Scott Coyle, University of Wisconsin-Madison

Waves are ubiquitous in nature and technology. Whether it's the rise and fall of ocean tides or the swinging of a clock's pendulum, the predictable rhythms of waves create a signal that is easy to track and distinguish from other types of signals.

Electronic devices use radio waves to send and receive data, like your laptop and Wi-Fi router or cellphone and cell tower. Similarly, scientists can use a different type of wave to transmit a different type of data: signals from the invisible processes and dynamics underlying how cells make decisions.

I am a synthetic biologist, and my research group developed a technology that sends a wave of engineered proteins traveling through a human cell to provide a window into the hidden activities that power cells when they're healthy and harm cells when they go haywire.

Waves are a powerful engineering tool

The oscillating behavior of waves is one reason they're powerful patterns in engineering.

For example, controlled and predictable changes to wave oscillations can be used to encode data, such as voice or video information. In the case of radio, each station is assigned a unique electromagnetic wave that oscillates at its own frequency. These are the numbers you see on the radio dial.

Scientists can extend this strategy to living cells. My team used waves of proteins to turn a cell into a microscopic radio station, broadcasting data about its activity in real time to study its behavior.

Turning cells into radio stations

Studying the inside of cells requires a kind of wave that can specifically connect to and interact with the machinery and components of a cell.

While electronic devices are built from wires and transistors, cells are built from and controlled by a diverse collection of chemical building blocks called proteins. Proteins perform an array of functions within the cell, from extracting energy from sugar to deciding whether the cell should grow.

Protein waves are generally rare in nature, but some bacteria naturally generate waves of two proteins called MinD and MinE – typically referred to together as MinDE – to help them divide. My team discovered that putting MinDE into human cells causes the proteins to reorganize themselves into a stunning array of waves and patterns.

On their own, MinDE protein waves do not interact with other proteins in human cells. However, we found that MinDE could be readily engineered to react to the activity of specific human proteins responsible for making decisions about whether to grow, send signals to neighboring cells, move around and divide.

The protein dynamics driving these cellular functions are typically difficult to detect and study in living cells because the activity of proteins is generally invisible to even high-power microscopes. The disruption of these protein patterns is at the core of many cancers and developmental disorders.

We engineered connections between MinDE protein waves and the activity of proteins responsible for key cellular processes. Now, the activity of these proteins trigger changes in the frequency or amplitude of the protein wave, just like an AM/FM radio. Using microscopes, we can detect and record the unique signals individual cells are broadcasting and then decode them to recover the dynamics of these cellular processes.

We have only begun to scratch the surface of how scientists can use protein waves to study cells. If the history of waves in technology is any indicator, their potential is vast.

Scott Coyle, Assistant Professor of Biochemistry, University of Wisconsin-Madison

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Martin Elvis, Smithsonian Institution

The 2020s have already seen many lunar landing attempts, although several of them have crashed or toppled over. With all the excitement surrounding the prospect of humans returning to the Moon, both commercial interests and scientists stand to gain.

The Moon is uniquely suitable for researchers to build telescopes they can't put on Earth because it doesn't have as much satellite interference as Earth, nor a magnetic field blocking out radio waves. But only recently have astronomers like me started thinking about potential conflicts between the desire to expand knowledge of the universe on one side and geopolitical rivalries and commercial gain on the other, and how to balance those interests.

As an astronomer and the co-chair of the International Astronomical Union's working group Astronomy from the Moon, I'm on the hook to investigate this question.

Everyone to the south pole

By 2035 – just 10 or so years away – American and Chinese rockets could be carrying humans to long-term lunar bases.

Both bases are planned for the same small areas near the south pole because of the near-constant solar power available in this region and the rich source of water that scientists believe could be found in the Moon's darkest regions nearby.

Unlike the Earth, the Moon is not tilted relative to its path around the Sun. As a result, the Sun circles the horizon near the poles, almost never setting on some crater rims. There, the never-setting Sun casts long shadows over nearby craters, hiding their floors from direct sunlight for the past 4 billion years, 90% of the age of the solar system.

These craters are basically pits of eternal darkness. And it's not just dark down there, it's also cold: below -418 degrees Fahrenheit (-250 degrees Celsius). It's so cold that scientists predict that water in the form of ice at the bottom of these craters – likely brought by ancient asteroids colliding with the Moon's surface – will not melt or evaporate away for a very long time.

Surveys from lunar orbit suggest that these craters, called permanently shadowed regions, could hold half a billion tons of water.

The constant sunlight for solar power and proximity to frozen water makes the Moon's poles attractive for human bases. The bases will also need water to drink, wash up and grow crops to feed hungry astronauts. It is hopelessly expensive to bring long-term water supplies from Earth, so a local watering hole is a big deal.

Telescopes on the Moon

For decades, astronomers had ignored the Moon as a potential site for telescopes because it was simply infeasible to build them there. But human bases open up new opportunities.

The radio-sheltered far side of the Moon, the part we never see from Earth, makes recording very low frequency radio waves accessible. These signals are likely to contain signatures of the universe's “Dark Ages,” a time before any stars or galaxies formed.

Astronomers could also put gravitational wave detectors at the poles, since these detectors are extraordinarily sensitive, and the Moon's polar regions don't have earthquakes to disturb them as they do on Earth.

A lunar gravitational wave detector could let scientists collect data from pairs of black holes orbiting each other very closely right before they merge. Predicting where and when they will merge tells astronomers where and when to look for a flash of light that they would otherwise miss. With those extra clues, scientists could learn how these black holes are born and how they evolve.

The cold at the lunar poles also makes infrared telescopes vastly more sensitive by shifting the telescopes' black body radiation to longer wavelengths. These telescopes could give astronomers new tools to look for life on Earth-like planets beyond the solar system.

And more ideas keep coming. The first radio antennae are scheduled to land on the far side next year.

Conflicting interests

But the rush to build bases on the Moon could interfere with the very conditions that make the Moon so attractive for research in the first place. Although the Moon's surface area is greater than Africa's, human explorers and astronomers want to visit the same few kilometer-sized locations.

But activities that will help sustain a human presence on the Moon, such as mining for water, will create vibrations that could ruin a gravitational wave telescope.

Also, many elements found on the Moon are extremely valuable back on Earth. Liquid hydrogen and oxygen make precious rocket propellant, and helium-3 is a rare substance used to improve quantum computers.

But one of the few places rich in helium-3 on the Moon is found in one of the most likely places to put a far-side, Dark Ages radio telescope.

Finally, there are at least two internet and GPS satellite constellations planned to orbit the Moon a few years from now. Unintentional radio emissions from these satellites could render a Dark Ages telescope useless.

The time is now

But compromise isn't out of the question. There might be a few alternative spots to place each telescope.

In 2024, the International Astronomical Union put together the working group Astronomy from the Moon to start defining which sites astronomers want to preserve for their work. This entails ranking the sites by their importance for each type of telescope and beginning to talk with a key United Nations committee. These steps may help astronomers, astronauts from multiple countries and private interests share the Moon.

Martin Elvis, Senior Astrophysicist, Smithsonian Institution

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Ambuj Tewari, University of Michigan

Over 100 years ago, Alexander Graham Bell asked the readers of National Geographic to do something bold and fresh – “to found a new science.” He pointed out that sciences based on the measurements of sound and light already existed. But there was no science of odor. Bell asked his readers to “measure a smell.”

Today, smartphones in most people's pockets provide impressive built-in capabilities based on the sciences of sound and light: voice assistants, facial recognition and photo enhancement. The science of odor does not offer anything comparable. But that situation is changing, as advances in machine olfaction, also called “digitized smell,” are finally answering Bell's call to action.

Research on machine olfaction faces a formidable challenge due to the complexity of the human sense of smell. Whereas human vision mainly relies on receptor cells in the retina – rods and three types of cones – smell is experienced through about 400 types of receptor cells in the nose.

Machine olfaction starts with sensors that detect and identify molecules in the air. These sensors serve the same purpose as the receptors in your nose.

But to be useful to people, machine olfaction needs to go a step further. The system needs to know what a certain molecule or a set of molecules smells like to a human. For that, machine olfaction needs machine learning.

Applying machine learning to smells

Machine learning, and particularly a kind of machine learning called deep learning, is at the core of remarkable advances such as voice assistants and facial recognition apps.

Machine learning is also key to digitizing smells because it can learn to map the molecular structure of an odor-causing compound to textual odor descriptors. The machine learning model learns the words humans tend to use – for example, “sweet” and “dessert” – to describe what they experience when they encounter specific odor-causing compounds, such as vanillin.

However, machine learning needs large datasets. The web has an unimaginably huge amount of audio, image and video content that can be used to train artificial intelligence systems that recognize sounds and pictures. But machine olfaction has long faced a data shortage problem, partly because most people cannot verbally describe smells as effortlessly and recognizably as they can describe sights and sounds. Without access to web-scale datasets, researchers weren't able to train really powerful machine learning models.

However, things started to change in 2015 when researchers launched the DREAM Olfaction Prediction Challenge. The competition released data collected by Andreas Keller and Leslie Vosshall, biologists who study olfaction, and invited teams from around the world to submit their machine learning models. The models had to predict odor labels like “sweet,” “flower” or “fruit” for odor-causing compounds based on their molecular structure.

The top performing models were published in a paper in the journal Science in 2017. A classic machine learning technique called random forest, which combines the output of multiple decision tree flow charts, turned out to be the winner.

I am a machine learning researcher with a longstanding interest in applying machine learning to chemistry and psychiatry. The DREAM challenge piqued my interest. I also felt a personal connection to olfaction. My family traces its roots to the small town of Kannauj in northern India, which is India's perfume capital. Moreover, my father is a chemist who spent most of his career analyzing geological samples. Machine olfaction thus offered an irresistible opportunity at the intersection of perfumery, culture, chemistry and machine learning.

Progress in machine olfaction started picking up steam after the DREAM challenge concluded. During the COVID-19 pandemic, many cases of smell blindness, or anosmia, were reported. The sense of smell, which usually takes a back seat, rose in public consciousness. Additionally, a research project, the Pyrfume Project, made more and larger datasets publicly available.

Smelling deeply

By 2019, the largest datasets had grown from less than 500 molecules in the DREAM challenge to about 5,000 molecules. A Google Research team led by Alexander Wiltschko was finally able to bring the deep learning revolution to machine olfaction. Their model, based on a type of deep learning called graph neural networks, established state-of-the-art results in machine olfaction. Wiltschko is now the founder and CEO of Osmo, whose mission is “giving computers a sense of smell.”

Recently, Wiltschko and his team used a graph neural network to create a “principal odor map,” where perceptually similar odors are placed closer to each other than dissimilar ones. This was not easy: Small changes in molecular structure can lead to large changes in olfactory perception. Conversely, two molecules with very different molecular structures can nonetheless smell almost the same.

Such progress in cracking the code of smell is not only intellectually exciting but also has highly promising applications, including personalized perfumes and fragrances, better insect repellents, novel chemical sensors, early detection of disease, and more realistic augmented reality experiences. The future of machine olfaction looks bright. It also promises to smell good.

Ambuj Tewari, Professor of Statistics, University of Michigan

This article is republished from The Conversation under a Creative Commons license. Read the original article.