Research Briefs

Dissecting the immunosuppressive tumor microenvironments in Glioblastoma-on-a-Chip for optimized PD-1 immunotherapy

Weiqiang Chen, assistant professor of biomedical and mechanical and aerospace engineering led this research.

Glioblastoma (GBM) is the most common and aggressive primary brain tumor among adults, with an average survival of less than 14 months despite aggressive surgery, chemotherapy, and radiotherapy. One of the most promising therapies has been the inhibition of programmed cell death protein-1 (PD-1), which can turn the immune system against the tumors in order to destroy it. However, that approach has been difficult due to the genetic difference between blastomas and the environments they form in.

Improving the clinical use of anti-PD-1 immunotherapy in GBM patients requires a comprehensive understanding of tumor genetics and microenvironment as well as the ability to dissect the dynamic interactions among GBM and immune suppressor cells. Now, a team of researchers led by NTU Tandon’s Weiqiang Chen have integrated critical markers of these microenvironments in a microfluidics-based ex vivo microphysiological system termed ‘GBM-on-a-Chip.’ 

Modeling the human immune environment in the current animal-based cancer models is challenging. Compared to chemotherapy, it is difficult to preclinically validate and study immunotherapy. Discrepancies between preclinical and clinical results have raised concerns about how the findings from the current models can be translated to patients. The engineered tumor model on chips can be an alternative to the current animal models and patient studies, and even achieve a so-called "clinical trial on chips" for a pre-screening of patients suitable for immunotherapy, and screening personalized therapy for each patient. These chips are patient-specific, allowing longitudinal analysis of cells to understand how the environment around a tumor changes the way that PD-1 cells act.

The team – including researchers from NYU Tandon, NYU Langone Health and NYU School of Medicine, with funding from the National Science Foundation and National Institutes of Health – were able to use the results of this longitudinal study to show that molecularly distinct GBM subtypes have distinct epigenetic and immune signatures that may lead to different immunosuppressive mechanisms. They could see which cells were elevated past their healthy amounts, and which were not responding as they should. This allowed them to administer a specialized treatment for the specific environment, supressing some cells like tumor-associated macrophages while boosting the effectiveness of specific T-cells. Thus, they showed that these patient-specific chips could lead to personalized immunotherapy screening, potentially improving therapeutic outcomes in GBM patients.

The research, conducted with investigators from NYU Tandon, NYU Langone Health, and the NYU School of Medicine, is available online at in the publication eLife at https://elifesciences.org/articles/52253

Telemedicine and Healthcare Disparities: A cohort study in a large healthcare system in New York City during COVID-19

Rumi Chunara, an assistant professor in the Department of Computer Science and Engineering at NYU Tandon,  and in the Department of Biostatistics at NYU School of Global Public Health, was corresponding author.

Through the COVID-19 pandemic, telemedicine has become a necessary entry point into the process of diagnosis, triage and treatment. Racial and ethnic disparities in health care have been well documented in COVID-19 with respect to risk of infection and in-hospital outcomes once admitted. The researchers assessed disparities in those who access healthcare via telemedicine for COVID-19.

The researchers used electronic health record data of patients at New York University Langone Health between March 19th and April 30, 2020 to conduct descriptive and multilevel regression analyses with respect to visit type (telemedicine or in-person), suspected COVID diagnosis and COVID test results.

The collaborators included Yuan Zhao of the NYU School of Global Public Health; Ji Chen of the NYU Grossman School of Medicine; Katharine Lawrence, Paul A. Testa and Devin M. Mann of NYU Langone Health; and Oded Nov, professor in the Department of Technology Management and Innovation at NYU Tandon. 

Controlling for individual and community-level attributes, the researchers found that Black patients had 0.6 times the adjusted odds of accessing care through telemedicine compared to white patients, though they are increasingly accessing telemedicine for urgent care, driven by a younger and female population. COVID diagnoses were significantly more likely for Black versus white telemedicine patients (while they were more likely for white patients when considering in-person and telemedicine visits).

While the study reports that Black patients are not accessing care through telemedicine (versus by in-person visits to emergency department and physician’s offices) at the same rate as white patients, it notes increased uptake by young, female Black patients. Mean income and decreased mean household size of patients' home zip code were also significantly related to telemedicine use.

The team reports that telemedicine access disparities reflect those in in-person healthcare access. Roots of disparate use are complex and reflect individual, community, and structural factors, including their intersection; many of which are due to systemic racism. Evidence regarding disparities that manifest through telemedicine can be used to inform tool design and systemic efforts to promote digital health equity.

The research, which was supported by a generous grant from the National Science Foundation, can be viewed online at: https://academic.oup.com/jamia/advance-article/doi/10.1093/jamia/ocaa217/5899729

Perovskite Solar Cells with Enhanced Fill Factors Using Polymer-Capped Solvent Annealing

Perovskite solar cells have seen massive improvements over the last few years. But despite big increases in power conversion efficiency, fill factors – one of the important characteristics in need of optimization – have still hovered around 80 percent, limiting the capacity for solar energy.

Thanks to a team led by Associate Professor André D. Taylor, that fill factor has been pushed up to 85 percent. Using a polymer-capped solvent-annealing process, they enhanced open-circuit voltage without sacrificing short-circuit current, creating better perovskite cells with improved output and a longer lifespan than current models.

The research team included NYU Tandon Postdoctoral Research Associates Jaemin Kong and Jason A. Röhr, along with colleagues from Yale University, Brown University, Brookhaven National Laboratory and the Korea Research Institute of Chemical Technology, and received funding from several groups including the National Science Foundation and the Office for Naval Research. 

They found that during the solvent-annealing, the perovskite surface flattens and the perovskite grains agglomerate into micrometer-sized clusters having enlarged α-phase crystallites, while the δ-phase simultaneously disappears. The optimized structure enhances efficiency from 18.2 percent to 19.8 percent reliably, creating more stable and better solar cells.

The research can be viewed online at: https://pubs.acs.org/doi/abs/10.1021/acsaem.0c00854

Enabling Remote Whole-Body Control with 5G Edge Computing

Huaijing Zhu, a Ph.D. student in electrical and computer engineering was lead author.

There are many real-world — and, someday, off-world — applications for light-weight, energy-efficient, fully autonomous robots. Yet the more autonomous a robot is, the greater its computational requirements. Onboarding the components to handle this computational function adds weight, cost and reduces potential for applications in hostile environments. 

It might thus be desirable to offload intensive computation--not only sensing and planning, but also low-level whole-body control--to remote servers in order to reduce on-board computational needs. Fifth Generation (5G) wireless cellular technology, with its low latency and high bandwidth capabilities, has the potential to unlock cloud-based high performance control of complex robots. However, state-of-the-art control algorithms for legged robots can only tolerate very low control delays, which even ultra-low latency 5G edge computing can sometimes fail to achieve.

In this work, the investigators, led by Ludovic Righetti, associate professor of electrical and computer engineering and mechanical and aerospace engineering, and a member of NYU WIRELESS, investigate the problem of cloud-based whole-body control of legged robots over a 5G link. Their novel approach consists of a standard optimization-based controller on the network edge and a local linear, approximately optimal controller that significantly reduces on-board computational needs while increasing robustness to delay and possible loss of communication. Simulation experiments on humanoid balancing and walking tasks that includes a realistic 5G communication model demonstrate significant improvement of the reliability of robot locomotion under jitter and delays likely to experienced in 5G wireless links.

The team included Kai Pfeiffer, a postdoctoral researcher, and Manali Sharma, a master's student in the Department of Electrical and Computer Engineering; Sundeep Rangan, professor of electrical and computer engineering and associate director of NYU WIRELESS; and Marco Mezzavilla, a research scientist at NYU WIRELESS.

The project, funded in part by the National Science Foundation National Robotics Initiative and OPPO Mobile Telecommunications, will be presented at the IEEE/RSJ International Conference on Intelligent Robots and Systems in October, 2020.

The research can be viewed online at: https://arxiv.org/abs/2008.08243.

A many-to-many assignment game and stable outcome algorithm to evaluate collaborative mobility-as-a-service platforms

Joseph Chow, assistant professor of civil and urban engineering, and Deputy Director of the C2SMART University Transportation Center at NYU Tandon, led this work.

There is a growing need to focus on managing the capacities, allocation, and pricing of mobility services in a Mobility-as-a-Service (MaaS) ecosystem. City agencies need to assess the impact on other mobility operators and travelers when a new company enters the market, or an existing one changes their capacity, routing algorithms, or pricing. At the same time, a new mobility service or change to an existing one can cause travelers to alter routes or switch between companies and services to get where they’re going. This can make certain routes unstable to operate. Policymakers and the MaaS platforms themselves need to maintain an equilibrium while serving the needs of travellers. Up until now, classic traffic assignment models that only emphasize traveler routes were not effective at tracking these complex decisions and their outcomes.

The NYU Tandon team, which included Theodoros P. Pantelidis and Saeid Rasulkhani, former Ph.D. students under Chow's guidance (now at American Airlines and Scoop Technologies, respectively), proposed a model that can allow travelers to make multimodal, multi-operator trips, resulting in stable cost allocations between competing network operators to provide MaaS for users. Matching multiple links of different traveler paths to multiple operators is a many-to-many assignment game. In such a game, the stability conditions become more complex because they need to be considered from both a user's path level as well as an operator's level in serving that user. This new model shows how to derive an optimal assignment flow and corresponding stable outcome space between the operators and the travelers or users. 

The research demonstrated the use of the model for handling pricing responses of MaaS operators in technological and capacity changes, government acquisition, consolidation, and firm entry. The model was tested on an illustrative network as well as in a series of comprehensive experiments on the Sioux Falls network to demonstrate its capabilities. They showed it was possible to use stability conditions to link network design decisions and algorithmic policies as well as market dynamics to capture market performance for both operators and users.

The research, supported by a grant from the National Science Foundation, is available online in the Elsevier journal Transportation Research at https://authors.elsevier.com/a/1bZVIhVEA%7EXPk

Dual SARS-CoV-2 virus and antibody at-home test receives NSF I-Corps grant 

Jin Kim Montclare, professor of chemical and biomolecular engineering, is principal investigator of the project. 

A team led by Jin Kim Montclare developed a rapid point-of-care (POC) test, called TAP, a lateral-flow assay for both SARS-CoV-2 virus and antibody testing that can be carried out at home. The TAP Diagnostics team, which includes Farbod Mahmoudinobar and Kamia Punia, post-doctoral researchers; Dustin Britton, a doctoral student; and Sara Thermer, an industry mentor, received a National Science Foundation Innovation Corps (I-Corps) grant  for $50,000 to commercialize the TAP at-home test. 

The test will allow for universal access to testing of both coronavirus infection and immunity via protein-protein interactions with virions and antibodies at once, rather than using a polymerase chain reaction (PCR)-based test that relies on time-consuming reverse transcription of RNA. 

The NYU Tandon investigators report that the lateral flow assay (LFA) setup will be easy to use, eliminates the need for specialized equipment, and can be carried out as a single step, reducing the amount of sample handling. The test will be organized as a multiplex lateral flow test strip (MLFTS) detection format, which has been shown to reduce production costs and improve detection efficiency by providing multiple test types on a single strip, each line acting as either a control, virus infection, or immunity detectors. 

By using Rosetta macromolecular modeling suite for computational modeling enabled, the investigators were able to select detector proteins that are both multivalent and optimally solvent exposed. The team is employing genetic engineering techniques to synthesize proteins and further optimize for high binding affinity and exposure to coronavirus virions and antibodies on a simple LFA. This work provides insights into protein engineering, computational protein design, nanomaterial design as well as the pathophysiology of coronavirus. Additionally, its deployment will provide further understanding of coronavirus epidemiology.

Congress has recently approved of $25 billion towards coronavirus testing in the wake the economic impact of COVID-19, a critically important step since public health experts agree that safely emerging from a lockdown will require regular testing of millions of Americans. A highly accurate, portable, at-home testing system delivers on two fronts: Current testing technologies are still limited to a large percentage of false negatives (at least 20%); and access to coronavirus and antibody testing is limited in low resource settings that lack reliable electrical services, running water, and diagnostic devices and personnel to operate them. 

Also, while adoption of such current technologies as at-home nasopharyngeal sampling is on the rise, the creation of a rapid, simple point-of-care (POC) test has not been addressed. Montclare says the technology developed by TAP Diagnostics will decrease community spread of infection and will help mitigate the negative economic impacts of the COVID-19 pandemic, particularly in low resource communities where current testing technologies are unaffordable. 

“Additionally, participation in the I-Corps program will add entrepreneurial skills to the repertoires of members of TAP Diagnostics, allowing the team to mentor the next generation of interdisciplinary entrepreneurs,” said Montclare.

Swiped: Analyzing Ground-truth Data of a Marketplace for Stolen Debit and Credit Cards

Damon McCoy is an associate professor of computer science and engineering.

Fraud due to counterfeit credit and debit cards is a growing problem, estimated at $20 billion worldwide for 2018. These losses were not distributed evenly. E.U. countries experienced some of the lowest levels of fraud, and the U.S. some of the highest. This is largely attributed to the E.U.’s early adoption of anti-counterfeit chip technology (EMV). 

This paper presents the first empirical study of ground-truth data from a major underground shop selling stolen credit and debit cards. To date, there is little quantitative knowledge about how this segment of the underground economy operates, despite causing fraud losses estimated at billions of dollars a year.

The team, including Tobias Lauinger, a postdoctoral researcher; and Maxwell Aliapoulios, Rasika Bhalerao, and Cameron Ballard, Ph.D. candidates under McCoy's direction, analyzed four years of leaked transactional data to characterize stolen card bazaar BriansClub's business model, sellers, customers, and finances. The shop earned close to $104 million in gross revenue, and listed over 19 million unique card numbers for sale. Around 97% of the inventory was stolen magnetic stripe data, commonly used to produce counterfeit cards for in-person payments. Perhaps surprisingly, customers purchased only 40% of this inventory. In contrast, the shop sold 83% of its card-not-present inventory, used for online fraud, which appeared to be in short supply. Demand and pricing were not uniform, as buyers appeared to perceive some banks as having weaker countermeasures against fraud.

Even multiple years into the U.S. EMV chip deployment, the supply of stolen magnetic stripe data continued to increase sharply. In particular, we identified a continuing supply of newly issued cards not equipped with EMV chips, especially among prepaid cards. Our findings suggest that improvements to EMV chip deployment in the U.S., combined with a limited supply of stolen card-not-present data, could be avenues to decreasing the revenue and profitability of this shop.

The research is available at https://krebsonsecurity.com/wp-content/uploads/2020/07/nyu-cardshop.pdf

Tunable assembly of hybrid colloids induced by regioselective depletion

David Pine is Chairman of the Department of Chemical and Biomolecular Engineering.  

There are numerous applications for colloidal particles that can be induced to organize into ordered and/or functional crystalline structures or patterns. These colloidal superstructures have uses in photonics, materials processing and more.

But it is challenging to achieve site-selective directional interactions generating predetermined colloidal superlattices with desired properties. The team exploited regioselective depletion interactions — a region-specific interaction between colloidal particles — to engineer the directional bonding and assembly of non-spherical colloidal hybrid microparticles.

The study, whose lead author is Mingzhu Liu of the Department of Chemistry, Molecular Design Institute at NYU, details results showing that a crystallization of a binary colloidal mixture can be regulated by tuning the depletion conditions. They achieved regioselective bonding by fabricating triblock biphasic colloids with controlled aspect ratios. Without any surface treatment, these biphasic colloids assemble into various colloidal superstructures and superlattices featuring optimized pole-to-pole or centre-to-centre interactions. Additionally, they observed polymorphic crystallization, and used algorithms they developed to quantify the abundance of each form, and investigated the crystallization process in real time.

They were able to demonstrate selective control of attractive interactions between specific regions of a colloid with no need of site-specific surface functionalization, leading to a general method for achieving colloidal structures with yet unforeseen arrangements and properties.

The research, published in Nature Materials, is available at https://www.nature.com/articles/s41563-020-0744-2 

Spatial defects nanoengineering for bipolar conductivity in MoS2

Elisa Riedo, professor of chemical and biomolecular engineering.

Fascinating opportunities are emerging from a new class of materials named two-dimensional (2D) semiconductors, which are only one atom thick. 2D materials are poised to have a bright future in the electronics and optoelectronics industry, as well as in Internet of Things devices. Any cell phone, computer, electronic device, and even solar cells, are all composed of the same basic electronic building block, the diode. Unfortunately, a major obstacle for the wide application of 2D materials in industry is the unsolved challenge of the scalable and robust nanofabrication of the core element of a diode, which is a “p-n junction”.

The investigators demonstrated a novel approach based on thermal scanning probe lithography (t-SPL) to fabricate state-of-the-art “p-n junctions” on a single atomic layer of molybdeunum disulfide (MoS2), a transition metal dichalcogenide.

To produce “p-n junctions”, it is necessary to dope a semiconductor in such a way that part of it is n-doped (doped with an excess number of electrons) and another part is p-doped (doped with an excess number of positively-charged “holes”). Riedo and Davood Shahrjerdy, professor of electrical and computer engineering at NYU Tandon, showed that by combining t-SPL with defects nanoengineering was possible to obtain nanoscale-resolution bipolar doping of MoS2, yielding to both n-type and p-type conduction, which can be readily extended to other 2D semiconductors. 

As part of the research, the team integrated t-SPL — using a probe heated above 200 degrees Celsius — with a flow-through reactive gas cell to achieve a unique nanoscale control of the local thermal activation of defects in monolayer MoS2. The defective patterns can give rise to either p- or n-type conductivity on demand, depending on the gasses used during the local heating process. Doping and defects formation mechanisms are elucidated at the molecular level by means of X-Ray photoelectron spectroscopy, transmission electron microscopy, and density functional theory.

The international team included researchers from the City University of New York (CUNY), Politecnico di Milano, the University of Illinois Urbana-Champaign, the University of Pennsylvania, and the National Research Council of Italy (CNR).

The research, funded by the Office of Basic Energy Sciences of the US Department of Energy, is published in Nature Communications at https://www.nature.com/articles/s41467-020-17241-1.pdf?origin=ppub

A Game-theoretic Taxonomy and Survey of Defensive Deception for Cybersecurity and Privacy

Research is being led by Quanyan Zhu, professor of electrical and computer engineering, and Jeffrey Pawlick, a Ph.D. candidate. 

Cyberattacks on both databases and critical infrastructure have threatened public and private sectors while ubiquitous tracking and wearable computing have infringed upon privacy. Advocates and engineers have recently proposed using defensive deception as a tool for defenders to leverage the information asymmetry typically enjoyed by attackers. 

Quanyan Zhu, professor of computer science and engineering led a team that surveyed 24 articles from 2008 to 2018 that use game theory to model defensive deception for cybersecurity and privacy. They propose a taxonomy that defines six types of deception: perturbation, moving target defense, obfuscation, mixing, honey-x, and attacker engagement. 

These types are delineated by their information structures, agents, actions, and duration: concepts captured by game theory. 

The team  proposes to define types of defensive deception, capture a snapshot of the state of the literature, provide a menu of models that can be used for applied research, and identify promising areas for future work. 

Our taxonomy provides a systematic foundation for understanding different types of defensive deception commonly encountered in cybersecurity and privacy.

Edward Colbert of the Virginia Tech Intelligent Systems Lab will also participate in the work, which is funded by the U.S. Army Research Office.  

The first phase of research, published by ACM Computing Surveys, is available at https://dl.acm.org/doi/10.1145/3337772

Learning Data Science Through Civic Engagement With Open Data

Research will be led by Oded Nov and Graham Dove, professors in the Department of Technology Management and Innovation.

Government services, such as education, transportation, and non-emergency municipal requests, are becoming increasingly digital. When data that these services produce are made openly available, residents are able to pose questions, such as: “How do City agencies respond to noise in my neighborhood?” and “How do waste and recycling services in my neighborhood compare with others?”

Prof. Oded Nov, and Dr. Graham Dove, both of Tandon’s Technology, Management & Innovation and CUSP, along with Dr. Camillia Matuk of NYU Steinhardt, will investigate data science learning that takes place as members of the public explore and analyze this open civic data in relation to issues posed in their everyday lives. In particular, the research team is interested in how government and non-government  programs may be able to support participants in using such data to answer their own questions. 

The researchers are particularly interested in how programs support diverse communities’ access to and analysis of open data. The team will begin by synthesizing current best practices in data science, informal education, and citizen science. They will also study the provision and impacts of programs offered by two organizations in New York City, a leader in Open Data initiatives. This inquiry will look at the design and delivery of activities and facilitation approaches, probe participants objectives and data literacy skills, and investigate barriers to entry. 

Finally, the team will build capacity for other similar organizations to explore and understand their impacts on community members’ engagement with civic data. This pilot study will establish evidence of the effectiveness of these programs, and in turn, inform future research into the identifying and amplifying best practices to support public engagement with data.

The team will study programs offered by the Mayor's Office of Data Analytics (MODA), which is the NYC agency with overall responsibility for the City’s Open Data programs, and BetaNYC, a leading nonprofit organization working to improve lives through civic design, technology, and engagement with government open data. 

The proposal for the grant, funded by the National Science Foundation, is available at https://www.nsf.gov/awardsearch/showAward?AWD_ID=2005890&HistoricalAwards=false

Observation of an Internal p–n Junction in Pyrite FeS2Single Crystals: Potential Origin of the Low Open Circuit Voltage in Pyrite Solar Cells

Eray Aydil, Alstadt Lord Mark Professor of Chemical and Biomolecular Engineering, co-authored this research. 

Iron pyrite (commonly known as “fool’s gold”) has long been considered a potentially ideal and non-toxic photovoltaic material for low-cost, earth friendly solar cells. But photovoltaic solar cells using pyrite exhibit low open-circuit voltages (a measure of true voltage value), and have failed to achieve greater than 3% conversion of solar energy to electricity, a combination resulting in relatively low output. 

The team has taken an important step by identifying for the first time a possible source of this low voltage: an internal p-n junction between the a conductive surface layer on pyrite crystals, suggesting that the low open-circuit voltage results from a ‘leaky’ interface. The researchers reveal this interface through measurement of electronic transport.

Their discovery could lead to innovations by using the natural internal interface between layers of the cell or designing a different kind of homojunction solar cell using pyrites. 

The research, conducted with investigators from the University of Minnesota, is available online in ACS Materials Letters: https://pubs.acs.org/doi/10.1021/acsmaterialslett.0c00207