Mayo Clinic research findings indicate that the removal of senescent cells in aging mice improves cognitive ability in animals that already show signs of dementia. The results of these tests using senolytic drugs in aging mice appear in Aging Cell.
Senescent cells are those cells in the body that have ignored the order to expire. These cells exist in a suspended state. They are powerful enough to avoid the body’s signal to die, but they are not powerful enough to keep dividing. Instead, they linger, spewing out toxic chemicals. The cells and their toxic chemicals often are grouped together as senescence.
Many physical states, such as chronic inflammation, are linked to human cognitive decline. But recent studies also have examined the link between cognitive impairment and senescence. In this paper, Mayo researcher Diana Jurk, Ph.D., member of the Robert and Arlene Kogod Center on Aging, and her colleagues used a two-pronged approach to explore whether cognitive decline can be reversed. One approach focused on the genetic response to a drug ― pharmacogenomics ― while the other examined the problem from the perspective of drug administration ― pharmacologic.
While senescent cells have been previously identified in the brain, it is still unknown which cell types in the brain become senescent during aging. To answer this question, Dr. Jurk and her team used single-cell RNA sequencing, which provides gene expression information from thousands of cells. Using this method, they identified that during aging, senescence is more pronounced in microglia and oligodentrocyte progenitor cells.
They then aged genetically modified mice ― INK-ATTAC mice ― and used two senolytic methods to clear senescent cells: AP20187, which eliminates p16positive senescent cells or the cocktail of dasatinib and quercetin. Both methods significantly improved cognitive function in the mice, based on before and after tests.
The authors write that this finding in mice provides a proof of concept for future studies on senescent cell removal as a potential therapy for age-related cognitive impairment in patients. This finding also reinforced a 2018 Nature paper by Mayo Clinic authors which showed that clearance of senescent cells improved cognitive decline in a mouse model of Alzheimer’s disease, as well as previous work by Dr. Jurk and colleagues on senescent cells and anxiety.
Still to be explored are the questions of:
Mechanistically, how do senescent cells contribute to aging of the brain?
Which senescent cells were targeted since the therapy was systemic?
What effect did this intervention have on immune cells in the genetically modified model?
The authors note that additional tests of cognitive function would further validate their findings.
The research was supported by grants from Glenn Foundation for Medical Research, the National Institute on Aging and other National Institutes of Health agencies, the Biotechnology and Biological Sciences Research Council, the Ted Nash Long Life Foundation, The Academy of Medical Sciences, the Conner Group, Robert J. and Theresa W. Ryan, and the Noaber Foundation. The full author list can be found in the Aging Cell article.
Researchers from Massachusetts Institute of Technology and Mayo Clinic announce news of a proof-of-concept patch for surgical and emergency situations where stitches or staples would normally be used. Published in the journal Advanced Materials, the patch brings together three technological innovations into what may be a better way to close surgical incisions or tissue damage inside the body.
Dr. Nabzdyk is an anesthesiologist and critical care specialist. In addition to clinical expertise, he has experience working in the realm of biomaterials and discovery science. He began working on this project during a dual fellowship in cardiothoracic anesthesia and critical care medicine at Massachusetts General Hospital and Harvard Medical School in Boston.
As inspiration, the scientists took a trick from one of the world’s clingiest creatures — barnacles. Any tide pooler who has tried to pry a barnacle off a seaside rock can tell you: Don’t bother. Whether on a rocky shore, a whale or a ship, barnacles stay put despite tide and time. At the other end of the spectrum, who hasn’t experienced the frustration of washing and covering a small cut, only to have the sticky bandage immediately fail, sliding off if the area isn’t entirely dry? There’s a reason why stitches or staples are used in surgery where blood and bodily fluids abound. Christoph Nabzdyk, M.D., a Mayo Clinic anesthesiologist and critical care specialist, explains that even so, stitches and staples are to some degree antiquated technologies.
“You have tissue trauma from surgery or due to other insults, and you create more trauma using stitches or staples to approximate the tissue edges,” says Dr. Nabzdyk. “It has been the standard of care, but you’re effectively poking new holes and causing more damage. And it can be difficult if there’s tissue weakness or partial breakdown of tissues, or simply difficult to physically get to. A lot of patients have comorbidities that render them susceptible for impaired healing. So it would be nice to have a device that you can apply without added trauma and have some redundancy if one stitch fails in a suture line.”
Translating Barnacle Glue to a Surgical Patch
To avoid creating new holes and more trauma, in some cases surgeons can use surgical patching or technologies such as surgical glue. These are not new concepts, but it’s an arena that Dr. Nabzdyk and his colleagues in the lab of Professor Xuanhe Zhao, Ph.D., at Massachusetts Institute of Technology knew could use improvement. In a manuscript posted in December 2020, Dr. Nabzdyk and colleagues, describe how current products tend to be slow to adhere, weak in their ability to bond to tissue, too stiff for some tissues, or unable to be removed without damage to the skin underneath.
To address these issues, the team turned to barnacles.
Barnacles stick to pretty much anything in or near the ocean and come in more than 1,000 species. They feed using a feathery net that extends out from their hard shells when they are submerged in water, collecting a microscopic feast.
The researchers determined that when these crustaceans prepare to settle down, they release a fatty liquid that cleans the area where they will lie and repels water. They then deploy proteins that adhere to their new home, be it on a whale, a ship or a shoreline rock.
Based on this idea, the researchers developed a clear, thin patch that’s stiff when dehydrated but flexible in an environment that has moisture. Before use, the patch is a firm, clear rectangle made of three layers: one that hydrates and repels contaminants, one that’s sticky, and an oily layer that allows the patch to glide through the body and avoid sticking to anything before it reaches its destination. The oily side is designed to be closest to the damaged tissue, and the anticontamination layer is on the side of the delivery system, say a catheter, forceps, or a surgeon’s hand.
The anticontamination layer uses charged molecules called zwitterions to create a shell of water around the patch. This helps prevent blood or bacteria from sticking to the patch. The adhesive layer is made up of various polymers that serve different functions, and the surface texture is made up of polymer granules that form a sort of tread similar to a car tire.
On its way through the body, the oil layer helps the patch reach the site of the repair with less friction. Once there, pressure forces the oil layer away from the textured adhesive layer. Chemical bonds form between the adhesive and tissue. The layer on the opposite side of the adhesive is highly charged, which helps hydrate the patch, and prevents blood or bacteria from sticking to it. The connection also can be released using a fluid designed to break the chemical bonds without damage to the tissue. This image was created and provided for use by Sarah J. Wu, first author.
“This granulated polymer surface is important because it helps create an increased surface area and also allows for the protective fluid layer to stay in place while the patch is navigated through the blood,” says Sarah J. Wu, co-first author and a graduate student at Massachusetts Institute of Technology. Because the body has all kinds of natural lubrication, this gives the patch the ability to repel fluids that may stand in the way of forming adhesion.
“You can’t attach something to the liquid itself,” Wu says, “but you can eliminate the liquid by pushing it out through these microscopic channels and by applying pressure to expose the adhesive layer, allowing it to stick to a solid surface. Additionally, fluid is absorbed into the bioadhesive layer away from the target tissue surface to wick the contact area dry and promote the bonding process.”
In terms of the chemistry for the sticky layer, Hyunwoo Yuk, Ph.D., the other co-first author, explains that the adhesive layer contains reactive chemical groups that will nearly instantly form strong bonds with the surface of the body.
“They form strong bonds within seconds, and that is the key to the magic. It’s a pressure-trigger-activated benign chemical reaction on the surface between the adhesive layer and the tissue surface. The pressure application will displace the protective silicone oil phase that allows maneuvering of the patch to the site of deployment,” Dr. Yuk says.
This illustration, provided by the researchers, depicts the patch on a surgical stapler. The yellow layer is the silicone oil, which helps the patch move through the body to the place where it will be deployed
Finally, the layer closest to the skin contains silicone oil. This type of oil is widely used as a lubricant for medical devices. It’s made to stay in place until pressure — no more than what a person could apply with their hand — expels it from the textured adhesive, pushing the body fluids out before it. Then the adhesive is able to stick to the tissue. It’s moldable and breaks down over time.
Advance to Care
The new patch, which is in the early stages of development, is part of a suite of projects being developed by Dr. Nabzdyk and the Massachusetts Institute of Technology team. All the prototypes are in preclinical stages, but future clinical trials are planned across a wide spectrum of applications. One problem the patch seeks to solve is major arterial bleeding.
“I have seen a lot of patients with bleeding issues,” says Dr. Nabzdyk, “I see the challenges and hopefully ways that might move the field along.”
The patch does not require a patient’s blood to clot, which the team sees as a key advance.
“As you can imagine, bleeding can be so brisk that it will not stop by itself, and patients may also have added bleeding abnormalities,” he says. “During cardiac surgery patients receive strong blood thinners, so promoting coagulation of blood as mean to stop focal brisk bleeding is not a sufficient choice. But the patch functions based on a simple chemical reaction and is independent of coagulation.”
Another problem the patch takes on is how to seal up holes or tears in hollow organs.
“On a catheter, this could be used to seal a bleeding vessel or an aneurysm, or even in the gut; it could be used to seal a defect in the colon wall if, for instance, a biopsy created a perforation,” he says.
Be it injured lungs or bleeding vessels, Dr. Nabzdyk sees many uses for something like their experimental patch. The research team is developing it for use with current tools, such as a standard surgical stapler or balloon catheters that can be threaded through the body to the site of the problem.
During surgery, tools such as catheters could deliver a patch to the area in need of repair. Stitches or staples can be difficult to place or to keep in place if a patient has tissue weakness or a breakdown of tissues. A patch that wouldn’t add to the already present tissue trauma would be useful, says Dr. Nabzdyk
clinician with a biomedical research background, Dr. Nabzdyk bridges the clinical and the chemical engineering world. He and Mayo Clinic are both listed on the patent for this technology, as well as the researchers from Massachusetts Institute of Technology. And while Dr. Nabzdyk says 2020 was a difficult year for finding time to participate in research due to the increased ICU workload related to COVID-19, he was able to continue contributing to the project with help from Mayo colleagues.
“Mayo provides excellent clinical care, but also has an infrastructure to maximize people’s productivity and empower them. In the short time I’ve been here, I’ve met a lot of people who are willing to help build a research effort around such projects,” he says. “It’s been exhilarating because my co-authors are such gifted scientists. They’ve won prestigious awards for this other work, and witnessing their brilliance has been a great source of inspiration. Hopefully between our world-class institutions, we can bring this idea to fruition.”
In addition to Dr. Nabzdyk, other authors on the paper — all from Massachusetts Institute of Technology — are: Sarah J. Wu, Hyunwoo Yuk, Ph.D., Jingjing Wu, Ph.D., and Professor Xuanhe Zhao. Patents on this work are held by Sarah Wu, and Drs. Yuk, Nabzdyk, and Zhao. Full funding information is in the Advanced Materials article.
To read about other bioengineering efforts Mayo is involved in, see the recent news release on a new hydrogel.
Finding an effective antidepressant medication for people diagnosed with depression, also called major depressive disorder, is often a long and complex process of “try and try again” ― going from one prescription to the next until achieving a therapeutic response.
This complex disease, which affects more than 16 million people in the U.S., can cause symptoms of persistent emotional and physical problems, including sadness, irritability and loss of interest. In severe cases, suicidal thoughts are a risk.
Now, a computer algorithm developed by researchers within Mayo Clinic’s Center for Individualized Medicine and the University of Illinois at Urbana-Champaign could help clinicians accurately and efficiently predict whether a patient with depression will respond to an antidepressant.
The new research, published in Neuropsychopharmacology, represents a possible step forward in individualizing treatment for major depressive disorder. It also demonstrates a collaboration between computer scientists and clinicians who are using large datasets to address challenges of individualizing medicine practices of globally devastating diseases.
Overall, the algorithm was tested on 1,996 patients with depression, and the outcomes of whether patients would respond favorably to therapy were correctly predicted in over 72% of the patients.
Harnessing teamwork to enhance patient outcomes
In this study, Arjun Athreya, Ph.D., a computer scientist within Mayo Clinic’s Molecular Pharmacology and Experimental Therapeutics, and William Bobo, M.D., chair of Mayo Clinic Florida’s Psychiatry and Psychology, closely collaborated to find ways in which a clinical challenge of global significance could be modeled. They worked together to derive insights to augment routine clinical decision-making to enhance patient outcomes.
“The idea was to create a technology that serves as a reliable companion to a clinician at the point of care, as opposed to a technology that replaces their judgment,” explains Dr. Athreya. “This meant I had to embed myself in the practice enough to learn the challenges clinicians face as well as the needs of the patient to collectively transform those needs and challenges into an engineering technology.”
Putting artificial intelligence (AI) to the test
The new approach uses an AI framework, called Almond, to find patterns and unique characteristics in a patient’s genomic and clinical data. This allows for the right treatment to be selected or enables a treatment to be changed relatively soon after it is started, if the algorithm predicts a poor response.
“We used the algorithm to identify the specific depressive symptoms and thresholds of improvement that were predictive of antidepressant response by four weeks for a patient to achieve remission or response, or a nonresponse, by eight weeks.”
– Dr. Arjun Athreya
For the study, Drs. Athreya and Bobo, and their team, trained the algorithm by creating symptom profiles for nearly 1,000 patients with major depressive disorder who were starting treatment with selective serotonin reuptake inhibitors, often called SSRIs. These are the most commonly prescribed first-line antidepressants.
The team first stratified patients according to their depression severity to construct a graph. Then they identified the different ways that their depression changed after starting treatment. They found that some depressive symptoms were more helpful in predicting treatment outcomes than others. They also identified the levels of improvement needed in each treatment to have a good outcome.
“We used the algorithm to identify the specific depressive symptoms and thresholds of improvement that were predictive of antidepressant response by four weeks for a patient to achieve remission or response, or a nonresponse, by eight weeks,” explains Dr. Athreya.
Overall, the algorithm was tested on 1,996 patients with depression, and the outcomes of whether patients would respond favorably to therapy were correctly predicted in over 72% of the patients.
Algorithm allows health care providers, patients to individualize care
Dr. Bobo says that providing interpretable predictions could enhance clinical treatment of depression and reduce the time associated with multiple trials of ineffective antidepressants.
“The model generates output in a way that clinicians are able to easily assimilate, interpret and potentially use in the limited time they have in clinical visits with the patients.”
– Dr. William Bobo.
“Typically, clinicians initiate a therapy and patients come back after four weeks,” Dr. Bobo explains. “Then, based on the clinician’s judgment of improvement, they make their best guess as to what the outcome would be at eight weeks, and decide to either change the therapy or stay the course.”
Dr. Bobo says the study highlights the essential partnership between computer scientists and clinicians.
“The logic that clinicians apply follows certain steps, and this is what really intrigued Dr. Athreya, who abstracted this as both a computational problem and an engineering problem. We were coming at the same idea with different viewpoints that ultimately had a high degree of synergy,” Dr. Bobo explains.
“What we then did with the study was to ‘algorithmize’ the clinician’s thinking logic using probabilistic graphical models to formalize the predictions of eight-week outcomes by how severe the disease was prior to treatment and how much specific depressive symptoms had to have improved between baseline and four weeks,” Dr. Bobo says. “This way, the model generates output in a way that clinicians are able to easily assimilate, interpret and potentially use in the limited time they have in clinical visits with the patients.”
The researchers say the model could be beneficial in busy primary care practices and may hasten referrals for specialty mental health consultation if the predicted outcomes of treatment are nonresponses.
“We hope this study will help us pave the way forward for developing electronic tools that can help clinicians and patients make better decisions about their treatment at the earliest point in time possible after starting it,” says Dr. Athreya.
For a disease that is marked with high degrees of variability in treatment outcomes across patients, this accuracy marks a step in the right direction of individualizing therapy for depression ― with the opportunity to augment clinical measures with biological measures such as genomics, which the team already is working on as part of a broader study supported by the National Science Foundation.
Also, the team is prospectively validating its findings in routing practice at Mayo Clinic in Rochester and Mayo Clinic in Florida through a Transform the Practice Award (NCT04355650).
POSTECH professor Sung-Min Park’s research team is developing a fully automated glucose management system that goes beyond the limits
Diabetes is on the rise worldwide. It is a permanent condition that requires care over a life time. To help manage it, an artificial pancreas system, which automatically measures blood sugar levels to infuse the appropriate amount of insulin into the blood, has now become smarter thanks to AI learning.
A research team, led by Professor Sung-Min Park and Ph.D. candidate Seunghyun Lee and M.S. candidate Jiwon Kim of POSTECH’s Department of Convergence IT Engineering and Electrical Engineering, has newly developed a reinforcement learning (RL) based AI algorithm that calculates the amount of insulin needed for a diabetic patient and injects it automatically. These findings were published as a feature article in the latest issue of IEEE Journal of Biomedical and Health Informatics, an international journal on medical information science.
Patients with type 1 diabetes must inject insulin daily. One must check the amount of carbohydrates in the food ingested each time, calculate the proper amount of insulin, then inject the correct dosage before each meal. Though artificial pancreas systems on the market help with this process, there is still the hassle of having to input the meal intake in advance each time.
To eliminate this inconvenience, the research team added a pharmacological concept to reinforcement learning, widely known as the algorithm of AlphaGo. This method of AI algorithm achieved a mean glucose of 124.72 mg/dL and percentage time in the normal range of 89.56%. Even without inputting the meal intake, the policy showed performance comparable to that of conventional artificial pancreas.
“The fully automated artificial pancreas is like autonomous driving for the medical industry,” explained Professor Sung-Min Park. “The newly developed AI algorithm enables fully automated blood sugar control without the hassle of inputting meal or exercise information.” He added, “We expect this algorithm to be extended to other drug-based treatments.”
This study was conducted with the support from the Mid-career Researcher Program in Basic Research in Science and Engineering of the National Research Foundation of Korea.
Reference: S. Lee, J. Kim, S. W. Park, S. -M. Jin and S. -M. Park, “Toward a Fully Automated Artificial Pancreas System Using a Bioinspired Reinforcement Learning Design: In Silico Validation,” in IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 2, pp. 536-546, Feb. 2021. URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9115809&isnumber=9348958 doi: 10.1109/JBHI.2020.3002022
A trio of papers provide new insight into the composition and evolution of the surface of Venus, hidden beneath its caustic, high temperature atmosphere. Utilizing imaging from orbit using multiple wavelengths – six-band spectroscopy proposed as part of the VERITAS and EnVision missions – scientists can map the iron content of the Venusian surface and construct the first-ever geologic map.
“Previous missions have only imaged one wavelength, and used 30-year-old topographic data to correct the spectra. Moreover, they were based on theoretical ideas about what Venus spectra look like, at very high temperatures. So the prior data have all been fairly qualitative,” said M. Darby Dyar, a Senior Scientist at the Planetary Science Institute and author on three recent papers on the topic.
These papers are based on new data from the Planetary Spectroscopy Laboratory at German Aerospace Center Institute of Planetary Research in Berlin, where Dyar works with a team including Jörn Helbert, first author of “Deriving iron contents from past and future Venus surface spectra with new high-temperature laboratory emissivity data” that appears today in Science Advances. That lab is unique in the world in that it can acquire spectra of rocks and minerals at Venus conditions. The new data lay the groundwork for the next planned Venus missions in order to finally be able to determine the different rocks there.
“We know very little about the geology of the Venus surface. What little we know comes from Soviet landers in the 1970s and from radar data from the Magellan mission, which ended in 1996. Until recently, there were no orbital spectroscopic data for Venus – as are common on other planets – because Venus is covered by thick, CO2 clouds. Visible and near-infrared (VNIR) light cannot penetrate those clouds except in some very small windows in the NIR around a wavelength of 1 micron,” Dyar said.
“But now we have acquired spectra in our ‘Venus chamber’. We can sample those data to match the windows in the CO2 atmosphere that an orbiter might have,” Dyar said. “There are five windows, into which we can fit six spectral bands – the one that was used by Venus Express plus five more. A six-window spectrometer that we designed is being proposed as part of two missions: the US-led VERITAS mission and the ESA mission called EnVision.
“The new lab data allow us to develop machine learning algorithms that can measure the iron contents of surface rocks from orbit with high accuracy. This is important because key igneous rocks types have distinctive iron contents, so we’ll be able to distinguish basalt, andesite, dacite, and rhyolites on the surface. Knowledge of rock types informs our understanding of how the Venus surface evolved,” she said.
“Our Science Advances paper essentially ‘validates’ the new lab data by showing that they match spectra taken on the surface by the Soviet landers in the 1970s. They also allow us to measure the iron contents of the basalts at two of those soviet landing sites, providing modern chemical data for previously unknown rocks” Dyar said.
“The Geophysical Research Letters paper more explicitly shows how we can determine FeO contents of Venus surface rocks using just the information in those six spectral bands. This means that if those missions get selected, we will be able to make a geologic map of the Venus surface from orbit using the FeO contents of the rocks,” Dyar said. “And the Icarus paper looks at the issue of recent volcanism on the Venus surface and suggests that contrary to prior work conducted under non-Venus conditions, the surface of Venus alters surprisingly slowly in its caustic atmosphere. This means that the surface won’t be covered in a single mineral, as prior work has suggested. Our paper gives estimates for how fast the surface will alter, and in turn constrains the age of the Venus surface.”
Dyar’s work on this research was partially funded by NASA’s Planetary Instrument Concepts for the Advancement of Solar System Observations (PICASSO) program.
Featured image: This image of Venus is a composite of data from NASA’s Magellan spacecraft and Pioneer Venus Orbiter. Credit: NASA/JPL-Caltech
In the 1960s, an exotic phase of matter known as an excitonic insulator was proposed. Decades later, evidence for this phase was found in real materials. Recently, particular attention has centered on Ta2NiSe5 because an excitonic insulator phase may exist in this material at room temperatures. The substance is made up of the elements tantalum, nickel, and selenium, and has the potential to lead to breakthroughs in more power-efficient, faster computers.
Now, in a new Physical Review Letters study from Caltech, researchers have, for the first time, figured out how to “flip the bits” of the excitonic insulator found in Ta2NiSe5. Computers communicate using a binary language of 1s and 0s, which are also called bits. For computers to work, the bits need to switch on or off (with 1s being on and 0s off). Some of today’s computing hardware works by flipping the magnetic moments, or orientations, of electrons, which can be either up or down. While excitonic insulators do not have magnetic moments, in Ta2NiSe5 they do harbor two intrinsic orientations that can be used to represent 1s and 0s.
“In the case of magnetic moments, one can flip their direction by applying opposite magnetic fields, for instance. But there is no known equivalent of a magnetic field for excitonic insulators. We came up with a way of using light to perform this task,” says David Hsieh, a professor of physics at Caltech, member of the Institute for Quantum Information and Matter (IQIM), and a co-author on the new study.
In the new theoretical and experimental study, the physicists demonstrate how to use bursts of laser light to control the excitonic insulator phases on timescales shorter than a picosecond, which is one-trillionth of a second. While the work has implications for ultrafast computer processing, the researchers are also excited about the fundamental aspects of their discoveries.
“In the process of learning to control and manipulate this material, we are also revealing the underlying rules of nature for a rare state of matter,” says the study’s lead author Honglie Ning, a graduate student working in Hsieh’s lab.
The Physical Review Letters study, titled, “Signatures of Ultrafast Reversal of Excitonic Order in Ta2NiSe5,” was funded by the Defense Advanced Research Projects Agency, IQIM/National Science Foundation, the Department of Energy, the Yamada Science Foundation Fellowship for Research Abroad, and Japan Society for the Promotion of Science Overseas Research Fellowships. Other Caltech authors include graduate student Omar Mehio; Michael Buchhold, former postdoctoral scholar in theoretical physics; and Gil Refael, Caltech’s Taylor W. Lawrence Professor of Theoretical Physics. Additional authors include Takashi Kurumaji and Joseph Checkelsky of MIT.
Featured image: Honglie Ning in a laboratory at Caltech. Credit: Caltech/David Hsieh Lab
Reference: Ning, H. and Mehio, O. and Buchhold, M. and Kurumaji, T. and Refael, G. and Checkelsky, J. G. and Hsieh, D. (2020) Signatures of Ultrafast Reversal of Excitonic Order in Ta₂NiSe₅. Physical Review Letters, 125 (26). Art. No. 267602. ISSN 0031-9007. https://resolver.caltech.edu/CaltechAUTHORS:20201224-090002408
Optical resonators, which circulate and confine light (for instance in lasers), are currently used in a variety of applications of all sizes—from pinpoint light sources smaller than the width of a human hair to kilometer-scale sensing devices such as the Laser Interferometer Gravitational-wave Observatory (LIGO) experiment that detects gravitational waves.
Devices known as optical parametric oscillators are among the widely used nonlinear resonators in optics; they are “nonlinear” in that there is light flowing into the system and light leaking out, but not at the same wavelengths. Though these oscillators are useful in a variety of applications, including in quantum optics experiments, the physics that underpins how their output wavelength, or spectrum, behaves is not well understood.
“When you add strong nonlinearity to resonators, you enter what we call a ‘rich physics regime,'” says Alireza Marandi, assistant professor of electrical engineering and applied physics. “‘Rich’ in physics terms usually means complicated and hard to use, but we need nonlinearities to create useful functionalities such as switching for computing.”
To be able to make full use of nonlinear optical resonators, researchers want to be able to understand and model the physics that underpin how they work. To this end, Marandi and his colleagues recently uncovered a potential way to engineer those rich physics, while discovering phase transitions in the light that is generated by the resonators. A paper about their findings was published on February 5 in Nature Communications.
Usually, the term phase transition might make one think of a change in the a physical state of matter; for example, water at atmospheric pressure transitions from a gas to a liquid at 100 degrees Celsius, and from a liquid to a solid (ice) at 0 degrees Celsius. This analogy of an abrupt change in state and behavior at a specific point also helps explain the spectral phase transitions that Marandi and his colleagues have noted in the optical resonators.
In the work, Marandi’s team built an optical resonator from optical fiber and a special nonlinear waveguide made of lithium niobate, an artificial salt used in various electronic and optical applications. The nonlinear resonator can generate light that has about twice the wavelength of its input light wavelength. When they precisely tweaked the resonator to different lengths, they found that the spectrum of the generated light experienced abrupt changes at specific lengths, akin to the phase transitions in physical matter.
Their discovery has both fundamental and practical implications. Fundamentally, phase transitions are a well understood and easy-to-model phenomenon, which could help simplify the “rich” physics of nonlinear optical resonators. Practically, they could be used in the construction of sensors. “Phase transitions are useful because they amplify the response you see with just a small change in input,” Marandi says. They also have the potential of becoming the “switching mechanism” in future optical computing architectures.
The paper is titled “Spectral Phase Transitions in Optical Parametric Oscillators.” Marandi’s co-authors are Caltech graduate student Arkadev Roy and postdoctoral scholar Saman Jahani; and Carsten Langrock and Martin Fejer of Stanford University. This research was funded by the Army Research Office and the National Science Foundation, as well as NTT Research Inc.
Featured image: A nonlinear waveguide used in the experiment. In this case, the waveguide provides the nonlinearity (parametric gain) for the light as it circulates through an optical resonator. Engineers at Caltech discovered that they could control phase transitions in the spectrum of light generated in the resonator by adjusting the length of the resonator. (Credit: Alireza Marandi/Caltech)
Reference: Roy, Arkadev and Jahani, Saman and Langrock, Carsten and Fejer, Martin and Marandi, Alireza (2021) Spectral phase transitions in optical parametric oscillators. Nature Communications, 12 . Art. No. 835. ISSN 2041-1723. PMCID PMC7864919. https://resolver.caltech.edu/CaltechAUTHORS:20201005-094314104
Seismologists at Caltech working with optics experts at Google have developed a method to use existing underwater telecommunication cables to detect earthquakes. The technique could lead to improved earthquake and tsunami warning systems around the world.
A vast network of more than a million kilometers of fiber optic cable lies at the bottom of Earth’s oceans. In the 1980s, telecommunication companies and governments began laying these cables, each of which can span thousands of kilometers. Today, the global network is considered the backbone of international telecommunications.
Scientists have long sought a way to use those submerged cables to monitor seismicity. After all, more than 70 percent of the globe is covered by water, and it is extremely difficult and expensive to install, monitor, and run underwater seismometers to keep track of the earth’s movements beneath the seas. What would be ideal, researchers say, is to monitor seismicity by making use of the infrastructure already in place along the ocean floor.
Previous efforts to use optical fibers to study seismicity have relied on the addition of sophisticated scientific instruments and/or the use of so-called “dark fibers,” fiber optic cables that are not actively being used.
Now Zhongwen Zhan (PhD ’13), assistant professor of geophysics at Caltech, and his colleagues have come up with a way to analyze the light traveling through “lit” fibers—in other words, existing and functioning submarine cables—to detect earthquakes and ocean waves without the need for any additional equipment. They describe the new method in the February 26 issue of the journal Science.
“This new technique can really convert the majority of submarine cables into geophysical sensors that are thousands of kilometers long to detect earthquakes and possibly tsunamis in the future,” says Zhan. “We believe this is the first solution for monitoring seismicity on the ocean floor that could feasibly be implemented around the world. It could complement the existing network of ground-based seismometers and tsunami-monitoring buoys to make the detection of submarine earthquakes and tsunamis much faster in many cases.”
The cable networks work through the use of lasers that send pulses of information through glass fibers bundled within the cables to deliver data at rates faster than 200,000 kilometers per second to receivers at the other end. To make optimal use of the cables—that is, to transfer as much information as possible across them—one of the things operators monitor is the polarization of the light that travels within the fibers. Like other light that passes through a polarizing filter, laser light is polarized—meaning, its electric field oscillates in just one direction rather than any which way. Controlling the direction of the electric field can allow multiple signals to travel through the same fiber simultaneously. At the receiving end, devices check the state of polarization of each signal to see how it has changed along the path of the cable to make sure that the signals are not getting mixed.
In their work, the researchers focused on the Curie Cable, a submarine fiber optic cable that stretches more than 10,000 kilometers along the eastern edge of the Pacific Ocean from Los Angeles to Valparaiso, Chile. (Although Zhan says the technique could be used on many of the hundreds of submarine cables that criss-cross the globe.)
On land, all sorts of disturbances, such as changes in temperature and even lightning strikes, can change the polarization of light traveling through fiber optic cables. Because the temperature in the deep ocean remains nearly constant and because there are so few disturbances there, the change in polarization from one end of the Curie Cable to the other remains quite stable over time, Zhan and his colleagues found.
However, during earthquakes and when storms produce large ocean waves, the polarization changes suddenly and dramatically, allowing the researchers to easily identify such events in the data.
Currently, when earthquakes occur miles offshore, it can take minutes for the seismic waves to reach land-based seismometers and even longer for any tsunami waves to be verified. Using the new technique, the entire length of a submarine cable acts as a single sensor in a hard-to-monitor location. Polarization can be measured as often as 20 times per second. That means that if an earthquake strikes close to a particular area, a warning could be delivered to the potentially affected areas within a matter of seconds.
During the nine months of testing reported in the new study (between December 2019 and September 2020), the researchers detected about 20 moderate-to-large earthquakes along the Curie Cable, including the magnitude-7.7 earthquake that took place off of Jamaica on January 28, 2020.
Although no tsunamis were detected during the study, the researchers were able to detect changes in polarization produced by ocean swells that originated in the Southern Ocean. They believe the changes in polarization observed during those events were caused by pressure changes along the seafloor as powerful waves traveled past the cable. “This means we can detect ocean waves, so it is plausible that one day we will be able to detect tsunami waves,” says Zhan.
Zhan and his colleagues at Caltech are now developing a machine learning algorithm that would be able to determine whether detected changes in polarization are produced by earthquakes or ocean waves rather than some other change to the system, such as a ship or crab moving the cable. They expect that the entire detection and notification process could be automated to provide critical information in addition to the data already collected by the global network of land-based seismometers and the buoys in the Deep-ocean Assessment and Reporting of Tsunamis (DART) system, operated by the National Oceanic and Atmospheric Administration’s National Data Buoy Center.
The new Science paper is titled “Optical polarization-based seismic and water wave sensing on transoceanic cables.” Zhan’s co-authors on the paper include Caltech graduate student Jorge C. Castellanos (MS ’18); Google researchers Mattia Cantono, Valey Kamalov, Rafael Muller, and Shuang Yin; and Antonio Mecozzi of the University of L’Aquila in Italy.
The research at Caltech was funded by the Gordon and Betty Moore Foundation.
Reference: Zhan, Zhongwen and Cantono, Mattia and Kamalov, Valey and Mecozzi, Antonio and Müller, Rafael and Yin, Shuang and Castellanos, Jorge C. (2021) Optical polarization–based seismic and water wave sensing on transoceanic cables. Science, 371 (6532). pp. 931-936. ISSN 0036-8075. https://resolver.caltech.edu/CaltechAUTHORS:20210225-153844866
A new signaling pathway has been identified that can prevent the overproduction of certain RNA-protein complexes in neurons. These complexes play an important role in neurodegenerative diseases.
Neurodegenerative diseases, such as various forms of senile dementia or amyotrophic lateral sclerosis (ALS), have one thing in common: large amounts of certain RNA-protein complexes (snRNPs) are produced and deposited in the nerve cells of those affected – and this hinders the function of the cells. The overproduction is possibly caused by a malfunction in the assembly of the protein complexes.
How the production of these protein complexes is regulated was unknown until now. Researchers from Martinsried and Würzburg in Bavaria, Germany, have solved the puzzle and now report on it in the open access journal Nature Communications. They describe in detail a signaling pathway that prevents the overproduction of snRNPs when they are not needed. The results should make it possible to better understand the processes in motor neuron diseases and senile dementia.
The research group led by Professor Michael Sendtner and Dr. Michael Briese from the Institute of Clinical Neurobiology at Julius-Maximilians-Universität Würzburg (JMU) was in charge of the publication. Professor Utz Fischer and Pradhipa Ramanathan from the JMU Institute of Biochemistry were also involved, as was a team from the Max Planck Institute of Biochemistry in Martinsried.
The next steps in research
Further investigations shall now show how the synthesis and degradation of excess snRNPs are regulated in nerve cells. The scientists hope that in the end they will be able to identify new therapeutic options for neurodegenerative diseases.
This work was financially supported by the German Research Foundation and the European Research Council.
Featured image: The molecule Larp7 plays an important role in the assembly of snRNP complexes. It accumulates in nerve cells (arrow) where the complexes are formed. (Image: Changhe Ji / Universität Würzburg)
Publication
Ji, C., Bader, J., Ramanathan, P. et al. Interaction of 7SK with the Smn complex modulates snRNP production. Nature Communications 12, 1278 (24 February 2021). https://doi.org/10.1038/s41467-021-21529-1
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