‘internet of photonic things’ with miniature sensors<div class="youtube-wrap"><div class="iframe-container"> <iframe width="854" height="480" frameborder="0" src=""></iframe><br/><br/> <br/></div></div><img alt="" src="/Profiles/PublishingImages/Yang_Lan.jpg?RenditionID=1" style="BORDER:0px solid;" /><div id="__publishingReusableFragmentIdSection"><a href="/ReusableContent/36_.000">a</a></div><p>A team of researchers at Washington University in St. Louis is the first to successfully record environmental data using a wireless photonic sensor resonator with a whispering-gallery-mode (WGM) architecture. <br/></p><p>The photonic sensors recorded data during the spring of 2017 under two scenarios: one was a real-time measurement of air temperature over 12 hours, and the other was an aerial mapping of temperature distribution with a sensor mounted on a drone in a St. Louis city park. Both measurements were accompanied by a commercial thermometer with a Bluetooth connection for comparison purposes. The data from the two compared very favorably.<br/></p><p>In the grand world of the "internet of things" (IoT), there are vast numbers of spatially distributed wireless sensors predominately based on electronics. These devices often are hampered by electromagnetic interference, such as disturbed audio or visual signals caused by a low-flying airplane and a kitchen grinder causing unwanted noise on a radio.<br/></p><p>But optical sensors are "immune to <g class="gr_ gr_66 gr-alert gr_spell gr_inline_cards gr_run_anim ContextualSpelling ins-del multiReplace" id="66" data-gr-id="66">electromagnetical</g> interference and can provide a significant advantage in harsh environments," said Lan Yang, the Edwin H. & Florence G. Skinner Professor of Electrical & Systems Engineering in the School of Engineering & Applied Science, who led the study from which the findings were published Sept. 5 in <em>Light: Science and Applications</em>.<br/></p><blockquote>"Optical sensors based on resonators show small footprints, extreme sensitivity and a number of functionalities, all of which lend capability and flexibility to wireless sensors," Yang said. "Our work could pave the way to large-scale application of WGM sensors throughout the internet."</blockquote><p>Yang's sensor belongs to a category called whispering gallery mode resonators, so named because they work like the famous whispering gallery in St. Paul's Cathedral in London, where someone on the one side of the dome can hear a message spoken to the wall by someone on the other side. Unlike the dome, which has resonances or sweet spots in the audible range, the sensor resonates at light frequencies and also at vibrational or mechanical frequencies, as Yang and her collaborators recently showed. <br/></p><p>"In contrast to existing table-sized lab equipment, the mainboard of the WGM sensor is a mere 127 millimeters by 67 millimeters — roughly 5 inches by 2.5 inches — and integrates the entire architecture of the sensor system," said Xiangyi Xu, the paper's first author and a graduate student in Yang's lab. "The sensor itself is made of glass and is the size of just one human hair; it is connected to the <g class="gr_ gr_68 gr-alert gr_spell gr_inline_cards gr_run_anim ContextualSpelling ins-del" id="68" data-gr-id="68">mainboard</g> by a single optical fiber. A laser light is used to probe a WGM sensor. Light coupled out of the sensor is sent to a photodetector with a transmission amplifier. A processor controls peripherals such as the laser current drive, monitoring circuit, thermo-electric cooler and Wi-Fi unit," Xu said.<br/></p><p>In her WGM, light propagates along the circular rim of a structure by constant internal reflection. Inside the circular rim, light rotates 1 million times. Over that space, light waves detect environmental changes, such as temperature and humidity, for example. The sensor node is monitored by a customized operating systems app that controls the remote system and collects and analyzes sensing signals.<br/></p><p>Wireless sensors, whether electronic or photonic (light-based), can monitor such environmental factors as humidity, temperature <g class="gr_ gr_75 gr-alert gr_gramm gr_inline_cards gr_run_anim Punctuation only-ins replaceWithoutSep" id="75" data-gr-id="75">and</g> air pressure. Applications for wireless sensors encompass environmental and health-care monitoring, precision agricultural practices and smart cities' data-gathering, among other possibilities. Smart cities are connected cities driven by internet data-harvesting. Precision agriculture uses digitized geographic information systems for precision agricultural practices such as soil mapping, which enables precise fertilizer and chemical applications and choice of seed selection for more efficient and profitable farming.<br/></p><p>Yang and her colleagues had to address stability issues, which were handled by the customized operation systems app they developed, and miniaturization of bulky laboratory measurement systems.<br/></p><p>"We developed a smartphone app to control the sensing system over WiFi," Yang said. "By connecting the sensor system to the internet, we can realize real-time remote control of the system."<br/></p><p>In June 2017, Yang and her group mounted the whole system on the outside wall of a building and accumulated a plot of the frequency shift of the resonance. They compared their data with the commercial thermometer.</p><p>"Thanks to their small size, the capability and flexibility of wireless photonic sensors can be improved by making them mobile," Yang said. </p><p>The researchers also mounted their system on an unmanned drone in May 2017 alongside the commercial thermometer. When the drone flew from one measurement location to others, the resonance frequency of the WGM shifted in response to temperature variations.</p><p>"The measurements matched well with results from the commercial thermometer," she said.  "The successful demonstrations show the potential applications of our wireless WGM sensor in the IoT. There are numerous promising sensing applications possible with WGM technology, including magnetic, acoustic, environmental and medical sensing."</p><p>The miniaturization of resonator sensing systems represents an exciting opportunity for IoT, as it will enable IoT to exploit a new class of photonic sensors with unprecedented sensitivity and capabilities," said Chenyang Lu, the Fullgraf Professor in the Department of Computer Science & Engineering and a co-author of the paper.<br/></p><SPAN ID="__publishingReusableFragment"></SPAN><p>Xu X, Chen W, Zhao G, Li Y, Lu C, Yang L. "Wireless whispering gallery mode sensor for thermal sensing and aerial mapping." <em>Light: Science and Applications</em>, Sept. 5, 2018. <br/></p><p>This research was supported by the Army Research Office.<br/></p><p>​<br/></p><div><div class="cstm-section"><h3>Lan Yang<br/></h3><div style="text-align: center;"> <strong> <a href="/Profiles/Pages/Lan-Yang.aspx"> <img src="/Profiles/PublishingImages/Yang_Lan.jpg?RenditionID=3" alt="Lan Yang" style="margin: 5px;"/></a> <br/></strong></div><ul style="text-align: left;"><li>Edwin H. & Florence G. Skinner Professor</li><li>Expertise: Photonics, optical sensing, microresonators, lasers, non-Hermitian physics, parity-time symmetry in photonics<br/></li></ul><p style="text-align: center;"> <a href="/Profiles/Pages/Lan-Yang.aspx">>> View Bio</a><br/></p></div></div> <br/>Tony Fitzpatrick team of researchers at Washington University in St. Louis is the first to successfully record environmental data using a wireless photonic sensor resonator with a whispering-gallery-mode (WGM) architecture. <p>Immune to electromagnetic interference, photonic sensor applications range from environmental and health-care monitoring to smart cities' data-gathering<br/></p>Y named chair of WashU electrical & systems engineering <img alt="Bruno Sinopoli" src="/Profiles/PublishingImages/Bruno%20Sinopoli%202018_MWH_021.JPG?RenditionID=2" style="BORDER:0px solid;" /><div id="__publishingReusableFragmentIdSection"><a href="/ReusableContent/36_.000">a</a></div><p>Bruno Sinopoli, a renowned expert in cyber-physical system and control systems, has been named chair of the Preston M. Green Department of Electrical & Systems Engineering in the School of Engineering & Applied Science at Washington University in St. Louis, effective Jan. 1, 2019.<br/></p><p>Sinopoli comes to WashU Engineering from Carnegie Mellon University, where he is a professor in the Department of Electrical & Computer Engineering and co-director of the Smart Infrastructure Institute. He also has appointments in the Robotics Institute and in mechanical engineering. He joined the faculty at Carnegie Mellon as an assistant professor in 2007. Previously, he was a postdoctoral fellow at the University of California, Berkeley and Stanford University.<br/></p><p>He succeeds R. Martin Arthur, who has been interim chair since 2016.<br/></p><p>"We were thrilled when the search resulted in Bruno being selected as the next chair of the Preston M. Green Department of Electrical & Systems Engineering," said Aaron Bobick, dean and James M. McKelvey Professor. "Bruno represents a new generation of electrical engineers who cross disciplines to solve problems in control and systems. He has both a deep appreciation of the history of the department and the ability to lead it in important new directions."<br/></p><p>Sinopoli's research is in cyber-physical systems, networked and distributed control systems, distributed interference in networks, smart infrastructures, wireless sensor and actuator networks, cloud computing, adaptive video streaming applications <g class="gr_ gr_42 gr-alert gr_gramm gr_inline_cards gr_run_anim Punctuation only-ins replaceWithoutSep" id="42" data-gr-id="42">and</g> energy systems.<br/></p><p>In 2015, the Carnegie Mellon team won the Microsoft Indoor Localization Competition in the infrastructure-based category at IPSN 2015. In 2010, he received the George Tallman Ladd Research Award from the Carnegie Institute of Technology at Carnegie Mellon, as well as an NSF CAREER Award, which is awarded to junior faculty who model the role of teacher-scholar through outstanding research, excellent education and the integration of education and research. As a graduate student at Berkeley, he received the Eli Jury Award for outstanding achievement in the area of systems, communications, control or signal processing.<br/></p><blockquote>"I am thrilled to join such an outstanding institution as Washington University as the chair of the Preston M. Green Department of Electrical & System Engineering, a department deeply rooted in fundamental research and with a rich tradition," Sinopoli said. "With the support of Dean Bobick and the university leadership, I am looking forward to working with faculty and staff to help the department grow and meet the new challenges of tomorrow's research and education."<br/></blockquote><p>Sinopoli has been a pioneer in networked control systems and cyber-physical systems security, where his work has contributed to launching both fields., with applications to smart grids. Along with colleague Anthony Rowe, he is advancing wireless broadband communications for use in hostile environments, such as burning buildings. They have been creating a system that allows firefighters and first responders to locate and orient themselves inside a burning structure. He also has been studying the interplay between financial and physical to encourage a faster recovery after extreme events such as tornadoes or hurricanes. Finally, Sinopoli has contributed to the development of UDOO, a computing platform commonly used by makers to prototype internet of things (IoT) systems.<br/></p><p>Sinopoli earned a master's and a doctorate in electrical engineering at the University of California, Berkeley in 2003 and 2005, respectively. In addition, he earned a certificate in Management of Technology from Haas Business School at UC Berkeley. He earned a bachelor's degree in electrical engineering at Università di Padova in Padua, Italy.<br/></p><SPAN ID="__publishingReusableFragment"></SPAN><p><br/></p>Bruno SinopoliBeth Miller 2018-09-12T05:00:00ZBruno Sinopoli will head the Preston M. Green Department of Electrical & Systems Engineering beginning Jan. 1, 2019. <p>​Sinopoli represents 'a new generation of electrical engineers'<br/></p> in to how our brains process information<img alt="" src="/news/PublishingImages/decisionmakingcrop-300x300.jpg?RenditionID=1" style="BORDER:0px solid;" /><div id="__publishingReusableFragmentIdSection"><a href="/ReusableContent/36_.000">a</a></div><p>How our brains process information is intimately tied to the kinds of goals we have or the tasks we need to perform. For example, when shown the word "yellow," our brains process it differently depending on whether we are asked to read the word or report the color of the ink. Not only that, each of us processes information differently, making understanding the brain basis of these kinds of complex cognitive processes particularly challenging for scientists.</p><p>ShiNung Ching, assistant professor in the Preston M. Green Department of Electrical & Systems Science in the School of Engineering & Applied Science, and Todd Braver, professor of psychological & brain sciences in Arts & Sciences, both at Washington University in St. Louis, will create new models of brain function to tease apart those individual differences and create new models for them with a three-year, $610,560 grant from the National Science Foundation (NSF). Matthew Singh, a WashU neuroscience graduate student, is another member of the research team.</p><p>The grant is one of 18 awarded by the NSF to conduct innovative research on neural and cognitive systems and will contribute to the NSF's commitment to the National Institutes of Health's Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative,</p><p>"The teams will integrate multiple disciplines to look at fundamental questions about the brain in new ways," said Shubhra Gangopadhyay, NSF program director in the Engineering Directorate. "The research will tackle problems that were previously intractable for neuroscience and cognitive science and will open up new avenues for future research. We are excited to see where these high-risk, high-reward proposals take us as a field."</p><p>The research seeks to understand the mechanisms of cognitive control, or how brain functions to allow us to vary our information processing and behavior based on our current goals and the situational context, such as not eating a friend's lunch despite being hungry.</p><p>Ching and Braver will study two sets of data to determine these mechanisms. One set is the functional MRI (fMRI) data from the Human Connectome Project (HCP), a five-year, National Institutes of Health-sponsored study led by Washington University School of Medicine that was aimed at constructing a complete map of structural and functional neural connections in the brains of a large, representative sample of individuals and their relatives. Unlike traditional MRI which takes anatomical images of the brain, fMRI measures brain activity by analyzing the neurons' demand for oxygen in the blood — the more activity, the more oxygen is needed. The initial stage of model building, which Singh has carried out, has used the HCP data to develop and evaluate the accuracy of the models in recreating the time course of brain activity patterns, sometimes called dynamics.</p><p>Next, the researchers will deploy the models to understand brain dynamics in <g class="gr_ gr_76 gr-alert gr_gramm gr_inline_cards gr_disable_anim_appear Grammar multiReplace" id="76" data-gr-id="76">a second</g> set of fMRI data that had individuals engage in a wide range of tasks that specifically depended on cognitive control. In the example of viewing words printed in different colors of ink, it requires more cognitive control when the task is to name the ink color and ignore the word itself. When presented with this task — called the Stroop task — researchers have previously found that when the ink color and word are different, it takes longer for the person to process the information, and therefore requires more brain activity. Yet exactly how and why individuals also differ in their brain activity dynamics is still a puzzle.</p><p>"We would like to be able to build up a specific mathematical model for each individual's brain activity so that we can begin to understand how the brain mechanisms of cognitive control differ between people," Ching said. "Instead of taking 100 people and doing an aggregation and providing one characterization on average, we are going to take all 100 people and produce a different model for each of them so that we can understand the variability of their brain dynamics."</p><p>Once they have the model, they will use it as a testbed to see what might be behind the overt observations.</p><p>"As people go about doing these tasks, the model will tell us what's different about their brain dynamics as reflected through this neuroimaging data," Ching said.</p><blockquote>"If successful, this could be a real game-changer in putting forward new computationally-based approaches to identify individual differences in the brain mechanisms that support higher cognition," Braver said. "Such approaches are definitely not being standardly used in brain imaging research, and as such current research is somewhat limited in being able to investigate individual differences in brain function."<br/></blockquote><p>To further look at the individual response to these tasks, Ching and Braver also plan to study sets of identical twins.</p><p>"Our modeling will allow us to disassociate this variability in terms of brain mechanisms and determine if differences are genetically encoded," Ching said.</p><p>Building models such as these <g class="gr_ gr_54 gr-alert gr_gramm gr_inline_cards gr_disable_anim_appear Grammar multiReplace" id="54" data-gr-id="54">has</g> been challenging for scientists because of the number of brain regions being recorded, Ching said.</p><p>"We're trying to directly use the brain data to build the model," he said. "Computationally, that's quite a challenging problem. We have introduced a number of innovations in terms of how we formulate the model and are leveraging a lot of new engineering theories and methods that have come online over the past few years to overcome it."</p><p>The scale of modeling and analysis they are proposing has rarely been attempted in neuroscience, the researchers said.</p><p>"Our hope is that these quantitative measures will help to explain how the individuals differ in their behavior and provide new predictive tools that can be used more broadly in the study of individual differences in human cognitive function," Braver said.  "Moreover, by more comprehensively characterizing brain function during cognition in this way, we will be in a position to understand the 'neural signatures' that define different cognitive and psychological states. In the long run, this could help with <g class="gr_ gr_60 gr-alert gr_gramm gr_inline_cards gr_disable_anim_appear Grammar only-ins replaceWithoutSep" id="60" data-gr-id="60">diagnosis</g> of cognitive impairments, and in developing new intervention techniques that can help enhance cognition."</p><SPAN ID="__publishingReusableFragment"></SPAN><br/><p>​</p> <span> <div class="cstm-section"><h3>​ShiNung Ching<br/></h3><div><p style="text-align: center;"> <a href="/Profiles/Pages/ShiNung-Ching.aspx"> <img src="/Profiles/PublishingImages/Ching_ShiNung.jpg?RenditionID=3" class="ms-rtePosition-4" alt="" style="margin: 5px;"/></a> </p><p>Author of 60 peer-reviewed papers<strong style="font-size: 1em;"> </strong></p><p>Recipient of the AFOSR Young Investigator Award and the Burroughs-Wellcome Fund Career Award at the Scientific Interface</p><p>PhD from the University of Michigan, and Postdoctoral Fellow at the Harvard Medical School and Massachusetts Institute of Technology</p><p>R<span style="font-size: 1em;">esearch areas: dynamical systems modeling of neural circuits, identification and control of spiking networks, dynamics of neural computation, neurocontrol engineering</span></p><div style="text-align: center;"> <a href="/Profiles/Pages/ShiNung-Ching.aspx">Read More</a></div></div></div></span><br/><br/>Beth Miller 2018-09-12T05:00:00ZNew models of brain function will be created to tease apart differences in complex cognitive processes with a three-year, $610,560 grant from the National Science Foundation (NSF).<p>​NSF grant will allow researchers to create new models to tease apart differences in complex cognitive processes <br/></p>‘Blink’-and-you-won’t-miss-amyloids-Alzheimers-Matthew-Lew.aspx911‘Blink’ and you won’t miss amyloids<div class="youtube-wrap"><div class="iframe-container"> <iframe width="854" height="480" frameborder="0" src=""></iframe><br/><br/> <br/></div></div> <br/><img alt="""" src="/news/PublishingImages/Matthew%20lew%202.00_03_24_12.Still002.jpg?RenditionID=1" style="BORDER:0px solid;" /><div id="__publishingReusableFragmentIdSection"><a href="/ReusableContent/36_.000">a</a></div><p>Tiny protein structures called amyloids are key to understanding certain devastating age-related diseases. Aggregates, or sticky clumped-up amyloids, form plaques in the brain, and are the main culprits in the progression of Alzheimer’s and Huntington’s diseases.</p><p>Amyloids are so tiny that they can’t be visualized using conventional microscopic techniques. A team of engineers at Washington University in St. Louis has developed a new technique that uses temporary fluorescence, causing the amyloids to flash, or “blink,” and allowing researchers to better spot these problematic proteins.<br/></p><p>“It has been pretty difficult, finding a way to image them in a non-invasive way — not changing the way they come together — and also figuring out a way to image them long-term to see how they clump and form larger structures,” said Matthew Lew, assistant professor in the <span class="s1" style="box-sizing: inherit;">Preston M. Green Department of Electrical & Systems Engineering</span> at the School of Engineering & Applied Science. “That was the focus of our research.”</p><p>Currently, scientists seeking to visualize amyloids use large amounts of a fluorescent material to coat the proteins in a test tube. When using a fluorescence microscope, the amyloids glow. However, it isn’t known how dyes that are permanently attached might alter the basic structure and behavior of the amyloid. It’s also difficult to discern the nanoscale structures at play using this bulk experimental technique.</p><p>Lew, whose research focus includes super-resolution microscopy and single-molecule imaging, worked with his former Washington University colleague Jan Bieschke, now an associate professor of brain science at University College in London, to develop the new technique that makes them blink. It’s called transient amyloid binding (TAB) imaging.</p><p>TAB uses a standard dye called thioflavin T, but instead of coating the amyloids, it temporarily sticks to them one at a time. The effect isn’t permanent, and the amyloids emits light until the dye detaches, yielding a distinctive blinking effect. The researchers were able to use a fluorescence microscope to observe and record the blinks. They then localized the position of each blinking thioflavin and reconstructed a super-resolved picture of the exact amyloid structure.</p><p>“The thioflavin T behaved like a group of fireflies, lighting up anytime they come into contact with the amyloid,” Bieschke said.</p><p>“What we saw were flashes of light over time,” Lew said. “On our computer screens, you’d see these individual spots blinking in sequence. We were then able to overlay all these dots together, giving us a complete look at the structure. If you didn’t separate them out, you’d see a blur.”</p><p>The team tested the TAB technique on a variety of amyloid structures and were able to reconstruct images for all of them, over an extended period of time and at various stages of aggregation. Their results were recently published in the journal ChemBioChem.</p><p>“There’s an intimate connection between seeing the proteins’ structure and learning how these proteins interact with neurons,” Lew said. “Ultimately, we need the imaging to understand all of the different shapes and structures that these proteins are building over time, and how that relates to the death of cells later on.”<br/></p> <SPAN ID="__publishingReusableFragment"></SPAN> <h5 style="box-sizing: inherit; color: #2f3030;">Kevin Spehar, Tianben Ding, Yuanzi Sun, Niraja Kedla, Jin Lu, George R. Nahass, Matthew D. Lew, and Jan Bieschke. Super-resolution imaging of amyloid structures over extended times using transient binding of single thioflavin-t molecules <span style="box-sizing: inherit; font-style: italic;">ChemBioChem</span> DOI 10.1002/cbic.201800352<br/></h5><h5 style="box-sizing: inherit; color: #2f3030;"> <span style="box-sizing: inherit;">Support for this research was provided by the National Science Foundation (ECCS-1653777) and by the National Institute of General Medical Science of the National institutes of Health (R35GM124858)</span></h5><p>​</p> <span> <div class="cstm-section"><h3>Matthew Lew</h3><div><p style="text-align: center;"> <img src="/Profiles/PublishingImages/Lew_Matthew_5620.jpg?RenditionID=3" class="ms-rtePosition-4" alt="" style="margin: 5px;"/> </p><p>2017 NSF CAREER Award:<strong style="font-size: 1em;"> </strong><span style="font-size: 1em;">“Nanoscale sensing and imaging using computational single-molecule nanoscopy”</span></p><p>Postdoctoral fellow in the de la Zerda Group in Structural Biology at the Stanford University School of Medicine</p><p>PhD in the lab of Nobel Laureate and WashU Engineering alumnus W. E. Moerner, <span style="font-size: 1em;">Stanford University</span></p><p>22 published papers</p><p>R<span style="font-size: 1em;">esearch areas: Microscopy, </span> <span style="font-size: 1em;">biophotonics, computational imaging, and nano-optics, especially when directed toward biological or biomedical applications.</span></p><div style="text-align: center;"> <a href="/Profiles/Pages/Matthew-Lew.aspx">Read More</a></div></div></div></span><br/>Erika Ebsworth-Goold protein structures called amyloids are key to understanding certain devastating age-related diseases.<p>​Engineering team just found new way to see proteins that cause Alzheimer’s, other diseases<br/></p>Y sync: How cells make connections could impact circadian rhythm <img alt="""" src="/news/PublishingImages/iStock-806943952.jpg?RenditionID=2" style="BORDER:0px solid;" /><div id="__publishingReusableFragmentIdSection"><a href="/ReusableContent/36_.000">a</a></div><p>If you've ever experienced jet lag, you are familiar with your circadian rhythm, which manages nearly all aspects of metabolism, from sleep-wake cycles to body temperature to digestion. Every cell in the body has a circadian clock, but researchers were unclear about how networks of cells connect with each other over time and how those time-varying connections impact network functions.</p><p>In research published Aug. 27 in <em>PNAS</em>, researchers at Washington University in St. Louis and collaborating institutions developed a unified, data-driven computational approach to infer and reveal these connections in biological and chemical oscillatory networks, known as the topology of these complex networks, based on their time-series data. Once they establish the topology, they can infer how the agents, or cells, in the network work together in synchrony, an important state for the brain. Abnormal synchrony has been linked to a variety of brain disorders, such as epilepsy, Alzheimer's disease <g class="gr_ gr_65 gr-alert gr_gramm gr_inline_cards gr_run_anim Punctuation only-ins replaceWithoutSep" id="65" data-gr-id="65">and</g> Parkinson's disease.</p><p><a href="/Profiles/Pages/Jr-Shin-Li.aspx">Jr-Shin Li</a>, professor of systems science & mathematics and an applied mathematician in the School of Engineering & Applied Science, developed an algorithm, called the ICON (infer connections of networks) method, that shows for the first time the strength of these connections over time. Previously, researchers could only determine whether a connection existed between networks.<br/></p><p>Li and collaborators first tested their method on <g class="gr_ gr_62 gr-alert gr_gramm gr_inline_cards gr_run_anim Grammar only-ins doubleReplace replaceWithoutSep" id="62" data-gr-id="62">simulated</g> network of different sizes they created. Next, they tested the method on a network of oscillators — populations of dynamic units that repeatedly fire together, go silent, then fire together again — created in the lab by Istvan Kiss, professor of chemistry at Saint Louis University. When they applied Li's algorithm to the network of interactions among the synthetic oscillators, the results matched what Kiss had determined through his experiments, finding the same connections in a network of 15 chemical oscillators. Such prediction of this dynamic topology was not previously possible, the researchers said.</p><p>Li said this method has a variety of applications beyond cell networks.</p><blockquote>"This lays the foundation to analyze real-world complex networks of tremendous size, such as transportation, internet, power grids, and social networks," he said.<br/></blockquote><p>Li also collaborated with Erik Herzog, professor of biology in Arts & Sciences at Washington University who studies the cellular and molecular basis of circadian rhythms in mammals, to determine the connections between cells in a mouse brain. Herzog measured the circadian rhythm from 541 cells from the right and left sides of the mouse brain, then asked Li to estimate how these connections changed over time — something that hadn't been done in the biology field.</p><p>"The connection at one time may be strong, but at another time it may be stronger or weaker, so we can use this data to recover the functional connectivity," Li said. "If we know this, then we know the network, then we can do more study and investigate over time whether this network will be synchronized or whether specific dynamic patterns will emerge."</p><p>Herzog said ICON would help him and other scientists to understand principles that allow systems to efficiently synchronize.</p><p>"For example, we want to define the essential features of networks of cells that keep daily time under different conditions," Herzog said. "We hope that ICON can map out connections and describe the interactions, such as attraction versus repulsion, of cells at different developmental stages so we can understand more about how circadian systems assemble after birth, adapt to challenges such as winter or summer, and fail to coordinate during stressors such as shift work or flying across multiple time zones."</p><p>In another experiment, collaborator William Schwartz, a former visiting professor of biology at Washington University now at the University of Texas at Austin, tested the method on seven groups of five mice who were housed together for a period of time as social networks. Schwartz measured the oscillations of the mice at the end of the experiment and provided the data to Li, who applied his algorithm to infer results from the data. In the end, both Schwartz and Li found that four of the groups of mice had social synchronization because they had the same body temperatures at the end of their time together. Three groups did not have the same body temperatures and were not socially synchronized.</p><SPAN ID="__publishingReusableFragment"></SPAN><p>Wang S, Herzog E, Kiss I, Schwartz W, Bloch G, Sebek M, Granados-Fuentes D, Wang L, Li, J-S. "Inferring Dynamic Topology for Decoding Spatiotemporal Structures in Complex Heterogeneous Networks." <em>PNAS</em>, Aug. 27, 2018, <a href=""></a>.<br/></p><p>This research was supported by funding from the National Academy of Science Keck Future Initiative seed grant; the National Science Foundation; the Air Force Office of Scientific Research (AFOSR); and the National Institutes of Health (1R21-EY027590-01, U01EB021956, NS09536702 <g class="gr_ gr_46 gr-alert gr_gramm gr_inline_cards gr_disable_anim_appear Punctuation only-ins replaceWithoutSep" id="46" data-gr-id="46">and</g> GM094109).<br/></p><p>​<br/><br/></p> <span> <div class="cstm-section"><h3>Jr-Shin Li<br/></h3><div><p style="text-align: center;"> <a href="/Profiles/Pages/Jr-Shin-Li.aspx"><img src="/Profiles/PublishingImages/Li_Jr-Shin.jpg?RenditionID=3" class="ms-rtePosition-4" alt="" style="margin: 5px;"/></a><br/></p><div style="text-align: center;"><div style="text-align: center;"> Professor Jr-Shin Li’s research group has extensive and close collaboration with biologists, chemists and applied physicists.</div> <br/> <a href="/Profiles/Pages/Jr-Shin-Li.aspx">View Bio</a></div></div></div></span><br/><br/>Jr-Shin Li and collaborators have developed a method that sheds light on circadian rhythm, an important function of metabolism.Beth Miller 2018-08-27T05:00:00ZJr-Shin Li collaborated with biologists and chemists to develop an algorithm that sheds light on circadian rhythm. <p>Algorithm lays the foundation to analyze real-world networks of tremendous size<br/></p>