Brain imaging links stimulant-use relapse to distinct nerve pathway

Researchers used advanced brain imaging techniques to study nerve fibers connecting to the nucleus accumbens, which plays an important role in motivation and addiction.
Credit: Loreen Tisdall and Kelly H. MacNiven.

You might assume that people who are most prone to developing a substance use disorder in the first place would also have the hardest time avoiding relapse following treatment. But a new study by scientists with the Wu Tsai Neurosciences Institute’s NeuroChoice Initiative reveals that relapse may be linked to quite different brain circuits than addiction itself.

“There’s a huge revolving door problem with relapse,” said Brian Knutson, a professor of psychology. “These findings suggest ​​that what gets you into taking drugs may not be the same processes that get you out of it, which could be very valuable to help predict who is at highest risk of relapse coming out of treatment.”

Drug addiction presents a major global challenge. More than 35 million people worldwide self-report problematic use of drugs and admissions to drug treatment programs have surged in the United States in recent years. For many drugs, in particular stimulants such as cocaine and amphetamines, relapse remains a common problem. For example, as many as 50 percent of people with stimulant use disorders relapse within 6 months of release from treatment.

“The statistics are disheartening,” said Kelly MacNiven, a social science research scholar in the Knutson lab and co-author of the new study. “Unfortunately not much is known at a biological level about the drivers of relapse — understanding this better is going to be the first step to developing better ways to help people get out of dependence.”

Technique Allows Researchers to Align Gold Nanorods with Magnetic Fields

Electron micrograph of gold nanorods overcoated with iron oxide nanoparticles and aligned in a magnetic field.
Image Credit: Mehedi H. Rizvi

An international team of researchers has demonstrated a technique that allows them to align gold nanorods using magnetic fields, while preserving the underlying optical properties of the gold nanorods.

“Gold nanorods are of interest because they can absorb and scatter specific wavelengths of light, making them attractive for use in applications such as biomedical imaging, sensors, and other technologies,” says Joe Tracy, corresponding author of a paper on the work and a professor of materials science and engineering at North Carolina State University.

It is possible to tune the wavelengths of light absorbed and scattered by engineering the dimensions of the gold nanorods. Magnetically controlling their orientation makes it possible to further control and modulate which wavelengths the nanorods respond to.

“In other words, if you can control the alignment of gold nanorods, you have greater control over their optical properties,” Tracy says. “And using magnetic fields to control that alignment means that you can control the alignment without actually touching the nanorods.”

In their technique, the researchers synthesize separate solutions of gold nanorods and iron oxide nanoparticles. Mixing the solutions drives assembly of the iron oxide nanoparticles onto the surface of the gold nanorods. The resulting “coated” nanorods can then be controlled using a low-strength magnetic field.

Where Once Were Black Boxes, NIST’s New LANTERN Illuminates

How do you figure out how to alter a gene so that it makes a usefully different protein? The job might be imagined as interacting with a complex machine (at left) that sports a vast control panel filled with thousands of unlabeled switches, which all affect the device’s output somehow. A new tool called LANTERN figures out which sets of switches — rungs on the gene’s DNA ladder — have the largest effect on a given attribute of the protein. It also summarizes how the user can tweak that attribute to achieve a desired effect, essentially transmuting the many switches on our machine’s panel into another machine (at right) with just a few simple dials.
Credit: B. Hayes/NIST

Researchers at the National Institute of Standards and Technology (NIST) have developed a new statistical tool that they have used to predict protein function. Not only could it help with the difficult job of altering proteins in practically useful ways, but it also works by methods that are fully interpretable — an advantage over the conventional artificial intelligence (AI) that has aided with protein engineering in the past.

The new tool, called LANTERN, could prove useful in work ranging from producing biofuels to improving crops to developing new disease treatments. Proteins, as building blocks of biology, are a key element in all these tasks. But while it is comparatively easy to make changes to the strand of DNA that serves as the blueprint for a given protein, it remains challenging to determine which specific base pairs — rungs on the DNA ladder — are the keys to producing a desired effect. Finding these keys has been the purview of AI built of deep neural networks (DNNs), which, though effective, are notoriously opaque to human understanding.

Natural Disasters Can Accelerate Changes to Tropical Forests

The Blue and John Crow Mountains National Park in Jamaica
Photo Credit of the Jamaica Conservation and Development Trust/Blue and John Crow Mountains National Park.

It’s no surprise that warming temperatures across the earth are having a slow, yet profound impact on the forests of the world.

In a global process called thermophilization, the makeup of forests and other natural communities are changing as plants and trees slowly shift their ranges to higher, cooler altitudes. Species that favor cold climates are moving away from the hot lowlands and into colder highland areas or disappearing from landscapes entirely. While species that favor warmer conditions are moving up and replacing them, research indicates.

Although Kenneth Feeley, associate professor of biology, has documented this phenomenon throughout South and Central America, he wanted to explore whether natural disasters could impact thermophilization, which is driven by climate change. By collaborating with an international team of renowned ecologists, including Edmund Tanner, professor at the University of Cambridge; John Healey, professor at Bangor University; and Peter Bellingham, a professor at the University of Auckland, Feeley said they were able to chronicle the conditions of a Jamaican forest for 40 years and observed that a hurricane sped up the transformation of these tropical forests.

“We saw a consistent process of thermophilization through time, but we noticed the rate of this process was not consistent, and that the hurricane actually accelerated the process,” said Feeley, the University’s Smathers Chair of Tropical Biology. “The forest is resilient and tends to resist changes imposed by climate change, but when you get a large disturbance event like a hurricane, it can break down those barriers, open up the forest to change, and speed up the process of thermophilization.”

Rensselaer Researchers Make Virus-Fighting Face Masks

Rensselaer Polytechnic Institute researchers have developed an accessible way to make N95 face masks not only effective barriers to germs, but on-contact germ killers. The antiviral, antibacterial masks can potentially be worn longer, causing less plastic waste as the masks do not need to be replaced as frequently.

Helen Zha, assistant professor of chemical and biological engineering and a member of the Center for Biotechnology and Interdisciplinary Studies at Rensselaer (CBIS), collaborated with Edmund Palermo, associate professor of materials science and engineering and a member of the Center for Materials, Devices, and Integrated systems (cMDIS) at Rensselaer, to fight infectious respiratory disease and environmental pollution with the perfect recipe to improve face masks.

“This was a multifaceted materials engineering challenge with a great, diverse team of collaborators,” Palermo said. “We think the work is a first step toward longer-lasting, self-sterilizing personal protective equipment, such as the N95 respirator. It may help reduce transmission of airborne pathogens in general.”

In research recently published in Applied ACS Materials and Interfaces, the team successfully grafted broad-spectrum antimicrobial polymers onto the polypropylene filters used in N95 face masks.

“The active filtration layers in N95 masks are very sensitive to chemical modification,” said Zha. “It can make them perform worse in terms of filtration, so they essentially no longer perform like N95s. They’re made out of polypropylene, which is difficult to chemically modify. Another challenge is that you don't want to disrupt the very fine network of fibers in these masks, which might make them more difficult to breathe through.”

Future with geothermal energy

The GeoLaB makes geosciences tangible: the first underground laboratory is being built in the Black Forest / Odenwald, in which researchers can directly observe deep geothermal processes.
Credit: KIT

Local, emission-free and base load-bearing: geothermal energy is an essential component of the energy transition. With GeoLaB, a new and unique underground research infrastructure, the Karlsruhe Institute of Technology (KIT), the German Research Center for Geosciences GFZ and the Helmholtz Center for Environmental Research UFZ now want to accelerate research and prepare the technology for widespread use. The project is to be realized either in the Black Forest or Odenwald, the Helmholtz Association is funding with 35 million euros.

In order to achieve climate neutrality and at the same time become more independent of energy imports, the use of deep geothermal energy is suitable in most regions of Germany. Heat from the subsurface is available regardless of the time of the year and day, which makes geothermal energy suitable for base loads. It is also renewable because heat flows into the reservoir due to the temperature conditions and the transport processes.

“Geothermal energy has huge potential. In Germany alone, we could replace a third of the gas requirements for our heat - and given the climate catastrophe and the geopolitical world situation, we can no longer do without it,” says Professor Holger Hanselka, President of KIT and Vice President for the Energy Research Area of the Helmholtz Association. “So that we can use the necessary technologies safely and that the environmental impact remains minimal, we will now develop geothermal energy accordingly with the help of GeoLaB."

MIT engineers devise a recipe for improving any autonomous robotic system

Autonomous robots have come a long way since the fastidious Roomba. In recent years, artificially intelligent systems have been deployed in self-driving cars, last-mile food delivery, restaurant service, patient screening, hospital cleaning, meal prep, building security, and warehouse packing.

Each of these robotic systems is a product of an ad hoc design process specific to that particular system. In designing an autonomous robot, engineers must run countless trial-and-error simulations, often informed by intuition. These simulations are tailored to a particular robot’s components and tasks, in order to tune and optimize its performance. In some respects, designing an autonomous robot today is like baking a cake from scratch, with no recipe or prepared mix to ensure a successful outcome.

Now, MIT engineers have developed a general design tool for roboticists to use as a sort of automated recipe for success. The team has devised an optimization code that can be applied to simulations of virtually any autonomous robotic system and can be used to automatically identify how and where to tweak a system to improve a robot’s performance.

The team showed that the tool was able to quickly improve the performance of two very different autonomous systems: one in which a robot navigated a path between two obstacles, and another in which a pair of robots worked together to move a heavy box.

The researchers hope the new general-purpose optimizer can help to speed up the development of a wide range of autonomous systems, from walking robots and self-driving vehicles, to soft and dexterous robots, and teams of collaborative robots.

No ‘safest spot’ to minimize risk of COVID-19 transmission on trains

Credit: by Keira Burton

The researchers, from the University of Cambridge and Imperial College London, developed a mathematical model to help predict the risk of disease transmission in a train carriage, and found that in the absence of effective ventilation systems, the risk is the same along the entire length of the carriage.

The model, which was validated with a controlled experiment in a real train carriage, also shows that masks are more effective than social distancing at reducing transmission, especially in trains that are not ventilated with fresh air.

The results, reported in the journal Indoor Air, demonstrate how challenging it is for individuals to calculate absolute risk, and how important it is for train operators to improve their ventilation systems in order to help keep passengers safe.

Since COVID-19 is airborne, ventilation is vital in reducing transmission. And although COVID-19 restrictions have been lifted in the UK, the government continues to highlight the importance of good ventilation in reducing the risk of transmission of COVID-19, as well as other respiratory infections such as influenza.

“In order to improve ventilation systems, it’s important to understand how airborne diseases spread in certain scenarios, but most models are very basic and can’t make good predictions,” said first author Rick de Kreij, who completed the research while based at Cambridge’s Department of Applied Mathematics and Theoretical Physics. “Most simple models assume the air is fully mixed, but that’s not how it works in real life.

Britain's earliest humans

Artist reconstruction of Homo heidelbergensis making a flint hand axe  
Credit: Department of Archaeology, University of Cambridge / Illustration by Gabriel Ugueto

Homo heidelbergensis may have occupied southern Britain between 560,000 and 620,000 years ago

Archaeological discoveries made on the outskirts of Canterbury, Kent (England) confirm the presence of early humans in southern Britain between 560,000 and 620,000 years ago. The breakthrough, involving controlled excavations and radiometric dating, comes a century after stone tool artefacts were first uncovered at the site. The research, led by archaeologists at the University of Cambridge, confirms that Homo heidelbergensis, an ancestor of Neanderthals, occupied southern Britain in this period – when it was still attached to Europe – and gives tantalizing evidence hinting at some of the earliest animal hide processing in European prehistory.

Located in an ancient riverbed, the Canterbury site was originally discovered in the 1920s when local laborers unearthed artefacts known as hand axes, but by applying modern dating techniques to new excavations their age has finally been determined. Led by Cambridge’s Department of Archaeology, the recent excavations have not only dated the original site but also identified new flint artefacts, including the very first ‘scrapers’ to be discovered there. The researchers have dated these stone tool artefacts using infrared-radiofluorescence (IR-RF) dating, a technique which determines the point at which feldspar sand-grains were last exposed to sunlight, and thereby establishing when they were buried.

On the Road to Tiny Transistors, How Flat is Flat?

The general architecture of a traditional MOSFET vs. a 2D FET. A FET (field-effect transistor) is a device for regulating the flow of charge carriers (such as electrons) across a channel with three terminals: a source, a drain, and a gate. A MOSFET (metal oxide semiconductor field effect transistor) is by far the most widely used type of FET and is a building block of modern electronics, used in commercial electronic devices for more than 50 years. One main difference between the traditional 3D MOSFET and the “emerging technology” of the 2D FET is that the channel in a traditional MOSFET is in a 3D material, while a 2D FET’s channel is a 2D material.
Credit: Sean Kelley/NIST

Transistors are the building blocks of modern electronics, used in everything from televisions to laptops. As transistors have gotten smaller and more compact, so have electronics, which is why your cell phone is a super powerful computer that fits in the palm of your hand.

But there’s a scaling problem: Transistors are now so small that they are difficult to turn off. A key device element is the channel that charge carriers (such as electrons) travel across between electrodes. If that channel gets too short, quantum effects allow electrons to effectively jump from one side to another even when they shouldn’t.

One way to get past this sizing roadblock is to use layers of 2D materials – which are only a single atom thick – as the channel. Atomically thin channels can help enable even smaller transistors by making it harder for the electrons to jump between electrodes. One well-known example of a 2D material is graphene, whose discoverers won the Nobel Prize in Physics in 2010. But there are other 2D materials, and many believe they are the future of transistors, with the promise of scaling channel thickness down from its current 3D limit of a few nanometers (nm, billionths of a meter) to less than a single nanometer thickness.

Though research has exploded in this area, one issue has been persistently overlooked, according to a team of scientists from the National Institute of Standards and Technology (NIST), Purdue University, Duke University, and North Carolina State University. The 2D materials and their interfaces – which researchers intend to be flat when stacked on top of each other – may not, in fact, be flat. This non-flatness in turn can significantly affect device performance, sometimes in good ways and sometimes in bad.