Tech

Scientists have made a significant breakthrough in the quest to understand the intricate processes that occur in the brain during seizures that are the key symptom of epilepsy.

A team of scientists from the University of Exeter has studied the mechanisms behind distinctive patterns of electrical activity of neuron groups in the brain that accompany the onset of seizures.

In healthy brains, networks of neurons move through states of similar behavior - known as synchronization - and dissimilar behavior, called desynchronization. These processes are also associated with both memory and attention.

However, in a brain with a neurological disorder, such as epilepsy, this synchronization can grow to an almost dangerous extent, when a collection of brain cells begins to emit excess electricity.

In a series of new studies, published recently in PLoS Computational Biology and the SIAM Journal on Applied Dynamical Systems, the research team used sophisticated mathematical modelling approach to explore the interplay between groups of neurons, that leads to transitions in synchronization changes.

Jennifer Creaser, co-author of the study and from the University of Exeter said: "Synchronization is thought to be important for information processing. But too much synchronization--such as what occurs in epileptic seizures or Parkinson's disease--is associated with disease states and can impair brain function."

The study, which took place at the Engineering and Physical Science Research Council's Centre for Predictive Modelling in Healthcare at the University of Exeter and University of Birmingham, used an extended version of an existing mathematical model that represents the brain as a network connecting multiple nodes of neuron groups.

The model network consists of bi-stable nodes, meaning that each node can switch between two stable states - resting and seizure. These nodes remain in their current state until they receive a stimulus that gives them the appropriate 'kick' to escape to the other state.

This stimulus comes from both other connected nodes and in the form of "noise" -- outside sources of neural activity, such as endocrine responses that are associated with an emotional state or physiological changes due to disease.

Adding a small amount of noise to the system caused each node to transition to the active state -- but the system's geometry was such that returning to the resting state took much longer than leaving.

Previously, the research team found that this leads to a cascade of escapes to the active state--much like a falling line of dominos--that spreads activity across the network.

The new research builds on this 'domino effect' to identify the circumstances that bring about these changes in synchrony and investigate how the type of coupling in a network affects its behaviour.

It found that, when the model incorporated more general amplitude and phase coupling, the nodes' synchrony could change between consecutive escapes during the domino effect.

Professor Peter Ashwin, co-author of the study said: "Although this is a theoretical study of an idealized model, it is inspired by challenges posed by understanding transitions between healthy and pathological activity in the brain."

Professor Krasimira Tsaneva-Atanasova, also co-author of the study added: "The mathematical modeling of seizure initiation and propagation can not only help to uncover seizures' complex underlying mechanisms, but also provide a means for enabling in silico experiments to predict the outcome of manipulating neural systems."

The largest single cell study to date of the childhood cancer, neuroblastoma, has answered important questions about the genesis of the disease. The researchers from the Wellcome Sanger Institute, Great Ormond Street Hospital (GOSH) and the Princess Máxima Center for Pediatric Oncology, discovered that all neuroblastomas arise from a single type of embryonic cell called sympathoblasts.

The study, published today (5 February 2021) in Science Advances, sought to understand why neuroblastomas range in severity, with some easy to treat and others having relatively low five-year survival rates. The fact that all neuroblastomas arise from sympathoblasts makes them an attractive drug target, because these cells exist only in the tumour after the child is born.

Neuroblastoma is a rare cancer that generally affects children under five years old. It begins in the abdomen, usually in the adrenal glands - hormone-producing glands above the kidneys. Neuroblastoma is remarkable in that its severity can vary greatly between individuals. In some children the cancer will disappear without treatment, whereas in others the cancer is relentless. The five-year survival rate for neuroblastoma is one of the lowest of all childhood cancers*.

This varied outlook prompted the researchers to ask whether the range of severity could be caused by neuroblastomas arising from different cell types at different stages of the child's development in the womb. This was made possible by the advent of single cell mRNA sequencing, a high-resolution technology that can identify different cell types present in a tissue according to the genes expressed by individual cells.

In this study, gene expression of 19,723 cancer cells was analysed and compared to a reference of 57,972 developmental adrenal cells in the hope of identifying the cell types from which neuroblastomas arise and to find novel treatment targets.

Dr Jan Molenaar, a senior author of the study from the Princess Máxima Center for Pediatric Oncology in the Netherlands, said: "What is most striking about our findings is that despite the great diversity of clinical behaviour of neuroblastoma, there is an overarching neuroblastoma cell type that is found in all patients. The identification of sympathoblasts as the root of all neuroblastoma is an important step towards understanding how the disease develops and, hopefully, how we can treat it."

Currently, many cancer treatments cause serious side effects for the patient. But in recent years, technological advances have sped up drug development by allowing researchers to identify differences between the biological processes, such as the expression of a particular gene, within healthy human cells and those within cancerous ones. These differences can be exploited to attack cancer cells without affecting the patient's healthy cells.

The presence of sympathoblasts, a developmental cell type not normally found in children after they are born, makes it a promising drug target for the treatment of neuroblastoma.

Dr Karin Straathof, a senior author of the study from Great Ormond Street Hospital, said: "Neuroblastoma is an unusual cancer in that some tumours resolve without intervention, yet the disease still has one of the lowest five-year survival rates of any childhood cancer. This study fills important gaps in our knowledge of what neuroblastoma cells are and revealed novel treatment targets. My hope is that new, less intrusive therapies can be developed by targeting sympathoblasts, a developmental cell type that exists only in neuroblastoma tumours after a child is born."

As well as facilitating the discovery of sympathoblasts as the root of neuroblastoma, the single-cell reference map of the developmental adrenal gland will also contribute to the Human Cell Atlas project**. The project aims to create comprehensive reference maps of all types of human cells - the fundamental units of life - as a basis for understanding human health and diagnosing, monitoring, and treating disease.

Dr Sam Behjati, a senior author of the study from the Wellcome Sanger Institute and Cambridge University Hospitals, said: "Our study shows the power of looking at individual childhood cancer cells in revealing not just one, but a plethora of novel treatment ideas. This raises the exciting prospect that a single cell atlas of all types of paediatric tumours may transform our understanding of childhood cancer."

UCLA materials scientists and colleagues have discovered that perovskites, a class of promising materials that could be used for low-cost, high-performance solar cells and LEDs, have a previously unutilized molecular component that can further tune the electronic property of perovskites.

Named after Russian mineralogist Lev Perovski, perovskite materials have a crystal-lattice structure of inorganic molecules like that of ceramics, along with organic molecules that are interlaced throughout. Up to now, these organic molecules appeared to only serve a structural function and could not directly contribute to perovskites' electronic performance.

Led by UCLA, a new study shows that when the organic molecules are designed properly, they not only can maintain the crystal lattice structure, but also contribute to the materials' electronic properties. This discovery opens up new possibilities to improve the design of materials that will lead to better solar cells and LEDs. The study detailing the research was recently published in Science.

"This is like finding an old dog that can play new tricks," said Yang Yang, the Carol and Lawrence E. Tannas Jr. Professor of Engineering at the UCLA Samueli School of Engineering, who is the principal investigator on the research. "In materials science, we look all the way down to the atomic structure of a material for efficient performance. Our postdocs and graduate students didn't take anything for granted and dug deeper to find a new pathway."

In order to make a better-performing perovskite material, the researchers incorporated a specially designed organic molecule, a pyrene-containing organic ammonium. On its exterior, the positively charged ammonium molecule connected to molecules of pyrene -- a quadruple ring of carbon atoms. This molecular design offered additional electronic tunability of perovskites.

"The unique property of perovskites is that they have the advantage of high-performance inorganic semiconductors, as well as easy and low-cost processability of polymers," said study co-lead author Rui Wang, a UCLA postdoctoral scholar in materials science and engineering. "This newly enhanced perovskite material now offers opportunities for improved design concepts with better efficiency."

To demonstrate perovskites' added effectiveness, the team built a photovoltaic (PV) cell prototype with the materials, and then tested it under continuous light for 2,000 hours. The new cell continued to convert light to energy at 85% of its original efficiency. This contrasts with a PV cell made of the same materials, but without the added altered organic molecule, which retained only 60% of its original efficiency.

Virtual reality isn't just for gaming. Researchers can use virtual reality, or VR, to assess participants' attention, memory and problem-solving abilities in real world settings. By using VR technology to examine how folks complete daily tasks, like making a grocery list, researchers can better help clinical populations that struggle with executive functioning to manage their everyday lives.

Lead author Zhengsi Chang is a PhD student that works in the lab of Daniel Krawczyk, PhD, deputy director of the Center for BrainHealth®. Along with Brandon Pires, a researcher at Texas Tech University Health Sciences Center, the team investigated whether VR can be used to effectively test a participant's executive functional load, or how much information a person can process to achieve a goal. Their findings were recently published in Computers in Human Behavior Reports.

The researchers adapted the Virtual Reality Functional Capacity Assessment Tool's (VRFCAT) "kitchen test", where participants plan a trip to the grocery store by comparing ingredients in kitchen cabinets to a list of recipes. Making a grocery list is an everyday task and should therefore accurately capture participants' daily working memory and performance. "Function-led tasks using VR technology allow us to maintain a balance between ecological validity and experimental control," said Chang.

In the virtual kitchen, 42 healthy adult college students memorized a slew of ingredients from a recipe list. The participants then navigated the kitchen to check for ingredients and tried to remember which ingredients they found. They returned to their recipe list, crossing off all the ingredients they didn't need at the store. Once they finished checking their grocery list, participants picked up their wallet and left the virtual kitchen.

To test their executive functional load, the researchers increased the number of ingredients and recipes to be memorized. Participants took longer to complete their grocery lists when they had to memorize more ingredients. This aligns with the researchers' prediction that participants' task performance would decrease as functional load increases, which suggests that this VR assessment can effectively test executive functional load.

Computers in Human Behavior Reports
Volume 2, August–December 2020, 100035
Researchers were surprised to find that participants' working memories were not related to how well they performed the task. "People might spend the same amount of time on the task, and make the same number of errors, but they could have totally different working memory capacities," said Chang.

Upon further analysis, the researchers realized that participants were actually switching up their strategies as executive functional load increased. Some participants tried to memorize as many ingredients as possible before looking at the recipe while others frequently switched between rummaging through the kitchen cabinets and examining the recipe list.

This strategy-switching explains why the researchers did not see a relationship between performance and participants' working memory. "This study indicates that our strategies have a dramatic effect on our capacity. If you enter into a task prepared with a plan, you will get the most out of your brain and see much better performance," said Krawczyk. Participants' performance reflects their executive function and supports the idea that the researchers' VR assessment can effectively test participants' executive function load.

The researchers hope to use their VR assessment to help people that suffer from executive function impairments. "We used VR technology to create an executive function assessment that can be used in neuropsychology to understand how veterans and other clinical populations manage their everyday lives," said Chang.

Philadelphia, February 4, 2021 - Researchers have leveraged the latest advances in RNA technology and machine learning methods to develop a gene panel test that allows for highly accurate diagnosis of the most common types of liposarcoma. It quickly and reliably distinguishes benign lipomas from liposarcomas and can be performed in laboratories at a lower cost than current "gold standard" tests. The new assay is described in The Journal of Molecular Diagnosis, published by Elsevier.

"Liposarcomas are a type of malignant cancer that is difficult to diagnose because, even under a microscope, it is hard to differentiate liposarcomas from benign tumors or other types of cancer that need different treatments," explains lead investigator Torsten Owen Nielsen, MD, PhD, Genetic Pathology Evaluation Centre, Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, Canada. "Many liposarcomas look like their benign and relatively common counterparts, lipomas. Diagnostic delay and uncertainty cause severe stress for patients, and misdiagnosis can have many consequences including delayed or inadequate treatment or unnecessary surgical procedures and long-term postoperative follow up."

Among the current recommended diagnostic tests for liposarcomas, immunohistochemistry (IHC) is inaccurate and hard to interpret, and fluorescence in situ hybridization (FISH) is relatively expensive, as well as labor- and equipment-intensive. In this study, investigators explored whether NanoString technology, ideal for analyzing even poor-quality RNA, could allow for more rapid and cost-efficient diagnosis of liposarcomas through gene expression.

The investigators utilized data from the Cancer Genome Atlas, a catalog of genetic information from over 20,000 cancer samples, to identify the 20 most common genes that are overexpressed in liposarcomas. Probes for these genes were designed by NanoString bioinformatics and run on a set of "training samples" that included lipomas and liposarcomas. Analysis of the NanoString results showed clear separation of lipoma from liposarcoma cases.

A machine learning model was developed to determine the probability that a given sample was positive for liposarcoma and was then applied to 45 retrospective cases to determine boundaries for positive and negative predictions. The test was subsequently applied in a real-world clinical setting. A molecular technologist with no knowledge of the clinical, histologic, IHC, or FISH information about the cases was asked to identify each sample as liposarcoma or not liposarcoma from the NanoString test results. The same samples were examined by specialist pathologists using standard testing.

The retrospective and prospective cases probed by the NanoString assay had a 93 percent success rate and agreed with standard tests 97.8 percent of the time. Results from the NanoString assay were available in 36 hours, whereas it took between one to two weeks to get FISH results. NanoString costs amounted to US$270 per case, factoring in reagents, labor, and equipment maintenance.

Advances in biotechnology have shown great promise in other cancers such as breast cancer. "We applied these new technologies to improve patient care in areas where existing diagnostic methods were inaccurate, slow, or costly and saw substantial improvements. There is no patent on this test; anyone can apply the method we describe, and we are happy to help others get set up to run it at their own institution," says Dr. Nielsen.

First author Xiu Qing (Jenny) Wang, a fourth year medical student at the University of British Columbia, adds that accurate and fast diagnosis can be critical for patients who are dealing with what are often difficult and quite large tumors. "I am proud to be part of a team that has made significant progress in bringing this test closer to clinical reality."

This group previously published a NanoString-based assay for sarcomas bearing diagnostic fusion oncogenes, which is now in clinical use. However, the most common type of liposarcoma carries a different type of mutation (gene amplifications) and so was not covered. Thus, the current study not only expands the types of sarcoma that can be diagnosed accurately with NanoString-based diagnostics, but also shows how a different category of mutation can be detected. This strategy may help develop diagnostics for other types of cancer.

LEBANON, NH - Dartmouth's and Dartmouth-Hitchcock's Norris Cotton Cancer Center (NCCC) is the first cancer center in the world to install BeamSite Cherenkov imaging cameras in its radiotherapy treatment rooms. The camera system, invented, validated and commercialized by entrepreneurs from NCCC and Dartmouth spinoff biomed tech company, DoseOptics, LLC, captures imaging and real-time video of the beam directly on the patient, allowing the radiation oncology team to visualize treatment delivery.

Cherenkov imaging makes radiation treatment a visual process. The Cherenkov effect occurs when photon or electron radiation beams interact with tissue, such as skin, producing a small light emission from the surface. BeamSite cameras can capture images of the treatment-beam shapes in real time, as well as show levels of intensity that are proportional to the radiation dose. These visual data can be used to verify both accuracy of dose and of beam delivery at each daily treatment, a verification not possible using standard quality assurance measures.

"Cherenkov imaging provides visualization of the radiation therapy treatment, so that the treatment team can see everything, and make adjustments when unexpected things happen," explains Brian Pogue, PhD, co-director of the Translational Engineering in Cancer Research Program at NCCC, MacLean Professor of Engineering Sciences at Dartmouth Engineering and co-founder of DoseOptics, LLC. A joint engineering and oncology team reviewed events recorded in their Cherenkov imaging study over the course of a few years, during which they documented incidents when radiotherapy delivery was not ideal and the adjustments made to rectify. Their findings, "Initial Clinical Experience of Cherenkov Imaging in External Beam Radiation Therapy Identifies Opportunities to Improve Treatment Delivery," have been published in The International Journal of Radiation Oncology, Biology, Physics.

There were a total of 64 patients in the study, under the supervision of radiation oncologist and lead author Lesley Jarvis, MD, PhD, Member of NCCC's Translational Engineering in Cancer Research Program and Associate Professor of Medicine at the Geisel School of Medicine at Dartmouth. The patients were receiving treatment for breast cancer, sarcoma, lymphoma and other cancers. Six patients were found to indicate that adjustments would have improved treatment, such as stray radiation dose exposure to the opposite breast, arm or chin during breast cancer treatments. The imaging system was also used to identify when inadvertent dose was not an issue, such as confirming no unintended exposure of the opposite leg during an extremity sarcoma treatment.

Radiation therapy is a repetitive procedure given to patients daily for about 30 days. Setting patients up on the treatment couch and daily alignment of the beam is a complex process. Beyond positional complications, the therapy team has to leave the room when the beam is on, so if anything happens during delivery, problem-solving tools are very limited. National statistics show that incidents of incorrect delivery might occur on a level of about 1%. In a busy clinic, this could mean one patient per week. "Normally the treatments are just fine," says Pogue. "However, if you cannot see where the beam is, then it is a blind treatment, and the interaction between patient and therapy team is just less natural than it could be if the treatment was visual."

NCCC is currently the only cancer center in the world with regular use of Cherenkov imaging in all radiotherapy treatments, and was uniquely positioned for clinical research teams to test out these cameras for the planned study. Cherenkov imaging cameras have been installed in most linear accelerators within Dartmouth-Hitchcock, providing an extra level of safety during each patient's therapy session. "Cherenkov cameras mounted inside the radiotherapy treatment rooms give us the ability to simply see the treatment and provide an intuitive guide to therapists that we otherwise wouldn't have had," says Pogue. "This is a terrific tool for tracking what happens each day and in each treatment, and for improving the quality of radiotherapy delivery."

DoseOptics' technology was developed through research at Dartmouth by Dartmouth faculty, who then licensed the product to the company. Pioneered at Dartmouth-Hitchcock, it is now expanding to other cancer centers. Since DoseOptics, LLC, received FDA clearance to market BeamSite in December of 2020, the team hopes all radiation oncology clinics will introduce the technology to their programs. "Clinics should have all the tools available to them to ensure that each treatment for each patient is accurate, and to be able to quickly notice issues and fix them," says Pogue.

Astronomers have for the first time used distant galaxies as 'scintillating pins' to locate and identify a piece of the Milky Way's missing matter.

For decades, scientists have been puzzled as to why they couldn't account for all the matter in the universe as predicted by theory. While most of the universe's mass is thought to be mysterious dark matter and dark energy, 5 percent is 'normal matter' that makes up stars, planets, asteroids, peanut butter and butterflies. This is known as baryonic matter.

However, direct measurement has only accounted for about half the expected baryonic matter.

Yuanming Wang, a doctoral candidate in the School of Physics at the University of Sydney, has developed an ingenious method to help track down the missing matter. She has applied her technique to pinpoint a hitherto undetected stream of cold gas in the Milky Way about 10 light years from Earth. The cloud is about a trillion kilometres long and 10 billion kilometres wide but only weighing about the mass of our Moon.

The results, published in the Monthly Notices of the Royal Astronomical Society, offer a promising way for scientists to track down the Milky Way's missing matter.

"We suspect that much of the 'missing' baryonic matter is in the form of cold gas clouds either in galaxies or between galaxies," said Ms Wang, who is pursuing her PhD at the Sydney Institute for Astronomy.

"This gas is undetectable using conventional methods, as it emits no visible light of its own and is just too cold for detection via radio astronomy," she said.

What the astronomers did is look for radio sources in the distant background to see how they 'shimmered'.

"We found five twinkling radio sources on a giant line in the sky. Our analysis shows their light must have passed through the same cold clump of gas," Ms Wang said.

Just as visible light is distorted as it passes through our atmosphere to give stars their twinkle, when radio waves pass through matter, it also affects their brightness. It was this 'scintillation' that Ms Wang and her colleagues detected.

Dr Artem Tuntsov, a co-author from Manly Astrophysics, said: "We aren't quite sure what the strange cloud is, but one possibility is that it could be a hydrogen 'snow cloud' disrupted by a nearby star to form a long, thin clump of gas."

Hydrogen freezes at about minus 260 degrees and theorists have proposed that some of the universe's missing baryonic matter could be locked up in these hydrogen 'snow clouds'. They are almost impossible to detect directly.

"However, we have now developed a method to identify such clumps of 'invisible' cold gas using background galaxies as pins," Ms Wang said.

Ms Wang's supervisor, Professor Tara Murphy, said: "This is a brilliant result for a young astronomer. We hope the methods trailblazed by Yuanming will allow us to detect more missing matter."

The data to find the gas cloud was taken using the CSIRO's Australian Square Kilometre Array Pathfinder (ASKAP) radio telescope in Western Australia.

Dr Keith Bannister, Principal Research Engineer at CSIRO, said: "It is ASKAP's wide field of view, seeing tens of thousands of galaxies in a single observation that allowed us to measure the shape of the gas cloud."

Professor Murphy said: "This is the first time that multiple 'scintillators' have been detected behind the same cloud of cold gas. In the next few years, we should be able to use similar methods with ASKAP to detect a large number of such gas structures in our galaxy."

Ms Wang's discovery adds to a growing suite of tools for astronomers in their hunt for the universe's missing baryonic matter. This includes a method published last year by the late Jean-Pierre Macquart from Curtin University who used CSIRO's ASKAP telescope to estimate a portion of matter in the intergalactic medium using fast radio bursts as 'cosmic weigh stations'.

What The Study Did: Researchers estimated the rate of nurse burnout in the United States and the factors associated with leaving or considering leaving their jobs due to burnout.

Authors: Megha K. Shah, M.D., M.Sc., of the Emory University School of Medicine in Atlanta, is the corresponding author.

To access the embargoed study: Visit our For The Media website at this link https://media.jamanetwork.com/

(doi:10.1001/jamanetworkopen.2020.36469)

Editor's Note: The article includes conflict of interest and funding/support disclosures. Please see the article for additional information, including other authors, author contributions and affiliations, conflict of interest and financial disclosures, and funding and support.

What The Study Did: Researchers in this randomized clinical trial investigated whether nasopharyngeal application of povidone iodine could reduce the viral load of patients with nonsevere COVID-19 symptoms.

Authors: Olivier Mimoz, M.D., Ph.D., University Hospital of Poitiers in Poitiers, France, is the corresponding author.

To access the embargoed study:  Visit our For The Media website at this link https://media.jamanetwork.com/ 

(doi:10.1001/jamaoto.2020.5490)

Editor's Note: The article includes conflict of interest and funding/support disclosures. Please see the article for additional information, including other authors, author contributions and affiliations, conflict of interest and financial disclosures, and funding and support.

CAMBRIDGE, MA -- Cryo-electron microscopy (cryo-EM) allows scientists to produce high-resolution, three-dimensional images of tiny molecules such as proteins. This technique works best for imaging proteins that exist in only one conformation, but MIT researchers have now developed a machine-learning algorithm that helps them identify multiple possible structures that a protein can take.

Unlike AI techniques that aim to predict protein structure from sequence data alone, protein structure can also be experimentally determined using cryo-EM, which produces hundreds of thousands, or even millions, of two-dimensional images of protein samples frozen in a thin layer of ice. Computer algorithms then piece together these images, taken from different angles, into a three-dimensional representation of the protein in a process termed reconstruction.

In a Nature Methods paper, the MIT researchers report a new AI-based software for reconstructing multiple structures and motions of the imaged protein -- a major goal in the protein science community. Instead of using the traditional representation of protein structure as electron-scattering intensities on a 3D lattice, which is impractical for modeling multiple structures, the researchers introduced a new neural network architecture that can efficiently generate the full ensemble of structures in a single model.

"With the broad representation power of neural networks, we can extract structural information from noisy images and visualize detailed movements of macromolecular machines," says Ellen Zhong, an MIT graduate student and the lead author of the paper.

With their software, they discovered protein motions from imaging datasets where only a single static 3D structure was originally identified. They also visualized large-scale flexible motions of the spliceosome -- a protein complex that coordinates the splicing of the protein coding sequences of transcribed RNA.

"Our idea was to try to use machine-learning techniques to better capture the underlying structural heterogeneity, and to allow us to inspect the variety of structural states that are present in a sample," says Joseph Davis, the Whitehead Career Development Assistant Professor in MIT's Department of Biology.

Davis and Bonnie Berger, the Simons Professor of Mathematics at MIT and head of the Computation and Biology group at the Computer Science and Artificial Intelligence Laboratory, are the senior authors of the study, which appears today in Nature Methods. MIT postdoc Tristan Bepler is also an author of the paper.

Visualizing a multistep process

The researchers demonstrated the utility of their new approach by analyzing structures that form during the process of assembling ribosomes -- the cell organelles responsible for reading messenger RNA and translating it into proteins. Davis began studying the structure of ribosomes while a postdoc at the Scripps Research Institute. Ribosomes have two major subunits, each of which contains many individual proteins that are assembled in a multistep process.

To study the steps of ribosome assembly in detail, Davis stalled the process at different points and then took electron microscope images of the resulting structures. At some points, blocking assembly resulted in accumulation of just a single structure, suggesting that there is only one way for that step to occur. However, blocking other points resulted in many different structures, suggesting that the assembly could occur in a variety of ways.

Because some of these experiments generated so many different protein structures, traditional cryo-EM reconstruction tools did not work well to determine what those structures were.

"In general, it's an extremely challenging problem to try to figure out how many states you have when you have a mixture of particles," Davis says.

After starting his lab at MIT in 2017, he teamed up with Berger to use machine learning to develop a model that can use the two-dimensional images produced by cryo-EM to generate all of the three-dimensional structures found in the original sample.

In the new Nature Methods study, the researchers demonstrated the power of the technique by using it to identify a new ribosomal state that hadn't been seen before. Previous studies had suggested that as a ribosome is assembled, large structural elements, which are akin to the foundation for a building, form first. Only after this foundation is formed are the "active sites" of the ribosome, which read messenger RNA and synthesize proteins, added to the structure.

In the new study, however, the researchers found that in a very small subset of ribosomes, about 1 percent, a structure that is normally added at the end actually appears before assembly of the foundation. To account for that, Davis hypothesizes that it might be too energetically expensive for cells to ensure that every single ribosome is assembled in the correct order.

"The cells are likely evolved to find a balance between what they can tolerate, which is maybe a small percentage of these types of potentially deleterious structures, and what it would cost to completely remove them from the assembly pathway," he says.

Viral proteins

The researchers are now using this technique to study the coronavirus spike protein, which is the viral protein that binds to receptors on human cells and allows them to enter cells. The receptor binding domain (RBD) of the spike protein has three subunits, each of which can point either up or down.

"For me, watching the pandemic unfold over the past year has emphasized how important front-line antiviral drugs will be in battling similar viruses, which are likely to emerge in the future. As we start to think about how one might develop small molecule compounds to force all of the RBDs into the 'down' state so that they can't interact with human cells, understanding exactly what the 'up' state looks like and how much conformational flexibility there is will be informative for drug design. We hope our new technique can reveal these sorts of structural details," Davis says.

In 2018, the physics world was set ablaze with the discovery that when an ultrathin layer of carbon, called graphene, is stacked and twisted to a "magic angle," that new double layered structure converts into a superconductor, allowing electricity to flow without resistance or energy waste. Now, in a literal twist, Harvard scientists have expanded on that superconducting system by adding a third layer and rotating it, opening the door for continued advancements in graphene-based superconductivity.

The work is described in a new paper in Science and can one day help lead toward superconductors that operate at higher or even close to room temperature. These superconductors are considered the holy grail of condensed matter physics since they would allow for tremendous technological revolutions in many areas including electricity transmission, transportation, and quantum computing. Most superconductors today, including the double layered graphene structure, work only at ultracold temperatures.

"Superconductivity in twisted graphene provides physicists with an experimentally controllable and theoretically accessible model system where they can play with the system's properties to decode the secrets of high temperature superconductivity," said one of the paper's co-lead authors Andrew Zimmerman, a postdoctoral researcher in working in the lab of Harvard physicist Philip Kim.

Graphene is a one-atom-thick layer of carbon atoms that is 200 times stronger than steel yet is extremely flexible and lighter than paper. It has almost always been known to be a good conductor of heat and electrical current but is notoriously difficult to handle. Experiments unlocking the puzzle of twisted bilayer graphene have been ongoing since MIT physicist Pablo Jarillo-Herrero and his group pioneered the emerging field of "twistronics" with their experiment in 2018 where they produced the graphene superconductor by twisting it to a magic angle of 1.1 degrees.

The Harvard scientists report successfully stacking three sheets of graphene and then twisting each of them at that magic angle to produce a three-layered structure that is not only capable of superconductivity but does so more robustly and at higher temperatures than many of the double-stacked graphene. The new and improved system is also sensitive to an externally applied electric field that allows them to tune the level of superconductivity by adjusting the strength of that field.

"It enabled us to observe the superconductor in a new dimension and provided us with important clues about the mechanism that's driving the superconductivity," said the study's other lead author Zeyu Hao, a Ph.D. student in the Graduate School of Arts and Sciences also working in the Kim Group.

One of those mechanisms has the theorists really excited. The trilayer system showed evidence that its superconductivity is due to strong interactions between electrons as opposed to weak ones. If true, this can not only help open a path to high temperature superconductivity but possible applications in quantum computing.

"In most conventional superconductors, electrons move with a high speed and occasionally cross-paths and influence each other. In this case, we say their interaction effects are weak," said Eslam Khalaf, a co-author on the study and postdoctoral fellow working in the lab of Harvard physics professor Ashvin Vishwanath. "While weakly interacting superconductors are fragile and lose superconductivity when heated to a few Kelvins, strong coupling superconductors are much more resilient but much less understood. Realizing strong coupling superconductivity in a simple and tunable system such as trilayer could pave the way to finally develop a theoretical understanding of strongly-coupled superconductors to help realize the goal of a high temperature, maybe even room temperature, superconductor."

The researchers plan on continuing to explore the nature of this unusual superconductivity in further studies.

"The more we understand, the better we have chance to increase the superconducting transition temperatures," said Kim.

Scientists have found genetic differences between bats killed by white-nose syndrome and bats that survived, suggesting that survivors rapidly evolve to resist the fungal disease, according to a Rutgers-led study with big implications for deciding how to safeguard bat populations.

White-nose syndrome has killed millions of bats in North America since 2006, following its introduction from Europe. The syndrome, caused by the fungal pathogen Pseudogymnoascus destructans, is arguably the most catastrophic wildlife disease in history. It has led to unprecedented declines in many North American bat species, including the little brown bat (Myotis lucifugus).

"Our finding that little brown bat populations have evolved, which could be why they survived, has large implications for management of bat populations going forward," said lead author Sarah Gignoux-Wolfsohn, a former postdoctoral associate at Rutgers University-New Brunswick now at the Smithsonian Environmental Research Center in Maryland. "Management decisions, such as whether to treat for white-nose syndrome or protect populations from other detrimental factors, can be informed by knowing which bats are genetically resistant to the disease."

"The deployment of vaccines or treatments for the fungus may be most needed in populations with few disease-resistant individuals," said Gignoux-Wolfsohn, who led the study - published in the journal Molecular Ecology - while at Rutgers. "Our study also has implications for other diseases that cause mass mortality. While rapid evolution in response to these diseases is often difficult to detect, our study suggests it may be more common than previously thought."

The disease got its name because the fungus, which grows in cold, dark and damp places, sometimes looks like white fuzz on bats' faces, according to the White-Nose Syndrome Response Team led by the U.S. Fish and Wildlife Service. The fungus attacks the bare skin of bats when they're hibernating and relatively inactive. As it grows, the fungus causes changes in bats that make them become unusually active and burn up fat they need to survive winter. Infected bats may do strange things, such as fly outside in the daytime during winter.

Before white-nose syndrome arrived in North America, the little brown bat was one of the most widespread bat species. The disease has caused populations to decline by about 78 percent, killing many hibernating colonies. Still, some populations appear to be recovering after significant declines, likely due to increased disease resistance.

Scientists sequenced bat genomes from three hibernating colonies in abandoned mines in New York, New Jersey and Vermont to determine whether little brown bats evolved as a result of the disease. They compared the genomes of bats killed by white-nose syndrome to survivors in recovering populations to identify genetic differences that may be responsible for survival.

The bats' evolution appears to have particularly affected genes associated with weight gain before hibernation and behavior during hibernation. Rapid evolution may have allowed the remaining bats to keep hibernating and survive infection that killed off millions of other bats.

"Evolution is often thought of as a process that happened long ago," Gignoux-Wolfsohn said. "We have found that it has also been happening right in our backyards and barns over the last decade."

The scientists are conducting a similar study in Indiana bats (Myotis sodalis). While also affected by white-nose syndrome, this species has experienced lesser declines than little brown bats.

Niigata, Japan - Researchers from the Graduate School of Science and Technology at Niigata University, Japan along with their collaborators from Tokyo University of Science (Japan), Yamagata University (Japan) and University of Regensburg (Germany) have published a scientific article which enhances clarity on the understanding of proton conduction mechanism in protic ionic liquids. The findings which were recently published in The Journal of Physical Chemistry B sheds light on the transport of hydrogen ions in these liquids, which opens new avenues for the development of novel energy generation and storage devices.

With an objective to understand the underlying ion transport mechanism in protic and pseudo-protic ionic liquids, the multinational research team has been on constant pursuit for over a decade to uncover the mystery. "We are fascinated by the immense potential exhibited by proton conducting ionic liquids. These superionic liquids are usually materials in the liquid state which show high protonic conductivity. Owing to their superior electrical properties, we believe that they are excellent candidates as electrolyte in fuel cells. So we have been constantly attempting to understand them better to effectively extract their potential as fuel cell electrolytes" says Professor Yasuhiro Umebayashi of the Graduate School of Science and Technology, Niigata University.

Such a study is essential in today's context as research groups across the globe strive to develop new technologies to meet the world's growing need for energy with a simultaneous caution to conserve the environmental resources. Among the prominent clean energy technologies, fuel cells have emerged as a suitable candidate for a wide variety of static and dynamic engineering applications.

Assistant Professor Hikari Watanabe of the Faculty of Science and Technology, Tokyo University of Science explains that "In the recent past, fuel cells are one of the most promising and high potential green energy sources. The investigation on improving the efficiency of fuel cells has been persistent for over a century and it's about time for a breakthrough".

A fuel cell converts the energy from a chemical reaction into electrical energy in an environment friendly process. The benefits of using fuel cells over conventional combustion-based technologies are numerous- higher efficiencies, lower emissions and quiet operation to name a few. The fuel cell functionally comprises, mainly of three regions such as the cathode, anode and electrolyte. While the cathode and anode are good conductors of electrons, the electrolyte is a chemical medium with poor electrical conduction. Predominantly, the electrolytes conduct oxide ions or protons. Owing to the agile nature of the protons, fuel cells constituted by protonic conductors hold immense potential in future renewable energy resources.

Protonic conductors, the materials which reveal the transportation of hydrogen ion, are usually considered to exhibit proton hopping mechanism. "The Grotthuss mechanism, alternatively referred to as proton hopping or proton jumping shall be visualized by an analogy wherein a group of children, arranged in a particular fashion, pass a ball consecutively from one person to the other. In scientific terms, proton hopping denotes the process of diffusion of an excess proton through a network of hydrogen bond" describes Assistant Professor Watanabe. Alternatively, proton transport occurs by the vehicle mechanism, wherein the free proton gets attached onto an oxygen ion and moves together with it. This mechanism is analogical to the movement of a ball carrier in a rugby game.

The researcher team by both theoretical simulations and experimental studies have identified a relation between the proton conduction behavior and the pH of the ionic liquid. They interestingly note that the proton conduction mechanism shifts from proton hopping to vehicle mechanism when the acidity of the liquid electrolyte increases. The valuable findings offers the potential to identify new families of protic ionic liquids by controlling the acid levels.

"The development of new hydrogen ion conductors will lead to the practical application of fuel cells. We have found that the mechanism of hydrogen ion conduction is related to the index of acid and alkali. The idea of acids and alkalis is widely known and can be applied to various substances. We hope that new hydrogen ion conductors will be identified using this index as a guideline", reveals Professor Umebayashi.

The interesting results of the research study will support the large-scale utilization of fuel cells in the near future.

The next time you tuck in to a tikka masala you might find yourself asking a burning question: are spices used in dishes to help stop infection?

It's a question many have chewed the fat over. And now thanks to new research from The Australian National University (ANU) we have an answer.

The quick takeaway is: probably not.

Professor Lindell Bromham and her colleagues asked why hot countries across the world tend to have spicy food? This pattern has led to what some have termed "Darwinian gastronomy" - a tummy-led cultural evolutionary process in countries with hotter climates.

To find out the answer to their question, the researchers feasted on a true smorgasbord of data, examining more the 33,000 recipes from 70 cuisines containing 93 different spices.

"The theory is that spicy foods helped people survive in hot climates where the risk of infection from food can have a big cost in terms of health and survival," Professor Bromham said.

"But we found that this theory doesn't hold up.

"Spicier food is found in hotter countries, but our analysis provides no clear reason to believe that this is primarily a cultural adaptation to reducing infection risk from food."

The study instead shows that while use of spice is related to the risk of foodborne illness, it's also associated with a wide range of health outcomes. In fact, spice use is even related to causes of death that have nothing to do with infection risk, such as fatal car accidents.

"So there is a significant relationship between life expectancy and spicy food," Professor Bromham said.

"But this doesn't mean that spicy food shortens your life span or makes you crash your car. Instead, there are many socioeconomic indicators that all scale together, and many of them also scale with spice use."

Professor Bromham said that because the spiciness of cuisines scales with many socio-economic factors, like gross domestic product per capita and life expectancy, it is difficult to tease apart the key causes. However, the researchers could rule out some possible explanations of why some areas use more spices in their cooking.

"Spicier foods are not explained by variation in climate, human population density or cultural diversity," she said.

"And patterns of spice use don't seem to be driven by biodiversity, nor by the number of different crops grown, nor even by the number of spices growing naturally in the area."

Whatever the key drivers for the use of spice, one thing is certain - our palettes and plates are a lot better for it!

The study's findings are published in Nature Human Behaviour.

Osaka - Many different catalysts that promote the conversion of glucose to sorbitol have been studied; however, most offer certain properties while requiring compromises on others. Now, researchers from Osaka University have reported a hydrotalcite-supported nickel phosphide nanoparticle catalyst (nano-Ni2P/HT) that ticks all the boxes. Their findings are published in Green Chemistry.

Sorbitol is a versatile molecule that is widely used in the food, cosmetics, and pharmaceuticals industries. There is therefore a pressing need to produce sorbitol in a sustainable, low-cost, and green manner.

The nickel catalysts that are commonly used in the industrial hydrogenation of glucose to sorbitol are unstable in air and require hash reaction conditions. Rare metal alternatives—despite being more efficient—can be expensive and are susceptible to poisoning.

nano-Ni2P/HT is stable in air and has a high activity for the hydrogenation of glucose to sorbitol. In addition, nano-Ni2P/HT produces a particular sorbitol structure, known as D-sorbitol, at more than 99% yield. This high selectivity means that a high-purity product can be obtained.

The nano-Ni2P/HT-catalyzed hydrogenation can be carried out in water. Moreover, the catalyst shows good conversion and selectivity when the temperature is just 25°C—compared with 100-180°C for conventional processes—or when the hydrogen gas pressure is only 1 bar—compared with 50-150 bar. The energy saved by using these mild conditions would lead to greener and more sustainable procedures, as well as reduce operating costs.

"Our nano-Ni2P/HT catalyst outperformed conventional nickel alternatives in terms of both the catalytic activity and the amount of D-sorbitol that was produced, which is very encouraging," study first author Sho Yamaguchi explains. "nano-Ni2P/HT also gave a better yield of D-sorbitol than a commercially available noble metal catalyst."

Repeated use of the catalyst showed that nano-Ni2P/HT could be recycled with no significant loss of performance. The reaction could also be carried out at high glucose concentration (50 wt%), which demonstrates the viability of the catalyst for large scale use.

"The continual improvement of industrial catalyst is necessary to achieve sustainable, low-cost production with an environmental conscience," says study corresponding author Takato Mitsudome. "We believe our catalyst will make an important contribution, not only to D-sorbitol production, but to the development of other processes that support the pharmaceutical, food, and cosmetics industries."