channel words (uh-huh, okay) changes over time when they play a LEGO® cooperative game.
Adrian Bangerter explains why those terms are important to analyse: “We use “small” words like okay, uh-huh, yeah, or right all the time to synchronise our behaviour with our partners. Yet so little is known about how young children acquire the use of them”.
Social interactions facilitated language evolution
The paper was published in the context of a special issue that focuses on the “Interaction Engine” Hypothesis. This hypothesis postulates that social abilities and motivations in humans were determining factors in the evolution of human language, whose origins remain unknown.
See also
In a series of 14 papers edited by Raphaela Heesen of Durham University and Marlen Fröhlich of the University of Tübingen, researchers investigate the social-cognitive capacities that paved the way for the emergence of language by proposing a multidisciplinary and comparative approach. The NCCR Evolving Language is part of this special issue with seven of its researchers co-authoring 4 papers.
About this social neuroscience research news
Author: Emilie Wyss
Source: NCCR
Contact: Emilie Wyss – NCCR
Image: The image is in the public domain
Original Research: Open access.
“How 2- and 4-year-old children coordinate social interactions with peers” by Federico Rossano et al. Philosophical Transactions of the Royal Society B Biological Sciences
Abstract
How 2- and 4-year-old children coordinate social interactions with peers
The Interaction Engine Hypothesis postulates that humans have a unique ability and motivation for social interaction. A crucial juncture in the ontogeny of the interaction engine could be around 2–4 years of age, but observational studies of children in natural contexts are limited. These data appear critical also for comparison with non-human primates.
Here, we report on focal observations on 31 children aged 2- and 4-years old in four preschools (10 h per child). Children interact with a wide range of partners, many infrequently, but with one or two close friends.
Four-year olds engage in cooperative social interactions more often than 2-year olds and fight less than 2-year olds. Conversations and playing with objects are the most frequent social interaction types in both age groups.
Children engage in social interactions with peers frequently (on average 13 distinct social interactions per hour) and briefly (28 s on average) and shorter than those of great apes in comparable studies. Their social interactions feature entry and exit phases about two-thirds of the time, less frequently than great apes.
The results support the Interaction Engine Hypothesis, as young children manifest a remarkable motivation and ability for fast-paced interactions with multiple partners.
Dolphin deaths: Reward offered for information after deadly interactions with humans in Texas and Florida
NOAA Fisheries, a division of the National Oceanic and Atmospheric Administration, announced up to $20,000 for information on anybody who harassed a sick dolphin last month on Quintana Beach in Texas.
The bottlenose dolphin was stranded alive on the beach on April 10 and was pushed back into the water as beachgoers tried to swim with and ride the animal, the Texas Marine Mammal Stranding Network said in a Facebook post.
“She ultimately stranded and was further harassed by a crowd of people on the beach where she later died before rescuers could arrive on scene,” the network said.
Officials have obtained footage of the harassment in Texas that may help in identifying the people involved, NOAA said.
The Marine Mammal Protection Act prohibits the harassing, harming, killing or feeding of wild dolphins.
Harassing, harming, killing or feeding wild dolphins is punishable by up to $100,000 in fines and up to 1 year in jail per violation, the agency said.
Meanwhile, an adult dolphin died in March after she impaled in the head with a spear-like object on Fort Myers Beach, NOAA officials said, citing the dolphin’s autopsy. The agency is also offering a reward of up to $20,000 for information about that incident.
The animal was apparently in a begging position for feeding when it was attacked while still alive. She appeared to have died from the trauma, NOAA said in a statement.
“Begging is not a natural behavior for dolphins and is frequently associated with illegal feeding. People can help prevent future harm to wild dolphins by not feeding or attempting to feed them,” it said.
The agency — whose mission is stewardship of ocean resources and habitats — advised people to avoid interacting with stranded marine animals because they may be sick or injured.
Pushing animals back into the water delays the animal getting the help it needs and may result in re-stranding in worse condition, it said.
CNN’s Michelle Watson and Rebekah Riess contributed to this report.
Physicists Observe Incredible ‘Quantum Tornados’ Formed From Ultra-Cold Atoms
Scientists have observed a stunning demonstration of classic physics giving way to quantum behavior, manipulating a fluid of ultra-cold sodium atoms into a distinct tornado-like formation.
Particles behave differently on the quantum level, in part because at this point their interactions with each other hold more power over them than the energy from their movement.
Then, of course, there’s the mind-boggling fact that quantum particles don’t exactly have a certain fixed location like you or I, which influences how they interact.
By cooling particles down to as close to absolute zero as possible and eliminating other interference, physicists can observe what happens when these strange interactions take hold, as a team from MIT has just done.
“It’s a breakthrough to be able to see these quantum effects directly,” says MIT physicist Martin Zwierlein.
The team trapped and spun a cloud of around 1 million sodium atoms using lasers and electromagnets. In previous research physicists demonstrated this would spin the cloud into a long needle-like structure, a Bose-Einstein condensate, where the gas starts to behave like a single entity with shared properties.
“In a classical fluid, like cigarette smoke, it would just keep getting thinner,” says Zwierlein. “But in the quantum world, a fluid reaches a limit to how thin it can get.”
In the new study, MIT physicist Biswaroop Mukherjee and colleagues pushed beyond this stage, capturing a series of absorption images that reveal what happens after atoms’ have switched from being predominantly governed by classical to quantum physics.
The image below highlights the densities of ultra-cold atoms across microseconds.
(Mukherjee et al, Nature, 2022)
The atom cloud evolved from the needle-like condensate (left), passed through snake-shaped instability (center), and formed miniscule tornadoes (right).
There are even tiny dark spots between the neighboring crystals (see the ‘x’ marks below) where vortexes of counterflow occur – just as we see in complex weather systems (think of the roiling adjoining storms on Jupiter).
“Here, we have quantum weather: The fluid, just from its quantum instabilities, fragments into this crystalline structure of smaller clouds and vortices,” explains Zwierlein.
“This evolution connects to the idea of how a butterfly in China can create a storm [in the US], due to instabilities that set off turbulence. Even in classical physics, this gives rise to intriguing pattern formation, like clouds wrapping around the Earth in beautiful spiral motions. And now we can study this in the quantum world.”
The team controlled the system so nothing else was exerting a force on the atomic subjects. This meant only the interactions of the particles themselves and their rotation was at play. Their resulting behavior displayed supersolid properties, somewhat like what electrons produce in the form of Wigner crystals.
While traditional crystal solids are usually composed of atoms arranged in a stationary, repeating grid structure, these structures continue to fluctuate but remain within a definable pattern – like a liquid pretending to be a solid by holding and flowing through a fixed shape.
The team essentially made the atoms to behave like they’re electrons in a magnetic field. Using atoms in this way makes the resulting quantum phenomena easier to both manipulate and observe – opening the way for even more discoveries about this mind-bending world.
“We can visualize what individual atoms are doing, and see if they obey the same quantum mechanical physics,” says Zwierlein.
This research was published in Nature.
Physicists Detect Elusive ‘Ghost Particles’ in The LHC For The Very First Time
A major milestone in particle physics has just been made at the Large Hadron Collider (LHC).
For the first time, candidate neutrinos have been detected, not just at the LHC, but in any particle collider.
The six neutrino interactions, detected using the neutrino subdetector FASERnu, not only demonstrate the feasibility of the technology, they open up a new avenue for studying these mysterious particles, particularly at high energies.
“Prior to this project, no sign of neutrinos has ever been seen at a particle collider,” said physicist Jonathan Feng of the University of California Irvine, co-leader of the FASER Collaboration.
“This significant breakthrough is a step toward developing a deeper understanding of these elusive particles and the role they play in the Universe.”
Neutrinos are actually everywhere. They’re one of the most abundant subatomic particles in the Universe; but they carry no charge and have almost zero mass so, although they stream through the Universe at almost the speed of light, they barely interact with it at all. Billions of the things are streaming through you right now. To a neutrino, the rest of the Universe is basically incorporeal; that’s why they’re also known as ghost particles.
Although they interact rarely, that’s not the same as never. Detectors such as IceCube in Antarctica, Super-Kamiokande in Japan, and MiniBooNE at Fermilab in Illinois use sensitive photodetector arrays designed to pick up the showers of light that emerge when a neutrino interacts with other particles in a completely dark environment, for example.
But for a long time, scientists have wanted to also study neutrinos produced at particle colliders. That’s because collider neutrinos, which emerge primarily from the decay of hadrons, are produced at very high energies, which are not very well studied. Detecting collider neutrinos provides access to neutrino energies and types that are rarely seen elsewhere.
FASERnu is what is known as an emulsion detector. Lead and tungsten plates are alternated with layers of emulsion: During particle experiments at the LHC, neutrinos can collide with nuclei in the lead and tungsten plates, producing particles that leave tracks in the emulsion layers, a bit like the way ionizing radiation makes tracks in a cloud chamber.
The plates need to be developed like photographic film. Then, physicists can analyze the particle trails to find out what produced them; whether it was a neutrino, and then what the neutrino’s ‘flavor’, or type, was. There are three neutrino flavors – electron, muon and tau – as well as their antineutrino counterparts.
In the FASERnu pilot run conducted in 2018, six candidate neutrino interactions were recorded in the emulsion layers. That may not seem like many, considering how many particles are produced in a run at the LHC, but it gave the collaboration two vital pieces of information.
“First, it verified that the position forward of the ATLAS interaction point at the LHC is the right location for detecting collider neutrinos,” Feng said. “Second, our efforts demonstrated the effectiveness of using an emulsion detector to observe these kinds of neutrino interactions.”
The pilot detector was a relatively small apparatus, at around 29 kilograms (64 pounds). The team is currently working on the full version, around 1,100 kilograms (over 2,400 pounds). This instrument will be significantly more sensitive, and will allow the researchers to differentiate between neutrino flavors and their antineutrino counterparts.
They’re expecting that the third observing run of the Large Hadron Collider will produce 200 billion electron neutrinos, 6 trillion muon neutrinos, and 9 billion tau neutrinos, and their antineutrinos. Since we’ve only detected around 10 tau neutrinos, total, to date, this will be a pretty big deal.
The collaboration is also eyeing even more elusive prey. They have their hopes pinned on a detection of dark photons, which are at the moment hypothetical, but which could help reveal the nature of dark matter, the mysterious directly-undetectable mass that makes up most of the Universe’s matter.
But the neutrino detections alone are a tremendously exciting step forward for our understanding of the fundamental components of the Universe.
“Given the power of our new detector and its prime location at CERN, we expect to be able to record more than 10,000 neutrino interactions in the next run of the LHC, beginning in 2022,” said physicist and astronomer David Casper of the University of California, Irvine, FASER project co-leader.
“We will detect the highest-energy neutrinos that have ever been produced from a human-made source.”
The team’s research has been published in Physical Review D.
Shock AI Discovery Suggests We’ve Not Even Discovered Half of What’s Inside Our Cells
Inside every cell of the human body is a constellation of proteins, millions of them. They’re all jostling about, being speedily assembled, folded, packaged, shipped, cut and recycled in a hive of activity that works at a feverish pace to keep us alive and ticking.
But without a full inventory of the protein universe inside our cells, scientists are hard-pressed to appreciate on a molecular level what goes wrong with our bodies that leads to disease.
Now, researchers have developed a new technique that uses artificial intelligence to assimilate data from microscopy images of single cells and biochemical analyses, to create a ‘unified map’ of subcellular components – half of which, it turns out, we’ve never seen before.
“Scientists have long realized there’s more that we don’t know than we know, but now we finally have a way to look deeper,” says computer scientist and network biologist Trey Ideker of the University of California (UC) San Diego.
Microscopes, powerful as they are, allow scientists to peer inside single cells, down to the level of organelles such as mitochondria, the power packs of cells, and ribosomes, the protein factories. We can even add fluorescent dyes to easily tag and track proteins.
Biochemistry techniques can go deeper still, honing in on single proteins by using, for example, targeted antibodies that bind the protein, pull it out of the cell, and see what else is attached to it.
Integrating those two approaches is a challenge for cell biologists.
“How do you bridge that gap from nanometer to micron-scale? That has long been a big hurdle in the biological sciences,” explains Ideker.
“Turns out you can do it with artificial intelligence – looking at data from multiple sources and asking the system to assemble it into a model of a cell.”
The result: Ideker and colleagues have flipped textbook maps of globular cells which give us a birds-eye view of candy-colored organelles into an intricate web of protein-protein interactions, organized by the teensy distances between them.
Classic view of a Eukaryote cross section. (Mariana Ruiz/LadyofHats/Wikimedia)
Fusing image data from a library called the Human Protein Atlas and existing maps of protein interactions, the machine learning algorithm was tasked with computing the distances between protein pairs.
The goal was to identify communities of proteins, called assemblies, that co-exist in cells at different scales, from the very small (less than 50 nm) to the very ‘large’ (more than 1 μm).
One shy of 70 protein communities were classified by the algorithm, which was trained using a reference library of proteins with known or estimated diameters, and validated with further experiments.
Around half of the protein components identified are seemingly unknown to science, never documented in the published literature, the researchers suggest.
In the mix was one group of proteins forming an unfamiliar structure, which the researchers worked out is likely responsible for splicing and dicing newly made transcripts of the genetic code that are used to make proteins.
Other proteins mapped included transmembrane transport systems that pump supplies into and out of cells, families of proteins that help organize bulky chromosomes, and protein complexes whose job it is to make, well, more proteins.
A hefty effort, it’s not the first time that scientists have tried to map the inner workings of human cells, though.
Other efforts to create reference maps of protein interactions have yielded similarly mind-boggling numbers and attempted to measure protein levels across tissues of the human body.
Researchers have also developed techniques for visualizing and tracking the interaction and movement of proteins in cells.
This pilot study goes a step further by applying machine learning to cellular microscopy images which locate proteins relative to large cellular landmarks such as the nucleus, and data from protein interaction studies that identify a protein’s nearest nano-scale neighbors.
“The combination of these technologies is unique and powerful because it’s the first time measurements at vastly different scales have been brought together,” says bioinformatician Yue Qin, also of UC San Diego.
In doing so, the Multi-Scale Integrated Cell technique or MuSIC “increases the resolution of imaging while giving protein interactions a spatial dimension, paving the way to incorporate diverse types of data in proteome-wide cell maps,” Qin, Ideker and colleagues write.
To be clear, this research is very preliminary: the team focused on validating their method and only looked at the available data from 661 proteins in one cell type, a kidney cell line which scientists have been culturing in the lab for going on five decades.
The researchers plan to apply their newfangled technique to other cell types, says Ideker.
But in the meantime, we’ll have to humbly accept we’re mere interlopers inside our own cells, capable of understanding a small fraction of the total proteome.
“Eventually we might be able to better understand the molecular basis of many diseases by comparing what’s different between healthy and diseased cells,” says Ideker.
The study was published in Nature.
Artificial intelligence successfully predicts protein interactions
UT Southwestern and University of Washington researchers led an international team that used artificial intelligence (AI) and evolutionary analysis to produce 3D models of eukaryotic protein interactions. The study, published in Science, identified more than 100 probable protein complexes for the first time and provided structural models for more than 700 previously uncharacterized ones. Insights into the ways pairs or groups of proteins fit together to carry out cellular processes could lead to a wealth of new drug targets.
“Our results represent a significant advance in the new era in structural biology in which computation plays a fundamental role,” said Qian Cong, Ph.D., Assistant Professor in the Eugene McDermott Center for Human Growth and Development with a secondary appointment in Biophysics.
Dr. Cong led the study with David Baker, Ph.D., Professor of Biochemistry and Dr. Cong’s postdoctoral mentor at the University of Washington prior to her recruitment to UT Southwestern. The study has four co-lead authors, including UT Southwestern Computational Biologist Jimin Pei, Ph.D.
Proteins often operate in pairs or groups known as complexes to accomplish every task needed to keep an organism alive, Dr. Cong explained. While some of these interactions are well studied, many remain a mystery. Constructing comprehensive interactomes—or descriptions of the complete set of molecular interactions in a cell—would shed light on many fundamental aspects of biology and give researchers a new starting point on developing drugs that encourage or discourage these interactions. Dr. Cong works in the emerging field of interactomics, which combines bioinformatics and biology.
Until recently, a major barrier for constructing an interactome was uncertainty over the structures of many proteins, a problem scientists have been trying to solve for half a century. In 2020 and 2021, a company called DeepMind and Dr. Baker’s lab independently released two AI technologies called AlphaFold (AF) and RoseTTAFold (RF) that use different strategies to predict protein structures based on the sequences of the genes that produce them.
In the current study, Dr. Cong, Dr. Baker, and their colleagues expanded on those AI structure-prediction tools by modeling many yeast protein complexes. Yeast is a common model organism for fundamental biological studies. To find proteins that were likely to interact, the scientists first searched the genomes of related fungi for genes that acquired mutations in a linked fashion. They then used the two AI technologies to determine whether these proteins could be fit together in 3D structures.
Their work identified 1,505 probable protein complexes. Of these, 699 had already been structurally characterized, verifying the utility of their method. However, there was only limited experimental data supporting 700 of the predicted interactions, and another 106 had never been described.
To better understand these poorly characterized or unknown complexes, the University of Washington and UT Southwestern teams worked with colleagues around the world who were already studying these or similar proteins. By combining the 3D models the scientists in the current study had generated with information from collaborators, the teams were able to gain new insights into protein complexes involved in maintenance and processing of genetic information, cellular construction and transport systems, metabolism, DNA repair, and other areas. They also identified roles for proteins whose functions were previously unknown based on their newly identified interactions with other well-characterized proteins.
“The work described in our new paper sets the stage for similar studies of the human interactome and could eventually help in developing new treatments for human disease,” Dr. Cong added.
Dr. Cong noted that the predicted protein complex structures generated in this study are available to download from ModelArchive. These structures and others generated using this technology in future studies will be a rich source of research questions for years to come, she said.
Dr. Cong is a Southwestern Medical Foundation Scholar in Biomedical Research. Other UTSW researchers who contributed to this study include Jing Zhang and Josep Rizo, Ph.D., who holds the Virginia Lazenby O’Hara Chair in Biochemistry.
Patterns in DNA reveal hundreds of unknown protein pairings
More information:
Ian R. Humphreys et al, Computed structures of core eukaryotic protein complexes, Science (2021). DOI: 10.1126/science.abm4805
Ian R. Humphreys et al, Computed structures of core eukaryotic protein complexes, Science (2021). DOI: 10.1126/science.abm4805
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UT Southwestern Medical Center
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Artificial intelligence successfully predicts protein interactions (2021, November 16)
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Quasi-particles with tunable interactions
The laws of quantum mechanics allow for the existence of ‘quasi-particles’: excitations in materials that behave exactly like ordinary particles. A major advantage of quasi-particles over ordinary particles is that their properties can be engineered. In a Nature Materials News & Views article this week, IoP physicist Erik van Heumen describes recent experiments where even the interactions between quasi-particles can be tuned.
In recent years, the mathematical branch of topology, studying the shapes of things, and the physical branch of condensed matter physics, studying the behaviour of solids and fluids, have merged into an exciting new research field: that of topological materials. One of the most exciting aspects of this combined field is the emergence of exotic quasi-particles: local disturbances in materials that behave exactly like particles. That such quasi-particles can exist, was already known from the quantum description of simple materials. What the combination with topology offers is a whole new set of such particles, known for example as Dirac and Weyl fermions, axions and magnetic monopoles.
Engineering interactions
Freeing themselves from the strict rules for ordinary particles dictated by nature, researchers gain control over the properties of quasi-particles by a careful choice of the materials used to generate them. One wish that has been high on the list has been to find materials in which the type and strength of interactions between quasi-particles can be tuned.
Recently, a family of materials was discovered that feature atoms arranged in a so-called kagome lattice. In his ‘News & Views’ article, Erik van Heumen describes experiments, reported on in the latest edition of Nature Materials, that suggest the formation in these materials of a so-called ‘flux-density wave’, an excitation that provides the first confirmation of the theoretical predictions that these materials could host exotically interacting quasi-particles. The fact that such tunable interactions between quasi-particles in materials can now be created in the laboratory holds great promise for future studies of topological materials.
Emergent magnetic monopoles isolated using quantum-annealing computer
More information:
Kagome lattices with chiral charge density, Nature Materials (2021). DOI: 10.1038/s41563-021-01095-z
Kagome lattices with chiral charge density, Nature Materials (2021). DOI: 10.1038/s41563-021-01095-z
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University ofAmsterdam
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Quasi-particles with tunable interactions (2021, September 24)
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from https://phys.org/news/2021-09-quasi-particles-tunable-interactions.html
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