Amazing Science

The odds of life spreading between the worlds of the newly-discovered seven-planet TRAPPIST-1 system are up to 1,000 times greater than in our own solar system. That’s the conclusion of a new analysis posted March 2 to the arXiv, an online repository of scientific papers.

The idea that simple organisms could accidentally travel between planets is known as panspermia. “Imagine one planet has life, and then you have a meteorite or asteroid impact that ejects some rock into space,” says mathematical physicist and lead author Manasvi Lingam of Harvard University in Cambridge, Massachusetts. “If these rocks are captured by a different planet, they could spawn life there.”

Panspermia has ancient origins, dating back to the Greek philosopher Anaxagoras in 500 B.C., who believed that cosmic “life seeds” brought organisms to Earth. The first detailed scientific treatment came in 1908, when Swedish chemist Svante Arrhenius wrote a book arguing that bacterial spores could have bounded from one planet to another. A modern-day version of the hypothesis briefly gained popularity in 1996 when a Martian meteorite found in Antarctica seemed to bear signs of microbial fossils—a claim ultimately rejected by most researchers.

Announced last month, TRAPPIST-1 is the newest exoplanetary system to spur excitement among astronomers. Orbiting the cool red dwarf star are seven rocky worlds ranging in size from slightly larger than Mars to a bit bigger than Earth. Three of them are in the star’s habitable zone, meaning liquid water could theoretically exist on their surface—though because red dwarf stars produce more flares and harsh radiation than sun-like stars, some scientists think life is unlikely on the surrounding planets. Others disagree.

Because the star TRAPPIST-1 is smaller and dimmer than our sun, its habitable zone encompasses a closer-in region—the three potentially habitable planets take only six, nine, and 12 days, respectively, to go around their parent star. The planets are much closer to each other as well. The two inner potentially habitable planets, for instance, are 30 times closer than Venus is to Earth, while the two farther ones are 65 times closer than Earth and Mars.

Applying models from the field of ecology, the researchers presuppose that the microbial transfer is similar to species jumping between islands. If the islands are closer together, a larger number of species could potentially travel between them. Hence, by analogy, it’s possible that many species might have made it from one planet in the TRAPPIST-1 system to another.

“Of course, this is only an analogy and not an exact match,” says Lingam. “But we know that greater biodiversity in any ecosystem also implies greater stability. If you have more species being transferred to the new planet, there’s less of a chance that they’ll die.”

Many unknowns remain, including just how long microbes can survive in space, whether or not they would be viable after traversing an atmosphere and crash-landing on a surface, and if the habitat of one world is similar enough to its sibling planets to allow such organisms to flourish.

The TRAPPIST-1 system is well-positioned to answer at least some of these; its exoplanets all eclipse in front of their parent star, meaning that telescopes here on Earth can capture starlight filtering through any potential planetary atmospheres. Such observations could reveal signs of vegetation—plants on Earth preferentially absorb red light, for example, giving our planet a “red edge” as seen from far away—or other biosignature molecules. If the TRAPPIST-1 worlds display similar signals, it could imply that panspermia occurred.

“What’s exciting to me more than anything is that this hypothesis is testable. This is no longer in the realm of theory,” says astronomer Rory Barnes of the University of Washington in Seattle, who was not involved in the work. Current and upcoming telescopes might be up the challenge, though it will likely take a large new space-based observatory such as the proposed Large UV/Optical/Infrared Surveyor (LUVOIR), which wouldn’t be launched until the 2030s, to answer such questions in detail.

Time crystals are one of the most interesting things physics has come up with in recent months, because they point to a whole new world of 'non-equilibrium' phases that are entirely different from anything scientists have studied in the past. 

For decades, we've been studying matter, such as metals and insulators, that's defined as being 'in equilibrium' - a state where all the atoms in a material have the same amount of heat. Now it looks like time crystals are the first example of the hypothesized but unstudied 'non-equilibrium' state of matter, and they could revolutionize how we store and transfer information via quantum systems. "It shows that the richness of the phases of matter is even broader [than we thought]," physicist Norman Yao from the University of California, Berkeley, who published the blueprint in January, told Gizmodo.

"One of the holy grails in physics is understanding what types of matter can exist in nature. [N]on-equilibrium phases represent a new avenue different from all the things we've studied in the past." First proposed by Nobel Prize-winning theoretical physicist Frank Wilczek back in 2012, time crystals are hypothetical structures that appear to have movement even at their lowest energy state, known as a ground state.

Usually when a material enters its ground state - also referred to as the zero-point energy of a system - movement should theoretically be impossible, because it would require it to expend energy. But Wilczek envisioned an object that could achieve everlasting movement while in its ground state by periodically switching the alignment of atoms inside the crystal over and over again - out of the ground state, back again, and repeat.

Let's be clear - this isn't a perpetual motion machine, because there's zero energy in the system. But the hypothesis did initially seem unlikely for another reason. It hinted at a system that breaks one of the most fundamental assumptions of our current understanding of physics - time-translation symmetry, which states that the laws of physics are the same everywhere and at all times.

As Daniel Oberhaus explains for Motherboard, time-translation symmetry is the reason why it would be impossible to flip a coin at one moment and have the odds of heads or tails at 50/50, but then the next time you flip it, the odds are suddenly 70/30.

MIT physicists have created a new form of matter, a supersolid, which combines the properties of solids with those of superfluids.

By using lasers to manipulate a superfluid gas known as a Bose-Einstein condensate, the team was able to coax the condensate into a quantum phase of matter that has a rigid structure — like a solid — and can flow without viscosity — a key characteristic of a superfluid. Studies into this apparently contradictory phase of matter could yield deeper insights into superfluids and superconductors, which are important for improvements in technologies such as superconducting magnets and sensors, as well as efficient energy transport. The researchers report their results this week in the journal Nature.

“It is counterintuitive to have a material which combines superfluidity and solidity,” says team leader Wolfgang Ketterle, the John D. MacArthur Professor of Physics at MIT. “If your coffee was superfluid and you stirred it, it would continue to spin around forever.” 

Physicists had predicted the possibility of supersolids but had not observed them in the lab. They theorized that solid helium could become superfluid if helium atoms could move around in a solid crystal of helium, effectively becoming a supersolid. However, the experimental proof remained elusive.

The team used a combination of laser cooling and evaporative cooling methods, originally co-developed by Ketterle, to cool atoms of sodium to nanokelvin temperatures. Atoms of sodium are known as bosons, for their even number of nucleons and electrons. When cooled to near absolute zero, bosons form a superfluid state of dilute gas, called a Bose-Einstein condensate, or BEC. 

Ketterle co-discovered BECs — a discovery for which he was recognized with the 2001 Nobel Prize in physics. “The challenge was now to add something to the BEC to make sure it developed a shape or form beyond the shape of the ‘atom trap,’ which is the defining characteristic of a solid,” explains Ketterle.

Next time a flu epidemic hits your area, putting everyone in bed, rejoice: it may mean that the recession is over. A new paper by Jérôme Adda, a scholar at Bocconi’s Department of Economics (Economic Activity and the Spread of Viral Diseases: Evidence from High Frequency Data, in The Quarterly Journal of Economics (2016) 131 (2): 891-941. doi: 10.1093/qje/qjw005), highlights the connection between the business cycle and the spread of viruses: “We find that epidemics spread faster during economic booms”, he writes. “During booms more people are traveling, which increases inter-personal contacts and the spread of diseases”.
 
The paper underlines the role of transportation in the expansion of epidemics and rates not only the impact of policies that limit interpersonal contacts, but also their cost-effectiveness, through a cost-benefit analysis. “Shutting down schools”, Adda says, “proves to be effective in curbing the spread of viral diseases, provided that the incubation period is short enough, but is not an efficient measure, unless it is offset by a corresponding number of extra school days in other periods of the year”. Closing transportation networks turns out to be cost-effective only in very extreme cases. “In general, such measures are efficient only when epidemics are more deadly than the typical flu”, Adda says, “as in the 2009 swine flu outbreak”.
 
Adda has reached his conclusions thanks to a unique dataset of high frequency data collected by French authorities since the ‘80s, detailing the weekly evolution of the incidence of three major viral diseases (the flu, gastro-enteritis and chickenpox) across geographical locations and age groups. He uses quasi-experiments to determine the effects of school shutdowns and transportation network closures, observing how spreading patterns change during vacations (mandated school shutdowns) and transportation strikes. “Furthermore, data confirm that the opening of new high-speed train lines makes viruses spread faster”, Adda says.
 
The most striking result of the study is the strength of the transportation effect, but its results may also help authorities shape effective measures in case of an outbreak. Longer school vacations in the winter, when viruses are more likely to spread, and shorter in the summer definitely make sense.
 
“We’ve seen that the effectiveness of some policies depends on the characteristics of different diseases”, Adda says, “and this is valuable information in an environment exposed to the emergence of new viruses and the resurgence of old ones, due to lack of vaccinations in immigrant populations and anti-vaxxers or to global warming that, as probably happened last August in Siberia, can cause the melting of permafrost and the release of frozen, extinct pathogens”.

Dana-Farber scientists report that a compound able to reverse the allegiance of innate immune system cells – turning them from tumor enablers into tumor opponents – caused breast tumors in mice to shrink and withdraw from distant metastases.

In a new study in the journal Nature, Dana-Farber Cancer Institute scientists report that a compound able to reverse the allegiance of innate immune system cells -- turning them from tumor enablers into tumor opponents -- caused breast tumors in mice to shrink and withdraw from distant metastases. When combined with chemotherapy or another immunotherapy, the new compound significantly extended the period of tumor remission.

The findings suggest a way to bring the full repertoire of the immune system to bear on cancer in humans, the authors said.

"Most current forms of cancer immunotherapy influence the behavior of T cells -- white blood cells that are part of the adaptive immune system -- by 'teaching' them to attack tumor cells or removing impediments to such an attack," said the study's lead author Jennifer Guerriero, PhD, of Dana-Farber. "This strategy has been effective against several types of cancer, but generally only a subset of patients benefit. We wanted to see if harnessing both arms of the immune system could produce superior results."

The targets of the new study were innate immune system cells known as tumor-associated macrophages (TAMs). They're often found deeply embedded within tumors, but although they're part of the immune system -- the body's defense against disease -- they frequently promote tumor growth. In doing so, they're responding to cues issued by the tumor itself.

The roles that macrophages play -- whether protective or destructive -- depend on signals from their environment. In wound healing, for example, macrophages marshal the elements of the immune system that clear away damaged tissue and restore the affected area. Tumor macrophages manage to hijack some of these supportive functions for their own purposes. Not without reason is cancer sometimes referred to as a wound that doesn't heal.

In previous research, the Dana-Farber scientists and their colleagues showed that a compound known as TMP195 could convert TAMs from aiding tumor growth to organizing an attack on it. A selective, first-in-class, class IIa HDAC inhibitor, TMP195 switches the macrophage response by altering gene activity within TAMs.

In this current study, TMP195 sharply reduced the rate of tumor growth in mice with breast tumors, researchers found. They next combined TMP195 with various chemotherapy regimens and with a form of immunotherapy known as T-cell checkpoint blockade. In both cases, the combinations produced longer-lasting remissions of breast cancer than TMP195 alone did.

"Once they've undergone conversion, macrophages act as the orchestrators of the immune system attack on the tumor," said Anthony Letai, MD, PhD, of Dana-Farber, co-senior author of the study with Michael A. Nolan, PhD, of GlaxoSmithKline. "Our findings demonstrate that class IIa HDAC inhibitors can be an effective way of harnessing the anti-tumor potential of macrophages in cancer therapy.

"The future of cancer treatment is likely to involve combinations of therapies that act on both the innate and adaptive arms of the immune system, as well as therapies, such as chemotherapy, radiation therapy, or targeted therapy, that act on cancer cells themselves," he continued. "The ability to engage the innate immune system is an exciting new front in cancer therapy."

A quantum microscope that uses a sensor built from diamonds could allow researchers to study such nanoscale mysteries as how DNA folds in a cell, why drugs work or how bacteria metabolize metals. Crucially, the microscope can image individual ions in a solution and reveal biochemical reactions as they occur — without interfering in the process. The team behind the system described the results in a 14 February preprint on the arXiv server1.

Researchers have long wanted an imaging system for molecular structures that works like hospital magnetic resonance imaging (MRI) machines, which reveal structures inside the human body without harming them. The idea behind a quantum MRI — which images at the quantum level using electron spins — is to do the same for chemical reactions including those involving metal ions. Current magnetic resonance techniques can only reveal structures measuring 10 micrometers or more, and the only way to detect metal ions inside a cell is to add reactive chemicals or freeze the cell so it can be imaged under powerful microscopes — procedures that kill the cell.

A hospital MRI machine works by placing a patient inside a magnetic field, such that protons in the body's atoms align with the machine's magnet. The machine then sends radio pulses through the body area being imaged, which knocks the protons out of alignment. When these pulses are switched off, the protons realign and emit electromagnetic waves at a particular frequency. If the frequency emitted by the body's tissues matches that of sensors in the machine, the two frequencies will resonate like guitar strings tuned to the same note. The machine uses this resonance to reconstruct an image of the body.

In January 1958, two medical officers at Porton Down, Britain’s military science facility, exposed their forearms to 50-microgram droplets of a substance called VX, which was a new, fast-acting nerve agent that could kill by seeping through the skin.

VX, short for “venomous agent X,” is tasteless, odorless and causes uncontrollable muscle contractions that eventually stop a person’s breathing within minutes. That experiment in 1958, according to University of Kent historian Ulf Schmidt, was perhaps the first human test of VX in the Western world.

Though VX is outlawed under the 1997 Chemical Weapons Convention, it was used to kill North Korean leader Kim Jong-un’s half-brother, Kim Jong-nam, in Malaysia. North Korea maintains the third largest stockpile of chemical weapons, trailing only the United States and Russia, according to the Nuclear Threat Initiative project. As such, South Korea has pinned blame for the attack on the North Korean government, and the use of a banned weapon may increase pressure on the international community to formulate a response.

Given these recent developments, it shouldn’t come as a surprise that this lethal chemical agent has a checkered, infamous past.

In the mid-1990s, the Japanese cult Aum Shinrikyo used VX in attempts to kill three people—one was successful. In 1969, the U.S. Army admitted that VX was responsible for the deaths of 6,000 sheep in Utah. But VX was trouble from the very start. You see, that first first-of-its-kind human trial in 1958 at Porton Down was actually an unauthorized experiment conducted in shadows, as Schmidt revealed in his 2015 book “Secret Science”.

The universe has come into sharper focus with the release this week of new images from the one of the largest telescopes in the world. A multinational collaboration led by the National Astronomical Observatory of Japan that includes Princeton University scientists has published a "cosmic census" of a large swath of the night sky containing roughly 100 million stars and galaxies, including some of the most distant objects in the universe. These high-quality images allow an unprecedented view into the nature and evolution of galaxies and dark matter.

These images and accompanying data were collected using a digital optical-imaging camera on the Subaru Telescope, located at the Mauna Kea Observatory in Hawaii. The camera, known as Hyper Suprime-Cam, is mounted directly in the optical path, at the "prime focus," of the Subaru Telescope. A single image from the camera captures an amount of sky equal to the area of about nine full moons.

The project, known as the Hyper Suprime-Cam Subaru Strategic Program, is led by the National Astronomical Observatory of Japan (NAOJ) in collaboration with the Kavli Institute for the Physics and Mathematics of the Universe in Japan, the Academia Sinica Institute of Astronomy and Astrophysics in Taiwan, and Princeton University.

The release includes data from the first one-and-a-half years of the project, consisting of 61.5 nights of observations beginning in 2014. The project will take 300 nights over five to six years. The data will allow researchers to look for previously undiscovered galaxies and to search for dark matter, which is matter that neither emits nor absorbs light but which can be detected via its effects on gravity. A 2015 study using Hyper Suprime-Cam sur-veyed 2.3 square degrees of sky and found gravitational signatures of nine clumps of dark matter, each weighing as much as a galaxy cluster (Miyazaki et al., 2015).

The current data release covers about 50 times more sky than was used in that study, showing the potential of these data to reveal the statistical properties of dark matter.

The survey consists of three layers: a Wide survey that will eventually cover an area equal to 7000 full moons, or 1400 square degrees; a Deep survey that will look farther into the universe and encompass 26 square degrees; and an UltraDeep survey that will cover 3.5 square degrees and penetrate deep into space, allowing observations of some of the most distant galaxies in the universe. The surveys use optical and near infrared wavelengths in five broad wavelength bands (green, red, infrared, z, and y) and four narrow-band filters. In the multi-band images, the images are extremely sharp, with star images only 0.6 to 0.8 arcseconds across.

Scientists have made the most detailed observation yet of a black hole outflow, from the active galaxy IRAS 13224-3809. The outflow's temperature changed on time scales of less than an hour -- hundreds of times faster than ever seen before. The rapid fluctuations in the outflow's temperature also indicated that the outflow was responding to X-ray emissions from the accretion disk, a dense zone of gas and other materials that surrounds the black hole.

Health data collected entirely from smartphones can be reliable, research from Mount Sinai Hospital claims. The researchers involved found that Apple’s ResearchKit platform and an app for asthma were fairly accurate when compared to existing patient studies.

Finding and recruiting participants is a big hurdle for medical studies. In recent years, people have started collecting health data from smartphones, which seems sensible given how common smartphones are. But this raises questions about whether data gathered this way can be trusted.

Today’s study, published today in the journal Nature Biotechnology, suggests that health care apps may be reliable, at least in regards to asthma. This is good news since smartphone usage is only increasing — there are supposed to be 6 billion smartphones used worldwide by 2020 — and collecting reliable health data from them could be very good for research.

Apple launched ResearchKit, a software medical platform, in 2015. It helps researchers recruit participants for studies; participants can enroll in trials and take surveys or provide other data. Early research partners included big names like the University of Oxford, Stanford Medicine, and the Dana-Farber Cancer Institute. The asthma mobile app from today’s study was one of the five disease-specific apps that Apple launched with the initial release of ResearchKit.

In today’s study, nearly 50,000 iPhone users downloaded the asthma app. Of these, about 7,600 people enrolled in the six-month study after completing the consent form. People in the study took surveys on how they treated their asthma; the app also provided information about location and air quality.

The scientists then looked at how this patient-reported data measured up when compared to external factors. For example, around the time there were fires in Washington state, patients in the area reported worse asthma symptoms, as might be expected, suggesting the data was fairly reliable. They were similarly able to correlate data related to heat and pollen.

University of Miami Rosenstiel School of Marine and Atmospheric Science scientist Mark Donelan and his Norwegian Meteorological Institute colleague captured new information about extreme waves, as one of the steepest ever recorded passed by the North Sea Ekofisk platforms in the early morning hours of Nov. 9 2007.

Within the first hour of the day, the Andrea wave passed by a four-point square array of ocean sensors designed by the researchers to measure the wavelength, direction, amplitude and frequency of waves at the ocean surface. Using the information from the wave set—a total of 13,535 individual waves—collected by the system installed on a bridge between two offshore platforms, the researchers took the wave apart to examine how the components came together to produce such a steep wave.

The data from the 100-meter wide “wall of water” moving at 40 miles per hour showed that Andrea may have reached heights greater than the recorded height of 49 feet above mean sea level. They also found that rogue waves are not rare as previously thought and occur roughly twice daily at any given location in a storm. The findings showed that the steeper the waves are, the less frequent their occurrence, which is about every three weeks at any location for the steepest rogues.

The Andrea crest height was 1.63 times the significant height (average height of the one third highest waves).

Optimal focusing of the Andrea wave showed that the crest could have been even higher and limited by breaking at 1.7 times the significant height. This establishes the greatest height rogues can reach for any given (or forecasted) significant height. “Rogue waves are known to have caused loss of life as well as damage to ships and offshore structures,” said Mark Donelan, professor emeritus of ocean sciences at the UM Rosenstiel School. “Our results, while representing the worst-case rogue wave forecast, are new knowledge important for the design and safe operations for ships and platforms at sea.”

The study, titled “The Making of the Andrea Wave and other Rogues,” was published in the March 8 issue of the journal Scientific Reports. The authors include: Donelan and Anne-Karin Magnusson from the Norwegian Meteorological Institute. The work was partly performed within the ExWaMar project (ID 256466/O80) funded by the Norwegian Research Council. ConocoPhillips provided the wave data.

"This one lineage of dinosaurs that became ever-more-birdy over time happened to hit on something successful." Bird evolution is like a Legend of Zelda puzzle: the pieces may take a while to come together, but once they do, it's next-level.

A team in China has corrected genetic mutations in at least some of the cells in three normal human embryos using the CRISPR genome editing technique. The latest study is the first to describe the results of using CRISPR in viable human embryos, New Scientist can reveal.

While this study – which attempted to repair the DNA of six embryos in total – was very small, the results suggest CRISPR works much better in normal embryos than it did in previous tests on abnormal embryos that could not develop into children.

“It is encouraging,” says Robin Lovell-Badge of the Francis Crick Institute in London, who has contributed to several major reports on human genome editing. The numbers are far too low to make strong conclusions though, he cautions.

To get a 3-D shape from an ordinary polynomial takes a little doing. The first step is to run the polynomial dynamically — that is, to iterate it by feeding each output back into the polynomial as the next input. One of two things will happen: either the values will grow infinitely in size, or they’ll settle into a stable, bounded pattern. To keep track of which starting values lead to which of those two outcomes, mathematicians construct the Julia set of a polynomial. The Julia set is the boundary between starting values that go off to infinity and values that remain bounded below a given value. This boundary line — which differs for every polynomial — can be plotted on the complex plane, where it assumes all manner of highly intricate, swirling, symmetric fractal designs.

The researchers, Hoi-Kwan Lau et al., have published a paper on generalizing quantum machine learning to infinite dimensions in a recent issue of Physical Review Letters.

As the physicists explain, quantum machine learning is a new subfield within the field of quantum information that combines the speed of quantum computing with the ability to learn and adapt, as offered by machine learning.

One of the biggest advantages of having a quantum machine learning algorithm for continuous variables is that it can theoretically operate much faster than classical algorithms. Since many science and engineering models involve continuous variables, applying quantum machine learning to these problems could potentially have far-reaching applications.

"Our work demonstrates the ability to take advantage of photonics to perform machine learning tasks on a quantum computer that could far exceed the speed of any conventional computer," coauthor George Siopsis at the University of Tennessee told Phys.org. "Quantum machine learning also offers potential advantages such as lower energy requirements owing to the ability to store more information per qubit, and a very low cost per qubit compared to other technologies."

Most quantum machine learning algorithms developed so far work only with problems involving discrete variables. Applying quantum machine learning to continuous-variable problems requires a very different approach.

To do this, the physicists had to develop a new set of tools that work with continuous variables. This involves replacing the logic gates that are used for discrete-variable states with physical gates, which work for continuous-variable states. Building up from these basic building blocks of the algorithm, the scientists then developed new methods that power the quantum machine learning problems, called subroutines, which are represented by matrices and vectors.

Although the results of the study are purely theoretical, the physicists expect that the new algorithm for continuous variables could be experimentally implemented using currently available technology. The implementation could be done in several ways, such as by using optical systems, spin systems, or trapped atoms. Regardless of the type of system, the implementation would be challenging. For example, an optical implementation that the scientists outlined here would require some of the latest technologies, such as "cat states" (a superposition of the "0" and "1" states) and high rates of squeezing (to reduce quantum noise).

In the future, the scientists hope to further investigate how continuous-variable quantum machine learning can be extended to replicate some of the latest results involving discrete variables. Another interesting avenue to pursue is a hybrid approach, which would combine the methods of both discrete and continuous variables in a single algorithm.

Combining computational with experimental approaches, researchers identify 12 new materials with potential use in solar fuels generators.

Researchers at Caltech and Lawrence Berkeley National Laboratory (Berkeley Lab) have -- in just two years -- nearly doubled the number of materials known to have potential for use in solar fuels. They did so by developing a process that promises to speed the discovery of commercially viable solar fuels that could replace coal, oil, and other fossil fuels.

Solar fuels, a dream of clean-energy research, are created using only sunlight, water, and carbon dioxide (CO2). Researchers are exploring a range of target fuels, from hydrogen gas to liquid hydrocarbons, and producing any of these fuels involves splitting water.

Each water molecule is comprised of an oxygen atom and two hydrogen atoms. The hydrogen atoms are extracted, and then can be reunited to create highly flammable hydrogen gas or combined with CO2 to create hydrocarbon fuels, creating a plentiful and renewable energy source. The problem, however, is that water molecules do not simply break down when sunlight shines on them -- if they did, the oceans would not cover most of the planet. They need a little help from a solar-powered catalyst.

To create practical solar fuels, scientists have been trying to develop low-cost and efficient materials, known as photoanodes, that are capable of splitting water using visible light as an energy source. Over the past four decades, researchers identified only 16 of these photoanode materials. Now, using a new high-throughput method of identifying new materials, a team of researchers led by Caltech's John Gregoire and Berkeley Lab's Jeffrey Neaton and Qimin Yan have found 12 promising new photoanodes.

A paper about the method and the new photoanodes appears the week of March 6 in the online edition of the Proceedings of the National Academy of Sciences. The new method was developed through a partnership between the Joint Center for Artificial Photosynthesis (JCAP) at Caltech, and Berkeley Lab's Materials Project, using resources at the Molecular Foundry and the National Energy Research Scientific Computing Center (NERSC).

"This integration of theory and experiment is a blueprint for conducting research in an increasingly interdisciplinary world," says Gregoire, JCAP thrust coordinator for Photoelectrocatalysis and leader of the High Throughput Experimentation group. "It's exciting to find 12 new potential photoanodes for making solar fuels, but even more so to have a new materials discovery pipeline going forward."

"What is particularly significant about this study, which combines experiment and theory, is that in addition to identifying several new compounds for solar fuel applications, we were also able to learn something new about the underlying electronic structure of the materials themselves," says Neaton, the director of the Molecular Foundry.

Increasingly crowded imaging & diagnostics: 19 out of the 24 companies under imaging & diagnostics raised their first equity funding round since January 2015 (this includes seed or Series A rounds, as well as a first round raised by stealth startup Imagen Technologies). In 2014, Butterfly Networks raised a $100M Series C, backed by Aeris Capital and Stanford University. This was one of the largest equity rounds to an AI in healthcare company, after China-based iCarbonX’s $154M mega-round and two $100M+ raises by oncology-focused Flatiron Health.

Remote patient monitoring: London-based Babylon Health, backed by investors including Kinnevik and Google-owned DeepMind Technologies, raised a $25M Series A round in 2016 to develop an AI-based chat platform. New York-based AiCure raised $12.3M in Series A funding from investors including Biomatics Capital Partners, New Leaf Venture Partners, Pritzker Group Venture Capital, and Tribeca Venture Partners, for the use of artificial intelligence to ensure patients are taking their medications. California-based Sense.ly has developed a virtual nursing assistant, Molly, to follow up with patients post-discharge. The company claims Molly gives clinicians “20% of their day back.” Sentrian, backed by investors including Frost Data Capital, analyzes biosensor data and sends patient-specific alerts to clinicians.

Core AI companies bring their algorithms to healthcare: Core AI startup Ayasdi, which has developed a machine intelligence platform based on topological data analysis, is bringing its solutions to healthcare providers for applications including patient risk scoring and readmission reduction. Other core AI startups looking at healthcare include H2O.ai and Digital Reasoning Systems.

Top VCs: Khosla Ventures and Data Collective are the top VC investors in healthcare AI startups, and have backed 5 unique companies each. Khosla Ventures backed California-based Ginger.io, which focuses on patients with depression and anxiety; healthcare analytics platform Lumiata; Israel’s Zebra Medical Vision and California-based Bay Labs, which applies AI to medical imaging; as well as drug discovery startup Atomwise. Data Collective backed imaging & diagnostics startups Enlitic, Bay Labs and Freenome, analytics platform CloudmedX, and previously mentioned Atomwise.

Drug discovery is also gaining attention: Startups are using machine learning algorithms to reduce drug discovery times, and VCs have backed 6 out of the 9 startups on the map. Andreessen Horowitz recently seed funded twoXAR, developer of the DUMA drug discovery platform; Khosla Ventures and Data Collective backed Atomwise, which published its first findings of Ebola treatment drugs last year, and has also partnered with MERCK; Lightspeed Venture Partners invested in Numedii in 2013; Foundation Capital participated in 3 equity funding rounds to Numerate.

AI in oncology: IBM Watson Group-backed Pathway Genomics has recently started a research study for its new blood test kit, CancerIntercept Detect. The company will collect blood samples from high-risk individuals who have never been diagnosed with the disease to determine if early detection is possible. Other oncology-focused startups include Flatiron Health, Cyrcadia (wearable device), CureMetrix, SkinVision, Entopsis, and Smart Healthcare.