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Manipulating non-magnetic atoms in a chromium halide enables tuning of magnetic properties




New approach creates synthetic layered magnets with unprecedented level of control over their magnetic properties

Chestnut Hill, Mass. (7/24/2020) – The magnetic properties of a chromium halide can be tuned by manipulating the non-magnetic atoms in the material, a team, led by Boston College researchers, reports in the most recent edition of Science Advances.

The seemingly counter-intuitive method is based on a mechanism known as an indirect exchange interaction, according to Boston College Assistant Professor of Physics Fazel Tafti, a lead author of the report.

An indirect interaction is mediated between two magnetic atoms via a non-magnetic atom known as the ligand. The Tafti Lab findings show that by changing the composition of these ligand atoms, all the magnetic properties can be easily tuned.

“We addressed a fundamental question: is it possible to control the magnetic properties of a material by changing the non-magnetic elements?” said Tafti. “This idea and the methodology we report on are unprecedented. Our findings demonstrate a new approach to create synthetic layered magnets with unprecedented level of control over their magnetic properties.”

Magnetic materials are the backbone of most current technology, such as the magnetic memory in our mobile devices. It is common practice to tune the magnetic properties by modifying the magnetic atoms in a material. For example, one magnetic element, such as chromium, can be replaced with another one, such as iron.

The team studied ways to experimentally control the magnetic properties of inorganic magnetic materials, specifically, chromium halides. These materials are made of one Chromium atom and three halide atoms: Chlorine, Bromine, and Iodine.

The central finding illustrates a new method of controlling the magnetic interactions in layered materials by using a special interaction known as the ligand spin-orbit coupling. The spin-orbit coupling is a property of an atom to re-orient the direction of spins – the tiny magnets on the electrons – with the orbital movement of the electrons around the atoms.

This interaction controls the direction and magnitude of magnetism. Scientists have been familiar with the spin-orbit coupling of the magnetic atoms, but they did not know that the spin-orbit coupling of the non-magnetic atoms could also be utilized to re-orient the spins and tune the magnetic properties, according to Tafti.

The team was surprised that they could generate an entire phase diagram by modifying the non-magnetic atoms in a compound, said Tafti, who co-authored the report with fellow BC physicists Ying Ran and Kenneth Burch, post-doctoral researchers Joseph Tang and Mykola Abramchuk, graduate student Faranak Bahrami, and undergraduate students Thomas Tartaglia and Meaghan Doyle. Julia Chan and Gregory McCandless of the University of Texas, Dallas, and Jose Lado of Finland’s Aalto University, were also part of the team.

“This finding puts forward a novel procedure to control magnetism in layered materials, opening up a pathway to create new synthetic magnets with exotic properties,” Tafti said. “Moreover, we found strong signatures of a potentially exotic quantum state associated to magnetic frustration, an unexpected discovery that can lead to an exciting new research direction.”

Tafti said the next step is to use these materials in innovative technologies such as magneto-optical devices or the new generation of magnetic memories.




How an Israeli Startup Is Using AI to Help People Make Babies




The first baby conceived using in-vitro fertilization (IVF) was born in the UK in 1978. Over 40 years later, the technique has become commonplace, but its success rate is still fairly low at around 22 to 30 percent. A female-founded Israeli startup called Embryonics is setting out to change this by using artificial intelligence to screen embryos.

IVF consists of fertilizing a woman’s egg with her partner’s or a donor’s sperm outside of her body, creating an embryo that’s then implanted in the uterus. It’s not an easy process in any sense of the word—physically, emotionally, or financially. Insurance rarely covers IVF, and the costs run anywhere from $12,000 to $25,000 per cycle (a cycle takes about a month and includes stimulating a woman’s ovaries to produce eggs, extracting the eggs, inseminating them outside the body, and implanting an embryo).

Women have to give themselves daily hormone shots to stimulate egg production, and these can cause uncomfortable side effects. After so much stress and expense, it’s disheartening to think that the odds of a successful pregnancy are, at best, one in three.

A crucial factor in whether or not an IVF cycle works—that is, whether the embryo implants in the uterus and begins to develop into a healthy fetus—is the quality of the embryo. Doctors examine embryos through a microscope to determine how many cells they contain and whether they appear healthy, and choose the one that looks most viable.

But the human eye can only see so much, even with the help of a microscope; despite embryologists’ efforts to select the “best” embryo, success rates are still relatively low. “Many decisions are based on gut feeling or personal experience,” said Embryonics founder and CEO Yael Gold-Zamir. “Even if you go to the same IVF center, two experts can give you different opinions on the same embryo.”

This is where Embryonics’ technology comes in. They used 8,789 time-lapse videos of developing embryos to train an algorithm that predicts the likelihood of successful embryo implantation. A little less than half of the embryos from the dataset were graded by embryologists, and implantation data was integrated when it was available (as a binary “successful” or “failed” metric).

The algorithm uses geometric deep learning, a technique that takes a traditional convolutional neural network—which filters input data to create maps of its features, and is most commonly used for image recognition—and applies it to more complex data like 3D objects and graphs. Within days after fertilization, the embryo is still at the blastocyst stage, essentially a microscopic clump of just 200-300 cells; the algorithm uses this deep learning technique to spot and identify patterns in embryo development that human embryologists either wouldn’t see at all, or would require massive collation of data to validate.

On top of the embryo videos, Embryonics’ team incorporated patient data and environmental data from the lab into its algorithm, with encouraging results: the company reports that using its algorithm resulted in a 12 percent increase in positive predictive value (identifying embryos that would lead to implantation and healthy pregnancy) and a 29 percent increase in negative predictive value (identifying embyros that would not result in successful pregnancy) when compared to an external panel of embryologists.

TechCrunch reported last week that in a pilot of 11 women who used Embryonics’ algorithm to select their embryos, 6 are enjoying successful pregnancies, while 5 are still awaiting results.

Embryonics wasn’t the first group to think of using AI to screen embryos; a similar algorithm developed in 2019 by researchers at Weill Cornell Medicine was able to classify the quality of a set of embryo images with 97 percent accuracy. But Embryonics will be one of the first to bring this sort of technology to market. The company is waiting to receive approval from European regulatory bodies to be able to sell the software to fertility clinics in Europe.

Its timing is ripe: as more and more women delay having kids due to lifestyle and career-related factors, demand for IVF is growing, and will likely accelerate in coming years.

The company ultimately hopes to bring its product to the US, as well as to expand its work to include using data to improve hormonal stimulation.

Image Credit: Gerd Altmann from Pixabay


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Scientists Made a Biohybrid Nose Using Cells From Mosquitoes




Thanks to biological parts of a mosquito’s “nose,” we’re finally closer to Smell-O-Vision for computers. And a way to diagnose early cancer.

With the recent explosion in computing hardware prowess and AI, we’ve been able to somewhat adequately duplicate our core senses with machines. Computer vision and bioengineered retinas tag-teamed to bolster artificial vision. Smart prosthetics seamlessly simulate touch, pain, pressure, and other skin sensations. Hearing devices are ever more capable of isolating specific sounds from a muddy cacophony of noises.

Yet what’s been missing is the sense of smell. Although humans are hardly smelling maestros in the animal kingdom, we can nevertheless detect roughly one trillion odors, often with only a single scent molecule drifting into our noses. Despite their best efforts, translating this prowess into a digital, artificial computer “nose” has eluded scientists. One major problem is reverse-engineering the sensitivity of our biological smelling mechanics—otherwise known as our olfactory system.

“Odors, airborne chemical signatures, can carry useful information about environments … However, this information is not harnessed well due to a lack of sensors with sufficient sensitivity and selectivity,” said Dr. Shoji Takeuchi from the Biohybrid Systems Laboratory at the University of Tokyo.

But here’s the thing: evolution already came up with a solution. Rather than rebuild olfaction from scratch, why not just use what’s available?

In a new paper published in Science Advances, Takeuchi and colleagues did just that. They tapped the odor-sensing components of a Yellow Fever mosquito (yikes), and rebuilt the entire construct with synthetic biology. Using a parallel chip design, they then carefully placed these biological components as an array onto a chip and monitored the setup with a computer.

By infusing odor chemicals into tiny liquid droplets—think essential oils mixing with liquid in a humidifier—the chip could detect odors with unprecedented sensitivity. Because each biological odor-sensing component is tailored to their favorite chemical, it’s possible in theory to use a single 16-channel chip to detect over a quadrillion mixes of odors.

“These biohybrid sensors…are highly sensitive,” said Takeuchi. With AI, he added, the biohybrid sensor could be further amped up to analyze increasingly complex mixes of chemicals, both in the environment and as a disease-hunting breathalyzer to monitor health.

The Anatomy of Smell

The animal olfactory system is an evolutionary work of art. Although specific biological setups differ between species, the general concept is pretty similar.

It all starts in the nose. Our noses are densely packed with olfactory cells, huddled high up in our nasal passages. Think of these cells as fatty bubbles, each with electrical wires to transfer information up the chain. Dotted along the outer rim of the bubbles are proteins, called olfactory receptors. These are basically “smart” tunnels that connect the outside environment with the interior sanctuary of the cells.

Here’s the crux: each tunnel, or receptor, is tailored to a single odor chemical. It’s normally shut. When an odor—say, vanillin, the dominant chemical in vanilla—drifts into the nasal passage, it grasps onto its preferred receptor. This action opens the receptor tunnel, causing ions to flow into the cell. Translation? It triggers an electrical current, a sort of “on” signal, that gets sent to the brain.

Now take something more complicated in scent, say, French toast. Multiple chemical molecules grab onto their respective odor receptors. Each sends a current, which get analyzed together as they race along nerve highways towards the brain. Based on previous experience, the brain can then analyze the combination data and determine “oh, that’s French toast!”

As you can tell, combination is key. It’s how our 400 or so olfactory receptors can detect a trillion different odors. It’s also what the Japanese team tapped into for inspiration in their new artificial “nose.”

Mosquitoes to the Rescue

Rather than reconstructing a human smell receptor, the team turned to mosquitoes. It’s not that the team particularly loves these blood-sucking demons. It’s because the mosquito odor receptors are built to heighten their sense of smell. In addition to the usual odor-grabbing component, these nuisance bugs also have a separate component that amplifies the electrical signal in a biological way. This allows them to smell with higher sensitivity, while being able to minutely discern what they’re smelling—blood or Deet. It’s a trait that’s terrible for us, but great for an artificial nose.

The next step was to reconstruct the cell structure on a chip. Here, the team used a proprietary recipe to carefully micro-engineer two bubbles, each filled with liquid, smashed together horizontally, forming a sort of squished figure-eight. At the nexus between the two bubbles is a structure that mimics a cell’s outer shell—its membrane, with two fatty layers. The team called this their artificial cell membrane.

They then tapped synthetic biology to make mosquito odor receptors from scratch using DNA. These receptors are embedded into the artificial cell membrane. The entire contraption—think two oil tanks, closely snuggled together but separated by the odor-sensing membrane—was then plopped onto a chip. Each odor-detection unit sat on top of a bunch of slits on the chip, as a sort of venting mechanism.

Here’s how the odor detection works. Each chip has engraved “channels,” allowing any odor to flow towards each detection unit. At a unit, the odor chemicals are then shuttled into the slits using blasts of nitrogen air. This “blows” the odor molecules to mix better with the liquid in the bubbles, so that the odor-detection component gets more of a “whiff.” Think stirring a pot of chili so you can smell it better—that’s the general idea.

Once the odor molecules grabbed onto the embedded mosquito smell sensors, the sensors generated an electrical current. Using an electrode and a computer, the team could then monitor for these currents. Rather than relying on a single device, they linked up 16 on a chip to further increase sensitivity.

As a proof of concept, the team fed an odor molecule called octenol to their biohybrid nose. Octenol is often detected in the breath of cancer patients. At just a few parts per billion—a range similar to our natural noses—the biohybrid “nose” was able to reliably pick up the smell, with over 90 percent accuracy.

In another test, the team had a volunteer breathe heavily into a bag. They then connected the bag, through a regulator for constant air flow, to the artificial nose. “Although the human breath contains about 3,000 different metabolites [molecules],” the team said, for a healthy human without octenol in their breath, the nose remained silent. However, once a tiny bit of octenol gas was added, the bionic “nose” immediately generated an electrical bump indicating its presence.

A Fragrant Future

For now, the team has only tested the contraption one scent at a time. But because the sensing channels are plug-and-play, they’re confident of a fragrant, blossoming future. Mixing and matching different odor receptors, for example, could lead to artificial noses that outwit our own.

Eventually, the team hopes to use their bionic nose as an affordable and portable way to detect early stages of diseases. With cancer and other health problems, the mixture of body odors can dramatically shift. It’s how dogs and other animals with a more heightened sense of smell than our own can detect health issues at early stages. In addition, a biohybrid nose could also venture into contaminated wastelands to screen for toxic chemicals without harming a living being.

To compliment the “mosquito nose” hardware, the team is already looking towards more sophisticated AI software. “I would like to expand upon the analytical side of the system by using some kind of AI. This could enable our biohybrid sensors to detect more complex kinds of molecules,” said Takeuchi.

Image Credit: Richárd Ecsedi on Unsplash


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No Trees Harmed: MIT Aims to One Day Grow Your Kitchen Table in a Lab




You’ve likely heard the buzz around lab-grown (or cultured) meat. We can now take a few cells from a live animal and grow those cells into a piece of meat. The process is kinder to animals, consumes fewer resources, and has less environmental impact.

MIT researchers will soon publish a paper describing a proof-of-concept for lab-grown plant tissues, like wood and fiber, using a similar approach. The research is early, but it’s a big vision. The idea is to grow instead of build some products made of biomaterials.

Consider your average wooden table. Over the years, a tree (or trees) converted sunlight, minerals, and water into leaves, wood, bark, and seeds. When it reached a certain size, the tree was logged and transported to a sawmill to be made into lumber. The lumber was then transported to a factory or wood shop where it was cut, shaped, and fastened together.

Now, imagine the whole process happening at the same time in the same location. That’s the futuristic idea at play here. Wood grown from only the cells you’re interested in (no seeds, leaves, bark, or roots) could be manipulated to produce desirable properties and grown directly into shapes (like a kitchen table). Fewer 18-wheelers and power tools.

No fuss, no muss.

And of course, once refined, the technique wouldn’t be confined to growing tables. Other products could be made from a variety of biomaterials. In theory, and at scale, the process would be more efficient, less wasteful, and save a few forests too.

That’s the vision. But first, researchers need to figure out if it’s even viable.

Coaxing Wood From Cells

Lead author and MIT PhD student in mechanical engineering, Ashley Beckwith, said she was inspired by time spent on a farm. Viewed through the exacting lens of an engineer, Beckwith was struck by agriculture’s inefficiencies. The weather and seasons are beyond our control. We use land and resources to grow whole plants but only use bits and pieces of them for food or materials.

“That got me thinking: Can we be more strategic about what we’re getting out of our process? Can we get more yield for our inputs?” Beckwith said in an MIT release about the research. “I wanted to find a more efficient way to use land and resources so that we could let more arable areas remain wild, or to remain lower production but allow for greater biodiversity.”

To test the idea, the team took cells from the leaves of a zinnia plant and fed them in a liquid growth medium. After the cells grew and divided, the researchers placed them in a gel scaffold and bathed the cells in hormones. You may be wondering what cells from zinnias—which are a small flowering plant—have to do with wood. Turns out, their properties can be “tuned” like stem cells to express desired attributes. The hormones, auxin and cytokinin, induced the zinnia cells to produce lignin, a polymer that makes wood firm.

A culture of wood-like cells from zinnia leaves. Image Credit: MIT

By adjusting their hormonal knobs, the team was able to dial in lignin production. Further, the gel scaffold, which is itself firm, coaxed the cells to grow into a particular shape.

“The idea is not only to tailor the properties of the material, but also to tailor the shape from conception,” said Luis Fernando Velásquez-García, a principal scientist in MIT’s Microsystems Technology Laboratories, coauthor on the paper, and Beckwith’s coadvisor.

Velásquez-García’s lab works with 3D printing technology, and he sees the new technique as a kind of additive manufacturing, where each cell is a printer and the gel scaffold directs their production. While it’s still early, the team believes their work proves plant cells can be manipulated to produce a biomaterial with properties suitable for a specified use. But of course, much more work is required to take the idea beyond proof-of-concept.

Growing Things

The researchers say they need to figure out if what they’ve learned can be adapted to other cell types. The hormonal knobs and dials may differ species to species. Also, scaling up requires solving problems like maintaining healthy gas-exchange between cells. Pending more research, whether the idea makes a strong case compared to traditional methods outside of the lab is, of course, also an open question. But this isn’t unusual.

Early research answers the basic question: Is this idea worth pursuing further? It often, necessarily, leaves key questions unanswered, such as cost and scalability. Early experiments in lab-grown meat, for example, were incredibly costly and lacked key properties. The first lab-grown burger famously cost a few hundred thousand dollars but lacked the fatty (tasty) bits of a traditional ground-beef burger.

It wasn’t ready for prime time in terms of cost or quality, but in the years since, investment and interest have grown and costs declined. Now it’s not so laughable to imagine lab-grown meat in your local grocery or restaurant. Just last year, Singapore became the first country to approve lab-grown meat for commercial consumption.

Whether or not this particular vision gathers steam, seeing cells as miniature factories isn’t new. Increasingly, the worlds of bioengineering and manufacturing are colliding. Engineered cells are already being put to work in industrial settings, and last fall, a Japanese clothing brand offered a limited edition (and extremely pricey) sweater made of 30% fiber produced by gene-hacked bacteria grown in a bioreactor.

Down the road, it’s possible we’ll not only build furniture—but grow it too.

Image Credit: Sam 🐷 / Unsplash


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Regulating the ribosomal RNA production line




Cryo-electron microscopy study allows researchers to visualize structural changes in an E. coli enzyme synthesizing ribosomal RNA that shift it between turbo- and slow-modes depending on the bacteria’s growth rate

The enzyme that makes RNA from a DNA template is altered to slow the production of ribosomal RNA (rRNA), the most abundant type of RNA within cells, when resources are scarce and the bacteria Escherichia coli needs to slow its growth. Researchers used cryo-electron microscopy (cryo-EM) to capture the structures of the RNA polymerase while in complex with DNA and showed how its activity is changed in response to poor-growth conditions. A paper describing the research led by Penn State scientists appears January 22, 2020 in the journal Nature Communications.

“RNA polymerase is an enzyme that produces a variety of RNAs using information encoded in DNA,” said Katsuhiko Murakami, professor of biochemistry and molecular biology at Penn State and the leader of the research team. “This is one of the key steps in the central dogma of molecular biology: transferring genetic information from DNA to RNA, which in turn often codes for protein. It’s required for life and the process is basically shared from bacteria to humans. We are interested in understanding how the structure of RNA polymerase is changed for modulating its activity and function, but it’s been difficult to capture using traditional methods like X-ray crystallography, which requires crystallizing a sample to determine its structure.”

RNA polymerase functions by binding to specific DNA sequences called “promoters” found near the beginning of genes that are going to be made into RNA. To understand the structure and function of the polymerase during this interaction, researchers need to capture the polymerase while it is bound to the promoter DNA, but the interaction can be very unstable at some promoters. Crystallography can only capture RNA polymerase bound to a promoter if the complex is very stable, but for ribosomal RNA promoters this interaction tends to be unstable so that the polymerase can quickly escape to begin making the RNA. To see these interactions the researchers turned to cryo-EM, a method that allows them to visualize the structure of macromolecules in solution.

“When you talk about RNA, most people think about messenger RNA (mRNA), which is the template for making proteins,” said Murakami. “But the most abundant type of RNA in cells doesn’t actually code for protein. Ribosomal RNA is the major structural component of the ribosome, which is the cellular machinery that builds proteins using messenger RNAs as templates. Ribosomal RNA synthesis accounts for up to 70 percent of total RNA synthesis in E. coli cells.”

When a cell divides, which E. coli can do every twenty minutes in nutrient-rich growth conditions, it needs to provide the two resulting daughter cells with enough ribosomes to function, so it is continually making ribosomal RNAs.

“If you do some back-of-the-envelope calculations, an E. coli cell needs to make around 70,000 ribosomes every 20 minutes,” said Murakami. “This means RNA polymerase starts ribosomal RNA synthesis every 1.7 seconds from each ribosomal RNA promoter. So, the polymerase has to bind the ribosomal RNA promoter transiently in order to quickly move onto the ribosomal RNA synthesis step. This is not an ideal for a crystallographic approach, but in a cryo-EM study, we could capture this interaction and, in fact, see different several stages of the interaction in a single sample.”

The researchers were able to determine the three-dimensional structures of the RNA polymerase-promoter complex at two different stages. One when the DNA was still “closed,” before the two strands of the DNA molecule are separated allowing access to the template strand (they refer to this as a closed complex), and one when the DNA was “open” (called an open complex) and primed for RNA synthesis to begin.

“We found a large conformational change in part of the polymerase called the ? (sigma) factor when it binds to promoter DNA, which has never been observed before” said Murakami. “This change opens a gate that allows the DNA to enter a cleft in the polymerase and form the open complex quickly.”

When E. coli needs to slow its growth due to limited resources, two molecules–a global transcription regulator called DksA and a bacterial signaling molecule called ppGpp, bind directly with the polymerase to reduce production of ribosomal RNA. The research team investigated how the binding of these two factors alters the conformation of the polymerase and affects its activity in a promoter-specific manner.

“DksA and ppGpp binding to the polymerase alters its conformation, which prevents the opening of a gate and therefor the polymerase has to follow an alternative pathway to form the open complex,” said Murakami. “This is not an ideal pathway for the ribosomal RNA promoter and thus slow its activity. It’s exciting to see these conformational changes to the polymerase that have direct functional consequences. We couldn’t do this without the cryo-EM, so I’m very thankful to have access to this technology here at Penn State for optimizing experimental conditions for preparing cryo-EM specimens before sending them to the National Cryo-EM Facility at NCI/NIH for high-resolution data collections. We are going to be able to continue to analyze cellular components and complexes that were previously inaccessible.”


In addition to Murakami, the research team includes Yeonoh Shin and M. Zuhaib Qayyum at Penn State and Danil Pupov, Daria Esyunina, and Andrey Kulbachinskiy at the Russian Academy of Sciences. The research was funded by the U.S. National Institutes of Health, the Russian Science Foundation, and the Russian Foundation for Basic Research. Additional support was provided by the National Cancer Institute’s National Cryo-EM Facility at the Frederick National Laboratory for Cancer Research.


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