Fairfax, VA, United States

George Mason University

www.gmu.edu
Fairfax, VA, United States

George Mason University is the largest research university in Virginia and is based in Fairfax County, Virginia, United States, south of and adjacent to the city of Fairfax. Additional campuses are located nearby in Arlington County, Prince William County, and Loudoun County. The university's motto is Freedom and Learning.The university was founded as a branch of the University of Virginia in 1957 and became an independent institution in 1972. Today, Mason is recognized for its strong programs in economics, law, creative writing, computer science, and business. In recent years, George Mason faculty have twice won the Nobel Prize in Economics. The university enrolls 33,917 students, making it the largest university by head count in the Commonwealth of Virginia. Wikipedia.

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Patent
University of Maryland University College, United States Of America, George Mason University and George Washington University | Date: 2016-10-19

A nanostructure sensing device comprises a semiconductor nanostructure having an outer surface, and at least one of metal or metal-oxide nanoparticle clusters functionalizing the outer surface of the nanostructure and forming a photoconductive nanostructure/nanocluster hybrid sensor enabling light-assisted sensing of a target analyte.


Patent
University of South Florida and George Mason University | Date: 2015-04-21

An implantable magnetic resonant imaging (MRI) safe stylus for biomedical devices is described. In one example, the stylus includes a set of stylus modules. One or more of the stylus modules includes a core rod formed of silicon carbide (SiC) material, a recording array mounted on the core rod, and a stimulation array mounted at a distal end of the core rod. The stylus also includes a hemispherical cap formed of SiC material. In part due to the construction and choice of materials used in the stylus, it does not substantially couple with electromagnetic fields during an MRI, for example. Therefore, the stylus does not produce excessive additional heat. The designs described herein also rely on the high thermal transport but low heat capacity of SiC to provide a thermal pathway which will conduct induced heat throughout the stylus, to dissipate heat more evenly.


Patent
George Mason University | Date: 2016-10-11

Provided are methods, systems, devices of a security-driven design method. The present methods and systems can enable integration of security requirements in the early stages of design along with other design constrains so that potential attacks during IC development, usage, and retirement would render ineffectual. Example methods and systems can comprise circuits and circuit design using vanishable logic through a novel hybrid design method. An example method or system can comprise vanishable logic based on hardware re-configuration and transformation by employing non-volatile memory cells.


Patent
George Mason University | Date: 2016-12-22

Compositions and methods for the simultaneous capture and release using micropattern surfaces for tissue and cell microdissection. In one example, a patterned thermoplastic film has a first surface and a plurality of projections attached to and extending outwardly from the first surface. The projections form a pattern on the thermoplastic film.


Patent
George Mason University | Date: 2016-12-07

Disclosed herein are methods for treating subjects with breast cancer, comprising determining a therapeutic regimen for cancer by measuring the level (amount) of proteins of one or more biomarkers. Also disclosed are methods of treating a subject with breast cancer by predicting or assessing a therapeutic outcome for subject.


Armananzas R.,George Mason University | Ascoli G.A.,George Mason University
Trends in Neurosciences | Year: 2015

The classification of neurons into types has been much debated since the inception of modern neuroscience. Recent experimental advances are accelerating the pace of data collection. The resulting growth of information about morphological, physiological, and molecular properties encourages efforts to automate neuronal classification by powerful machine learning techniques. We review state-of-the-art analysis approaches and the availability of suitable data and resources, highlighting prominent challenges and opportunities. The effective solution of the neuronal classification problem will require continuous development of computational methods, high-throughput data production, and systematic metadata organization to enable cross-laboratory integration. © 2015 Elsevier Ltd.


Parekh R.,George Mason University | Ascoli G.A.,George Mason University
Neuron | Year: 2013

The importance of neuronal morphology in brain function has been recognized for over a century. The broad applicability of " digital reconstructions" of neuron morphology across neuroscience subdisciplines has stimulated the rapid development of numerous synergistic tools for data acquisition, anatomical analysis, three-dimensional rendering, electrophysiological simulation, growth models, and data sharing. Here we discuss the processes of histological labeling, microscopic imaging, and semiautomated tracing. Moreover, we provide an annotated compilation of currently available resources in this rich research " ecosystem" as a central reference for experimental and computational neuroscience


Olds J.L.,George Mason University
Nature Reviews Neuroscience | Year: 2016

Several large-scale international research initiatives have recently been launched, fuelling substantial financial investments in neuroscience and raising expectations for the development of new knowledge and therapies. Meeting these expectations will require global coordination of stakeholders and the adoption of team-based approaches that are not yet the norm for neuroscience. © 2016 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: CYBER-PHYSICAL SYSTEMS (CPS) | Award Amount: 399.93K | Year: 2017

The goal of this project is to develop an automated assistive device capable of restoring walking and standing functions in persons with motor impairments. Although research on assistive devices, such as active and passive orthoses and exoskeletons, has been ongoing for several decades, the improvements in mobility have been modest due to a number of limitations. One major challenge has been the limited ability to sense and interpret the state of the human, including volitional motor intent and fatigue. The proposed device will consist of powered electric motors, as well as the power generated by the persons own muscles. This work proposes to develop novel sensors to monitor muscle function, and, muscle fatigue is identified, the system will switch to the electric motors until the muscles recover. Through research on methods of seamless automated control of a hybrid assistive device while minimizing muscle fatigue, this study addresses significant limitations of prior work. The proposed project has the long-term potential to significantly improve walking and quality of life of individuals with spinal cord injuries and stroke. The proposed work will also contribute to new science of cyber-physical systems by integrating wearable image-based biosensing with physical exoskeleton systems through computational algorithms. This project will provide immersive interdisciplinary training for graduate and undergraduate students to integrate computational methods with imaging, robotics, human functional activity and artificial devices for solving challenging public health problems. A strong emphasis will be placed on involving undergraduate students in research as part of structured programs at our institutions. Additionally, students with disabilities will be involved in this research activities by leveraging an ongoing NSF-funded project.

This project includes the development of wearable ultrasound imaging sensors and real-time image analysis algorithms that can provide direct measurement of the function and status of the underlying muscles. This will allow development of dynamic control allocation algorithms that utilize this information to distribute control between actuation and stimulation. This approach for closed-loop control based on muscle-specific feedback represents a paradigm shift from conventional lower extremity exoskeletons that rely only on joint kinematics for feedback. As a testbed for this new approach, the team will utilize a hybrid exoskeleton that combines active joint actuators with functional electrical stimulation of a persons own muscles. Repetitive electrical stimulation leads to the rapid onset of muscle fatigue that limits the utility of these hybrid systems and potentially increases risk of injury. The goals of the project are: develop novel ultrasound sensing technology and image analysis algorithms for real-time sensing of muscle function and fatigue; investigate closed-loop control allocation algorithms utilizing measured muscle contraction rates to minimize fatigue; integrate sensing and control methods into a closed loop hybrid exoskeleton system and evaluate on patients with spinal cord injury. The proposed approach will lead to innovative CPS science by (1) integrating a human-in-the-loop physical exoskeleton system with novel image-based real-time robust sensing of complex time-varying physical phenomena, such as dynamic neuromuscular activity and fatigue, and (2) developing novel computational models to interpret such phenomena and effectively adapt control strategies. This research will enable practical wearable image-based biosensing, with broader applications in healthcare. This framework can be widely applicable in a number of medical CPS problems that involve a human in the loop, including upper and lower extremity prostheses and exoskeletons, rehabilitation and surgical robots. The new control allocation algorithms relying on sensor measurements could have broader applicability in fault-tolerant and redundant actuator systems, and reliable fault-tolerant control of unmanned aerial vehicles.


Grant
Agency: National Aeronautics and Space Administration | Branch: | Program: STTR | Phase: Phase II | Award Amount: 749.75K | Year: 2017

Extravehicular Mobility Units (EVU) are the necessary to perform elaborate, dynamic tasks in the biologically harsh conditions of space and they have stringent requirements on physical and chemical nature of the equipment/components/processes, to ensure safety and health of the individual require proper functioning of its life-support systems. Monitoring the Portable Life Support System (PLSS) of the EVU in real time ensures the safety of the astronaut and success of the mission. In Phase I, N5 Sensors has demonstrated and manufactured an ultra-small form factor, highly reliable, rugged, low-power sensor architecture for carbon dioxide (CO2) and ammonia (NH3) that is ideally suited for monitoring trace chemicals in spacesuite environment in presence of humidity and oxygen. N5 will perform additional design refinements in Phase II and implement on-chip components for enhanced analytical and operational reliability. Additionally, a complete detector system will be designed, integrated with various electronic components and tested to determine system level performance and reliability. Subsequent design refinements will be done.

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