Research News

Since it appeared in 2019, COVID-19 has claimed over 6 million lives and upended society across the globe. The condition, caused by the SARS-CoV-2 virus, attacks cells in the lungs, heart and brain, among other organs. Researchers soon realized that the disease affected these organs so dramatically because its distinctive spikes binded to the angiotensin-converting enzyme 2, or ACE2 receptor. The protein - common in those organs - provides the entry point for the coronavirus to hook into and infect cells.

ACE2 receptors were thus the obvious choice when testing for or treating COVID-19. By recreating the ACE2 and introducing it to an infected body, the virus would bind to the protein, revealing itself in a test or occupying itself with a ‘fake’ receptor. But relying on the ACE2 protein alone may not provide sufficient binding to find and fight the virus.

Now, researchers from across NYU and led by Jin Kim Montclare, Professor of Chemical and Biomolecular Engineering at NYU Tandon, have created a new protein that has an increased ability to bind to viruses, creating a more efficient tool in the fight against COVID-19. The secret is creating a version of ACE2 that mimics a multivalent assembled protein (MAP). Multivalent assembled proteins are like naturally occurring antibodies. Their bodies have multiple sites that can link and bind to the viruses they are trying to attack, making them far more effective at hooking into their targets.

The ACE-MAP the team designed utilizes a coil-shaped cartilage oligomeric matrix protein, a nanomaterial that Montclare’s lab has used before in different applications. When fused with part of ACE2 across the coils surface, they found that the new materials greatly increased the valency compared to ACE2 alone, potentially binding to multiple virus bodies at a time rather than a single one.

This new material has potential uses in both detection and treatment. Because the biomaterial is so much more effective at attaching themselves to viral bodies, it would require fewer of them compared to the natural antibodies currently used in tests and therapeutics. This technology has possible uses in testing for and treating other diseases with known receptors and a similar structure, such as HIV. Ongoing research will confirm the effectiveness of ACE-MAP in other models, and may be a key component of the fight against COVID-19 in the future.

This work was supported by the National Science Foundation and the Army Research Office.

This research was performed under the direction of Maurizio Porfiri, Institute Professor of Mechanical and Aerospace Engineering, Biomedical Engineering, Civil and Urban Engineering and incoming Director of the NYU Center for Urban Science and Progress (CUSP) at NYU Tandon. Collaborators were Peng Zhang, former researcher in Porfiri’s group and now faculty member at Tennessee Tech, and John Ohanian, research scientist at Luna Innovations.

Webbing structures — from chin straps and parachute material, to space habitats — are extensively employed in engineering systems as load-bearing components. They are frequently subjected to extended ultraviolet (UV) light irradiation, which can affect their integrity and reduce their mechanical strength. Despite technological advancements in structural health monitoring, long-term UV sensing techniques for webbings remain under-developed.

In the study, "Spiropyran-functionalized photochromic nylon webbings for long-term ultraviolet light sensing," published as the lead research (and featured cover) in the Journal of Applied Physics, the investigators explored an enticing solution: a photochromic nylon webbing that, because it comprises spiropyran (SP) functionalized polymers, demonstrates color variation in response to extended UV exposure with controlled, color variation over multiple time scales that is conducive to UV sensing.

The team developed a mathematical model grounded in photochemistry to interpret experimental observations, unveiling the photochromic phenomenon as a multi-step, multi-timescale photochemical process involving several chemical species offering the basis for the inference of the webbing’s color

In their research, the team found that the decay rate of the webbings’ color demonstrated a dependence on the initial concentration of the SP dye. Webbings with the lowest dye concentration maintained sensitivity for four weeks, whereas at the highest dye concentration, they exhibited sensing capability after eight weeks. Thus dye concentration could be customized to meet the lifetime of the targeted applications.

The proposed photochromic webbing and the photochemistry-based mathematical model could inform future designs of UV-sensitive structures that maintain sensitivity under weeks of continuous sunlight UV exposure.

Emotions directly influence the way we think and interact with others in different situations. Additionally, when it interferes with rationality in our decision-making or perception, emotions play an undeniable role.

From another perspective, studies from different fields indicate that human emotions could be impacted by any changes in the environment. Thus, having a solid understanding of the dynamics of emotions could benefit individuals in multiple ways: from the enhancement of therapeutic solutions and maintaining well-being to the prevention of unpleasant emotions and inducing favorable ones. Decoding emotions has been an ongoing research among researchers, dictating joint efforts from behavioral, physiological, and computational angles.

According to Russell's circumplex model, emotions are defined in a two-dimensional arousal-valence circular space. Compared to the arousal index, which designates the severity of the emotional state, internal valence levels determine the quality of emotions. In a high arousal mood, feeling excited or happy is opposed to nervousness and feeling stressed. On the flip side and in low arousal conditions, staying calm stands on the opposite side of feeling bored. Hence, feeling pleasant, which refers to the positive valence, is one of the key factors in one’s health condition and could result in a better quality of life.

In a new piece of research, a team led by Rose Faghih, Associate Professor of Biomedical Engineering, follows the goal of establishing a mechanism to track an individual's emotional valence level and regulate it in a closed-loop framework. Since the emotional valence state is hidden and cannot be measured directly, the authors need to measure it indirectly.

The use of wearable devices and designing decoders to gain insight into the internal brain states provides a good alternative to directly measuring brain activity. Inspired by recent advances in wearable technologies which could measure various physiological signals from humans in the loop, there exists possibilities to track emotional valence state indirectly and in a more practical manner. Among different physiological signals, it has been shown that facial electromyography (EMG) carries valuable information about the internal valence state. According to various research, facial EMG measurements of the zygomaticus muscle (zEMG) give the most distinct indicator of emotional valence state. In fact, changes in the zygomaticus muscle, which could be captured via wearables, could provide researchers with valuable information regarding one’s valence level.

In this research, the authors employ a mathematical framework to relate changes in zEMG signal for inferring the internal valence state. They extract corresponding features from the zEMG signal and estimate the internal valence state. Taking advantage of tracking of emotional valence state in a systematic way, they then propose a knowledge-based control architecture to close the loop and keep the estimated valence state within a favorable range. Within a closed-loop approach, biomarkers are collected to provide information regarding the individual's valence state to then automatically adjust the actuators in response to the current emotional state.

In silico results verify that, by making use of physiological data, the proposed controller could effectively regulate the estimated valence state. Ultimately, it is envisioned that the future outcomes of this research will include supporting alternative forms of self-therapy by using wearable machine interface architectures capable of mitigating periods of pervasive emotions and maintaining daily well-being and welfare. This study is the very first closed-loop control framework for regulating emotional valence state using facial muscle activity. This research suggests an important new step toward new clinical applications and the self-management of mental health. In the practical implementation of the proposed architecture, a wearable device would collect zEMG signal, and the appropriate practices (e.g., listening to music, breathing exercises) would be suggested to maintain the emotional valence levels within the favorable range.

Faghih, who joined NYU earlier in 2022, has been integrating research into her classroom teaching. The research covered in this Frontiers in Computational Neuroscience paper started as a course project in Faghih’s “State- Space Estimation with Physiological Applications” course at the University of Houston. In this class students had the opportunity to use advanced methods learned in the course to analyze physiological data in hands-on biomedical engineering projects designed by Faghih. Student authors Luciano Branco and Arian Ehteshami were graduate students in the course, and Hamid Fekri Azgomi, who recently completed his PhD under the supervision of Faghih, was a PhD student mentor for the project.

This research was led by Maurizio Porfiri, Institute Professor of mechanical and aerospace engineering, civil and urban engineering, and biomedical, and a member of the Center for Urban Science and Progress (CUSP) at NYU Tandon. Co-authors are Post-Doctoral Associate Peng Zhang of the department of mechanical and aerospace engineering at NYU Tandon and CUSP, and Sean D. Peterson of the University of Waterloo.

For over a century, scientists have sought to understand how fish orient against an incoming flow, even without visual and flow cues. In this study the researchers explore a potential hydrodynamic mechanism of fish rheotaxis — movement away or toward water currents — through the study of the bidirectional coupling between fish and the surrounding fluid.

The researchers point out that a major contribution of the proposed model is the treatment of the fish as an invasive sensor that both reacts to and influences the background flow, thereby establishing a coupled interaction between the fish and the surrounding environment.

By modeling a fish as a vortex dipole, a jet flow with a system of two vortices, in an infinite channel with an imposed background flow, they established a dynamical system that captures the existence of a critical flow speed for fish to successfully orient while performing cross-stream, periodic sweeping movements.

The researcher's juxtaposed their models with experimental observations in the literature on the rheotactic behavior of fish deprived of visual and lateral line cues. The crucial role of bidirectional hydrodynamic interactions unveiled by this model points at an overlooked limitation of existing experimental paradigms to study rheotaxis in the laboratory.

In the modern world, any challenge might be a source of stress. The fast-paced life has the potential to induce emotional and cognitive stress. Feeling overwhelmed, anxious, and agitated are among the symptoms associated with high levels of cognitive stress. Conversely, loss of cognitive engagement might also prevent individuals from following their goals. A low level of positive stress, which is also called eustress, might cause memory problems, lack of motivation, and poor concentration. It can also negatively affect individuals’ productivity in the workplace. According to multiple reports from “Mental Health America”, the percentage of people with depression and anxiety is growing in the United States. Thus, it is crucial to establish a mechanism for tracking one's cognitive stress level and keeping it within a favorable range.

Influenced by the recent advances in wearable technologies and inspired by the fact that skin conductance signal carries important information regarding one’s internal arousal state, there could be practical approaches to handle cognitive stress levels in daily life. In response to any environmental stimuli, the human brain controls the autonomic nervous system in multiple ways. As changes in electrodermal activity (EDA) signals are impacted by the changes in sweat glands activation, variations in skin conductance signal can be tracked in real-time using wearable devices.

In order to eventually enable wearable technologies to track and regulate one's internal stress state, the lab of Rose Faghih has pioneered methods for estimating the internal arousal state by monitoring one's EDA, i.e., fluctuations in their skin conductivity. They have developed a mathematical framework to model the internal arousal state and relate it to the changes in skin conductance signal (i.e., a signal that can be measured via wrist-worn wearable devices). Moreover, to estimate the hidden arousal state, they have developed methods that analyze the EDA signal and extract the underlying arousal events that activate sweat glands.  

Systematic modeling of the internal arousal state enables developing controllers to close the loop and regulate the estimated stress state. In this regard, well-established control techniques (i.e., linear quadratic regulator and model predictive controller) can be applied. In the process of finding the optimal control policy to regulate the stress state within these approaches, a corresponding cost function needs to be minimized. Hence, the performance of these control systems highly depends on the selection of corresponding objective functions.

As the researchers are dealing with a human-in-the-loop problem, the process of finding the appropriate objective functions becomes more complicated. To overcome this challenge and enhance the performance of the closed-loop system, Faghih and Fekri Azgomi, who recently completed his PhD under the supervision of Faghih, developed a supervised controller that includes a knowledge-based supervised layer that adjusts the objective function and tunes parameters in real-time. In this supervised architecture, a fuzzy system is utilized to continuously update parameters in the objective function to adjust environmental variations. The results verify the effectiveness of proposed architecture in keeping the estimated stress state within a desired range by designing a supervised layer on top of the optimal control formulation.  

Furthermore, the proposed supervised control architectures provide an excellent setting to incorporate the relevant medical expertise to enhance the closed-loop system. These novel supervised control approaches could be further expanded to deliver adaptive and robust closed-loop characteristics to address inter- and intra-subject variations.

There also exists great potential in implementing the supervised control architectures in other closed-loop applications such as adaptive deep brain stimulation for the treatment of neurodegenerative brain disorders and automating medication intake in a wide range of diseases. Once one models and tracks one's internal states, utilizing supervised control methodologies would result in enhancing the closed-loop treatment practices in a more personalized manner.

The researchers participating in this grant include Farshad Khorrami and Ramesh Karri, Professors of Electrical and Computer Engineering and member and director — respectively — of the NYU Center for Cybersecurity; and Research Scientist Prashanth Krishnamurthy.

A project to develop methods of securing the U.S. power grid from hackers, led by NYU Tandon researchers at the NYU Center for Cybersecurity, is one of six university teams receiving a portion of $12 million from the U.S. Department of Energy (DOE), supporting research, development, and demonstration (RD&D) of novel cybersecurity technologies to help the U.S. power grid survive and recover quickly from cyberattacks.

The Tandon team received $1.94 million for the project from the DOE fund, with matching support from NYU bringing the total to around $2.8 million, to develop Tracking Real-time Anomalies in Power Systems (TRAPS) to detect and localize anomalies in power grid cyber-physical systems. Collaborators include SRI International, the New York Power Authority, and Consolidated Edison. TRAPS will correlate time series measurements from electrical signals, embedded computing devices, and network communications to detect anomalies using semantic mismatches between measurements, allowing it to perform cross-domain real-time integrity verification.

Administered by the DOE's Office of Cybersecurity, Energy Security, and Emergency Response (CESER), the strategic project aims to advance anomaly detection, artificial intelligence and machine learning, and physics-based analytics to strengthen the security of next-generation energy systems. These systems include components placed in substations to detect cyber intrusions more quickly and automatically block access to control functions.  

The program aligns with the DOE’s larger goal of bolstering the security and resiliency of the power grid toward advancing President Biden’s goal of a 100% clean electrical grid by 2035 and net-zero carbon emissions by 2050.

 S. Farokh Atashzar, assistant professor of electrical and computer engineering at NYU Tandon and member of the Center for Urban Science and Progress (CUSP), has received a major NSF award (~$400K) to conduct fundamental research on the control of networked dynamic systems in the presence of distributed delays.

The COVID pandemic, an example of large-scale disease propagation, can be seen as a "mega-network" where complex interactions and distributed delays in the interconnections lead to hard-to-predict, echoing “waves” of disease spread. S. Farokh Atashzar, with the support of a collaborative National Science Foundation Civil, Mechanical and Manufacturing Innovation (NSF CMMI) grant, and in collaboration with Northeastern University, will dive into these "waves" by developing novel approaches to computational network modeling and designing optimal mitigation control to minimize the spread.

This research seeks to develop a comprehensive framework for data-driven control of large-scale networks where time delays and complex behavior play an important role. In the COVID pandemic, such effects lead to ``reflective" spreading waves, resulting in hard to predict and control phases of infection spread. But accurate network models of society and disease spread are necessary to enhancing pandemic preparedness and making healthcare systems and governments ready to respond well to potential future airborne epidemic diseases.

Effective mitigation of pandemics spread over networks requires: (a) unveiling the topology, dynamics, and delays of the underlying network from experimental data; (b) using this information to design networks that can robustly minimize the systemic effects of localized infection foci; and (c) synthesizing real-time optimal control laws that adjust local parameters to prevent the onset of delay-induced echoing waves of pandemic spread. This research seeks to achieve these objectives by embedding the problem into a more general one: data-driven control synthesis, based on nonlinear passivity control theory, for networked systems in the presence of delay-induced non-minimum phase/non-passive behavior.

Atashzar is also with NYU Center for Urban Science and Progress (CUSP). The project will promote health-related engineering science, research, and education for students from NYU CUSP and NYU Tandon.

This research included Rose T. Faghih, Associate Professor of Biomedical Engineering at NYU Tandon, and researchers from the University of Houston, the Louis Bolk Instituut, and the Weill Institute for Neurosciences.

According to the CDC, obesity prevalence in the United States was 42.4 percent in 2017–2018. Obesity prevalence in the United States climbed from 30.5 percent to 42.4 percent between 1999 and 2018. Obesity has been linked to cardiovascular disease, stroke, Type 2 diabetes, and a variety of malignancies. These are some of the most common causes of death that can be avoided. In 2008, the yearly medical cost of obesity in the United States was estimated to reach $147 billion.

It is important to understand the root cause of obesity in terms of hormonal imbalance. To regulate food intake, the brain responds to signals from fat (adipose) tissue, the pancreas, and the digestive tract. These instructions are transmitted via hormones like leptin, insulin, and ghrelin, as well as other small molecules. Among them, leptin is a signaling hormone that is essential to signal the brain in the suppression of appetite. It regulates food intake, metabolism, energy expenditure, and body weight. Thus, leptin regulation is known to be closely related to diseased conditions such as obesity.

In a new study from researchers at NYU Tandon and other schools, the group analyzed the behavior of two neuroendocrine hormones, leptin and cortisol (a stress hormone), in a cohort of patients with obesity. They used a system theoretic approach that can accurately estimate the internal secretion patterns, timings, amplitudes, number of underlying hormone secretory pulses, infusion, and clearance rates of hormones in patients with obesity by only measuring the 24-hour blood assay of their hormones.

The findings suggest a method for mathematically modeling both leptin and cortisol hormones to characterize how they interact as part of a larger system. Because the relationship between leptin and cortisol hormones is complex, the new results and projections will help us understand how these hormones work together to keep the body in a state of homeostasis.

The researchers demonstrated a negative relation between leptin and cortisol secretion, based on a statistical test called the Granger causality test among the patients with obesity. These results indicate that increases in cortisol are prospectively associated with reductions in leptin, suggesting a negative inhibitory relationship in 14 out 18 obese women investigated. Reduced leptin may result in a decrease in satiety and thereby lead to obesity

The model can be a crucial contribution toward the potential development of the next generation of agile closed-loop medical systems related to obesity. Such next-generation closed-loop medical systems will identify deviations from homeostasis and suggest necessary interventions benefiting from regular tracking of hormone secretory events and underlying endocrinological system parameters. This way complex conditions such as obesity can be prevented at the root level resulting in an overall increase in the quality of life and reduction in the total national medical expenditure.

Collaborators in this research, whose corresponding author is Andrea Silverman, Assistant Professor of Civil and Urban Engineering and a member of the Center for Urban Science and Progress at NYU Tandon, include Catherine Hoar, a postdoctoral researcher at NYU Tandon; and investigators at the New York City Department of Environmental Protection; Queens College of the City University of New York (CUNY); the CUNY Graduate Center; CUNY Queensborough Community College; and the Eugene Lang College of The New School.

In response to the COVID-19 pandemic, the New York City Department of Environmental Protection (NYC DEP) partnered with academic institutions, including NYU Tandon, to launch a wastewater monitoring program with the goal of tracking concentrations of SARS-CoV-2 in wastewater from the city’s 14 sewersheds. Cities like New York established such wastewater-based epidemiology (WBE) programs on the premise that viral particles of SARS-CoV-2, the virus causing COVID-19, are excreted by infected individuals into the sewer system. 
Such WBE data can provide community-level information that is not biased by rates of clinical testing, which may vary in different communities or at different times throughout the pandemic.

New research, led by Andrea Silverman, assistant professor of environmental engineering, and Catherine Hoar, a postdoctoral researcher under Silverman’s supervision, presents insights from the development of this monitoring program and explores the extent to which trends in SARS-CoV-2 concentrations in wastewater reflect trends in COVID-19 cases confirmed from clinical testing in NYC communities.  

To assess the relationship between the concentration of SARS-CoV-2 in the city’s sewersheds and confirmed cases of COVID-19 in the communities served by those sewersheds, the researchers analyzed samples of raw wastewater entering each of NYC’s 14 wastewater treatment facilities every week during the City’s second wave of the COVID-19 outbreak, beginning in in August 2020. They then compared viral load data they’d gathered from wastewater samples to publicly available case data provided by the NYC Department of Health and Mental Hygiene (DOHMH).

In correlating the wastewater and clinical data sets for each sewershed, they found that SARS-CoV-2 viral loads in wastewater corresponded to new laboratory-confirmed COVID-19 cases in the corresponding populations for all individual sewersheds: an increase in COVID-19 cases was associated with an increase in SARS-CoV-2 concentrations in wastewater. The researchers also used this analysis to estimate the minimum number of new COVID-19 cases per day that was associated with detection of SARS-CoV-2 in wastewater with the monitoring methodology they applied.

Broadly, the researchers concluded that relative trends in SARS-CoV-2 loads in wastewater can be evaluated and associated with trends in clinical testing data, and therefore can potentially contribute to situational awareness of disease incidence in large urban sewersheds. The data from this work is publicly available via the NYC Open Data portal and the United States Center for Disease Control and Prevention’s wastewater surveillance data dashboard. As the COVID-19 pandemic continues and evolves, Dr. Silverman and Dr. Hoar continue to partner with and advise the NYC DEP in their ongoing COVID-19 WBE program.

The research was supported by he New York City Department of Environmental Protection (NYC DEP), and the Alfred P. Sloan Foundation.

This research was led by Yury Dvorkin, professor of electrical and computer engineering, and member of the C2SMART Tier 1 Transportation Center at NYU Tandon; and included Hafiz Anwar Ullah Khan and Sara Price, Ph.D. and M.S. candidates, respectively, under Dvorkin's guidance; and post-doctoral researcher Charalampos Avraam.

Electrified transportation is one of the critical aspects of the global trend towards decarbonization. However barriers to consumer adoption of EVs by the public exist. Principally, they are access to, and affordability of electric vehicle (EV) charging infrastructure. The latter concern is lessening as light-duty electric vehicle (EV) prices rapidly declining to as low as $18,875 after federal tax credits and state rebates and their ranges increase with battery and drivetrain improvements.

To address charging availability major efforts are underway in the United States to roll-out public EV charging infrastructure. But persistent social disparities in EV adoption call for interventions.

In a paper, the investigators led by Yury Dvorkin, analyzed existing EV charging infrastructure across New York City (NYC) to identify features that correlate with the current distribution of EV charging stations. They found that population density is not correlated with the density of EV chargers, hindering New York’s EV adoption and decarbonization goals.

To determine the socio-demographic and transportation factors affecting the distribution of EV charging stations in NYC, they used the publicly available Alternative Fuel Station Locator dataset from Alternative Fuel Data Centre at the US Department of Energy. This dataset provides a current accounting of the types and locations of all alternative fuel stations in NYC.

Based on correlation analysis, hypothesis testing, and conditional analysis, the results demonstrate that the availability and affordability of EV charging stations in NYC are not determined by the population density, but are correlated with the median household income, percentage of White–identifying population, and presence of highways in a zip code, with the distribution of EV charging stations heavily skewed against low–income, Black–identifying, and disinvested neighborhoods in NYC.

The results underscore the need for policy frameworks that incorporate equity and justice in the roll-out of EV charging infrastructure.