Role of Cortical Network Plasticity in Recovery from Traumatic Brain Injury

Professor Laleh Najafizadeh is part of a team receiving a grant from the New Jersey Commission on Brain Injury Research. The title of the project is "Role of Cortical Network Plasticity in Recovery from Traumatic Brain Injury" and the award amount is $540,000.   This project is in collaboration with Professor David Margolis (PI) from the Department of Cell Biology and Neuroscience (CBN) in the School of Arts and Sciences at Rutgers, and Professor Janet Alder from the Department of Neuroscience and Cell Biology at Rutgers Robert Wood Johnson Medical School.

The project uses a mouse model along with novel imaging and quantitative methods to investigate brain plasticity underlying recovery from traumatic brain injury, and to screen candidate drugs with potential therapeutic value for treating traumatic brain injury. There is a critical need to discover treatments that improve recovery of brain function after injury. Little is known about how brain activity changes in response to the initial injury and how these changes cause lasting detrimental effects on mental function and behavior. The proposed experiments and analytical methods aim to identify the mechanisms and timing of the brain’s recovery from injury and to relate brain changes in the same subjects to cognitive and behavioral recovery.

Probing Neural Connectivity at Multiple Temporal Scales

Professor Laleh Najafizadeh is PI on a new NSF grant from the Biomedical Engineering (BME) Program of the Division of Chemical, Bioengineering, Environmental and Transport Systems (CBET).

The title of the project is "Probing Neural Connectivity at Multiple Temporal Scales" and the award amount is $363,273. This project is in collaboration with David Margolis (Co-PI), Assistant Professor at the Department of Cell Biology and Neuroscience (CBN) from the School of Arts and Sciences at Rutgers.

This project will develop a new comprehensive framework that will enable quantitative assessment of studying short-term and long-term network changes to advance our understanding of the dynamics of functional reorganization of the brain. The brain is a highly complex dynamic system in which neural functional connections are continuously changing at multiple time scales. These changes can occur at short scales, for example due to learning a simple task, or at relatively longer scales, due to wide range of reasons, such as learning complex concepts, brain-related diseases, and going through rehabilitation. Currently, our understanding of how the neural functional interactions form and change with time has been very limited because of lack of 1) quantitative measures that can reliably characterize these changes at different time scales, and 2) the ability to continuously monitor and record brain activities at different time scales, from millisecond to days and weeks. This project aims to address these limitations by taking a combined theoretical-experimental approach to establish a comprehensive data-driven framework that will enable quantitative characterization of the dynamic properties of brain functional networks at multiple temporal scales. With a focus on somatosensory learning, this research will use the proposed framework along with chronic imaging in GCaMP6f reporter mice, to quantitatively examine 1) how the interactions among functional brain networks are modified by task performance (short term changes), 2) how such interactions differ over days when mice finally becomes expert in performing the task (long term changes), and 3) how manipulating different nodes in the network, will change the dynamics of brain functional interactions. The success of this project, by including the temporal dimension into the analysis, will have a transformative impact in the field of neuroscience. This project will also provide a unique opportunity for the graduate and undergraduate students to obtain multidisciplinary expertise at the intersection of signal processing, statistics, neurobiology and imaging, thus providing an ideal platform for the training of the next generation engineers and neuroscientists.

Rutgers is playing a key role in a new NSF scientific software initiative

Shantenu Jha is a co-PI and the lead Computing Engineer/Science Faculty on a new $20M NSF Scientific Software Innovation Institutes for Molecular Sciences. The Institute called MolSSI -- Molecular Science Software Institute, will enable computational scientists to tackle problems that are significantly larger and more complex than thosecurrently within reach. MolSSI will impact domains that use molecular simulations -- from proteins and drug designs to the next generation of materials.

MolSSI is one of only two Software Institutes funded by the NSF and represents some of the most innovative and daring investment in cyberinfrastructure in decades by the NSF. As part of this initiative, Prof. Jha will be responsible for the (i) software engineering process, (ii) middleware upon which MolSSI supported science and codes will depend, and (iii) deployment and integration with NSF production infrastructure.

For more information please see the press release from the NSF at

PERMIT: Privacy-Enabled Resource Management for IoT Networks

Professors Anand Sarwate (PI) and Narayan Mandayam (co-PI) have received a new NSF grant from the Secure and Trustworthy Cyberspace (SaTC) program.

The title of the project is "PERMIT: Privacy-Enabled Resource Management for IoT Networks” and the award amount is $500,000 for three years (9/1/16 to 8/31/19).

The project investigates how privacy can be used to inform the design and management of future data sensing systems involved in applications related to the Internet of Things (IoT), industrial monitoring and control, "smart" homes/cities, and personalized health care. These systems will collect sensitive information about individuals and also require high bandwidth. For applications or services that rely on populations of individuals, reducing the amount of information transmitted can save bandwidth while enhancing privacy. The objectives of this work are to use ideas from data privacy technologies and wireless resource management techniques to jointly manage privacy and bandwidth in wireless sensing systems. Ultimately, this project seeks to formulate new trade-offs between privacy and quality-of-service that can generalize to other networking problems.

The full project abstract can be found on the NSF website:

Sparse and Low Rank Methods for Imbalanced and Heterogeneous Data

Vishal M. Patel received an NSF award for the project titled "Sparse and Low Rank Methods for Imbalanced and Heterogeneous Data." This is a two year collaborative effort between Rutgers University (Vishal Patel, PI) and The Johns Hopkins University.   Rutgers' share for this award is $249,152.

As part of this project, Vishal and his team will develop novel sparse and low-rank modeling techniques for dealing with imbalanced, heterogeneous and multi-modal visual data-sets.

Abstract: In rcent years, sparse and low-rank modeling techniques have emerged as powerful tools for efficiently processing visual data in non-traditional ways. A particular area of promise for these theories is visual recognition, where object detection and image classification approaches need to be able to deal with the highly diverse appearance of real-world objects. However, existing visual recognition methods generally succeed only in the presence of sufficient amounts of homogeneous and balanced training data that are well matched to the actual test conditions. In practice, when the data are heterogeneous and imbalanced, the performance of existing methods can be much worse than expected.

This project will develop a comprehensive framework for real-world visual recognition based on novel sparse and low-rank modeling techniques, which will be able to deal with imbalanced, heterogeneous and multi-modal data. Imbalanced data will be handled using convex optimization techniques that automatically divide a dataset into common and rare patterns, and select a small set of representatives for the common patterns that are then combined with the rare patterns to form a balanced dataset. Heterogeneous and multi-modal data will be handled using non-convex optimization techniques that learn a latent representation from multiple domains or modalities. Classification and clustering algorithms can be applied to the latent representation. Applications of these methods include image and video-based object recognition, activity recognition, video summarization, and surveillance.

For more information, please see:

The ProteOhmic Smart-Patch: Transcutaneous Monitoring of Molecular Levels in Blood Using Flexible and Natural Substrates

Mehdi Javanmard received a DARPA grant for the project "The ProteOhmic Smart-Patch: Transcutaneous Monitoring of Molecular Levels in Blood Using Flexible and Natural Substrates". The award amount is $185,887.15 and the duration is 15 months.

FIA-NP: Collaborative Research: The Next-Phase MobilityFirst Project - From Architecture and Protocol Design to Advanced Services and Trial Deployments

Principal Investigators: Dipankar Raychaudhuri, Roy D. Yates, Yanyong Zhang, Wade K. Trappe, Richard P. Martin

NSF has processed supplemental funding in the amount of $598,451 for the referenced award. The award, with this amendment, now totals $2,899,858.

Abstract: The Next-Phase MobilityFirst (MF) project aims to have a major impact on the architecture of the future Internet by re-architecting it to address the needs of emerging mobile platforms and applications. Adoption of technologies arising from this project may be expected to provide improved efficiency, security and robustness that would benefit both network operators and end-users of the Internet. This project, originally funded as a collaborative research effort under the NSF Future Internet Architecture (FIA) program (2010-13) in which the MF architecture was designed over the past 3 years, is centered on a new name-based service layer which serves as the narrow-waist of the protocol; this name-based services layer makes it possible to build advanced mobility-centric services in a flexible manner while also improving security and privacy properties. The architecture incorporates novel storage-aware routing techniques which provide significant improvements in mobile network capacity and functionality. The next phase of the MobilityFirst project is aimed at making the transition from early-stage architecture and prototyping to advanced real-world services and trial network deployments. The research and experimental trials agenda is aimed at validating and refining the core name service, routing, security and management components of the MF architecture, while also responding to emerging trends in network technology and services such as the cellular mobile data explosion, the growth of content, the emergence of cloud computing, and software-defined network (SDN) technology.

For more information:

MANTIS: A Multimodal Imaging Suite to Enhance Army's ISTAR Capabilities and Improve Soldiers Health

A DURIP proposal on which Waheed Bajwa is PI and his collaborator Mark Pierce (BME) is a Co-PI has been selected by the DoD as one of the 176 winning DURIP proposals this year (out of a total of 622 proposal submissions). The announcement for this award can be found at (winning list of awardees at

Details of the project are shown below.

Title: MANTIS: A Multimodal Imaging Suite to Enhance Army's ISTAR Capabilities and Improve Soldiers Health

Awarded Amount: $275,000

Abstract: The main objective of this DURIP project is to acquire multimodal sensing equipment that can help address the main challenge of providing academic researchers access to non classified multimodal data that can be used in lieu of classified Army data for ISTAR-related research. The acquired equipment will result in a suite of multimodal imaging devices that not only image scenes across a broad swath of spectrum that ranges from the ultraviolet (UV) and visible (VIS) to the long-wave infrared (LWIR), but that also provide high-resolution depth/ranging information and high frame-rate video of the scenes. This imaging suite, which we term MANTIS as an acknowledgement of the amazing multispectral vision capabilities of mantis shrimp, will comprise a total of eight imaging devices (including a LIDAR sensor) and it will be able to image tens of different spectra by using bandpass filters, owing to the wide spectral sensitivity of these devices.

Next Generation Tactical Waveform Development

The Space and Terrestrial Communications Directorate (S&TCD) of the U.S. Army has awarded Rutgers University an 8-month contract with an initial budget of $71,395. The WINLAB Rutgers University team is led by Wade Trappe and will support the Army's development of a next generation tactical waveform. The effort will involve the development of protocols and algorithms involving new physical layer methods, as well as the prototyping of such technologies S&TCD Cognitive Networking, Directional Networking to support transitioning to Army mid-tier waveforms and Next-Generation/multi-function/hybrid waveform programs.

The ThruProt Analyzer: Bringing Proteomics to the Field Using a Sample-to-Answer Electronic Multiplexed Platform

Mehdi Javanmard received a $344,942 NSF grant for the project "The ThruProt Analyzer: Bringing Proteomics to the Field Using a Sample-to-Answer Electronic Multiplexed Platform". This is a 3-year collaborative grant with colleagues from the RU Ocean Sciences Department, and Mehdi is the PI.

The abstract of the award is given below.

"IDBR: TYPE A- The ThruProt Analyzer: Bringing Proteomics to the Field Using a Sample-to-Answer Electronic Multiplexed Platform".

PI: Mehdi Javanmard,

Co-PIs: Paul G. Falkowski and Debashish Bhattacharya (Rutgers Univ. Ocean Sciences Dept.)

An award is made to Rutgers University New Brunswick to develop a battery powered handheld sensitive instrument whose purpose is to detect panels of proteins for field based environmental monitoring. A portable device for protein analysis virtually impacts all fields of biology and also the biomedical sciences. Protein analysis of environmental samples typically requires collecting and storing samples and returning weeks to months later in labs to begin analyzing the data. The proposed instrument will instead allow for point-of-use on the field analysis of biological samples. The result of this research will be an instrument that is handheld and generalizable to the needs of individual biological laboratories. This disruptive tool will be valuable for basic biology, as well as clinical, biotechnological, and agricultural research. This tool will also have greater societal benefits including improving agricultural practices and deeper insights into environmental biology, which is necessary for protecting the environment. This is multidisciplinary work that combines engineering, nanofabrication, chemistry, physics, and biology and provides a great opportunity to educate and train graduate students, undergraduates, and high school students. One of the important educational outcomes of the proposed research includes building teaching modules that will be incorporated into a massive open online course (MOOC) in biosensor and bioinstrumentation development. For our outreach efforts, we will collaborate with the Rutgers Office for Diversity and Academic Success in the Sciences to attract under-represented students to the study of STEM disciplines.


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