Charlotte, NC, United States
Charlotte, NC, United States

The University of North Carolina at Charlotte, also known as UNC Charlotte, UNCC, or Charlotte, is a public research university located in Charlotte, North Carolina, United States. UNC Charlotte offers 21 doctoral, 64 master's, and 90 bachelor's degree programs through nine colleges: the College of Arts + Architecture, the College of Liberal Arts & science, the Belk College of Business, the College of Computing and Informatics, the College of Education, the William States Lee College of Engineering, the College of Health and Human Services, the Honors College, and the University College.UNC Charlotte has three campuses: Charlotte Research Institute Campus, Center City Campus, and the main campus, located in University City. The main campus sits on 1,000 wooded acres with approximately 85 buildings about 8 miles from Uptown Charlotte.The university is the largest institution of higher education in the Charlotte region, which is the second largest banking center in the United States. Wikipedia.

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University of North Carolina at Charlotte | Date: 2016-10-21

Isatoic anhydride derivatives having an N-substituent which includes a quaternary ammonium group are useful for labeling and/or functionalizing a target material and/or for coupling materials together. The isatoic anhydride derivatives of the present disclosure can be advantageously water soluble, easily prepared and purified. Isatoic anhydride derivatives useful in the present disclosure preferably have at least one chemically reactive group or at least one binding group or at least one detectable label. Anthranilate derivatives made from the isatoic anhydrides derivatives or otherwise and kits including the isatoic anhydride derivatives are also disclosed.

University of North Carolina at Charlotte | Date: 2016-12-06

An energy storage system controller, including: an energy storage system coupled to a power distribution system; and a processor in communication with the energy storage system, wherein the processor executes: a renewables capacity firming algorithm operable for conditioning intermittent power of a renewable energy station using real time and historical input data such that it is made more stable and non-intermittent, optionally utilizing one or more parameter values associated with comparable time periods taking into account one or more factors comprising cloud state; and a peak load shaving algorithm operable for ensuring that the energy storage system is capable of transmitting full power capacity at a predicted feeder peak load time determined by the processor from real time and historical input data; wherein the performance of the renewables capacity firming algorithm and the performance of the peak load shaving algorithm are optimized in parallel.

University of North Carolina at Charlotte | Date: 2016-10-07

A method to implement circuits and circuit elements having one or more ports may include digitizing, using analog-to-digital converters, continuous-time input signals received from one or more ports of a circuit to form discrete-time input signals. At a digital signal processor, the discrete-time input signals are received and the discrete-time input signals are processed to calculate a desired discrete-time output signals. Using digital-to-analog converters, the calculated desired discrete-time output signal are calculated to form outputs of continuous-time output signals at the one or more ports of the circuit. The continuous-time output signals are output to the same one or more ports that receive the continuous-time input signals; and producing, thereby, a desired relationship between the continuous-time output signals and the continuous-time input signals at the one or more ports.

A comparative discrimination spectral detection (CDSD) system for the identification of chemicals with overlapping spectral signatures, including: a radiation source for delivering radiation to a sample; a radiation collector for collecting radiation from the sample; a plurality of beam splitters for splitting the radiation collected from the sample into a plurality of radiation beams; a plurality of low-resolution optical filters for filtering the plurality of radiation beams; a plurality of radiation detectors for detecting the plurality filtered radiation beams; and a processor for: receiving a set of reference spectra related to a set of target chemicals and generating a set of base vectors for the set of target chemicals from the set of reference spectra, wherein the set of base vectors define a geometrical shape in a configuration space; receiving a set of filtered test spectra from the plurality of radiation detectors and generating a set of test vectors in the configuration space from the set of filtered test spectra; assessing a geometrical relationship of the set of test vectors and the geometrical shape defined by the set of base vectors in the configuration space; and based on the assessed geometrical relationship, establishing a probability that a given test spectrum or spectra matches a given reference spectrum or spectra.

Oliver J.D.,University of North Carolina at Charlotte
FEMS Microbiology Reviews | Year: 2010

Many bacteria, including a variety of important human pathogens, are known to respond to various environmental stresses by entry into a novel physiological state, where the cells remain viable, but are no longer culturable on standard laboratory media. On resuscitation from this 'viable but nonculturable' (VBNC) state, the cells regain culturability and the renewed ability to cause infection. It is likely that the VBNC state is a survival strategy, although several interesting alternative explanations have been suggested. This review describes the VBNC state, the various chemical and physical factors known to induce cells into this state, the cellular traits and gene expression exhibited by VBNC cells, their antibiotic resistance, retention of virulence and ability to attach and persist in the environment, and factors that have been found to allow resuscitation of VBNC cells. Along with simple reversal of the inducing stresses, a variety of interesting chemical and biological factors have been shown to allow resuscitation, including extracellular resuscitation-promoting proteins, a novel quorum-sensing system (AI-3) and interactions with amoeba. Finally, the central role of catalase in the VBNC response of some bacteria, including its genetic regulation, is described. © 2010 Federation of European Microbiological Societies Published by Blackwell Publishing Ltd. All rights reserved.

Yan S.,University of North Carolina at Charlotte
Cellular and molecular life sciences : CMLS | Year: 2014

To maintain genome stability, cells have evolved various DNA repair pathways to deal with oxidative DNA damage. DNA damage response (DDR) pathways, including ATM-Chk2 and ATR-Chk1 checkpoints, are also activated in oxidative stress to coordinate DNA repair, cell cycle progression, transcription, apoptosis, and senescence. Several studies demonstrate that DDR pathways can regulate DNA repair pathways. On the other hand, accumulating evidence suggests that DNA repair pathways may modulate DDR pathway activation as well. In this review, we summarize our current understanding of how various DNA repair and DDR pathways are activated in response to oxidative DNA damage primarily from studies in eukaryotes. In particular, we analyze the functional interplay between DNA repair and DDR pathways in oxidative stress. A better understanding of cellular response to oxidative stress may provide novel avenues of treating human diseases, such as cancer and neurodegenerative disorders.

Agency: NSF | Branch: Continuing grant | Program: | Phase: ADVANCES IN BIO INFORMATICS | Award Amount: 888.08K | Year: 2016

Biological science has made great strides recently both by capitalizing on automated methods of data collection and by enabling researchers to share results efficiently via public databases. This project will achieve the same for applications that use videos to track movement of animals, cells, or robots, by producing freely available software for efficient and automatic extraction of movement data and directly adding such data to a public database (the KNB Data Repository). The software will be easy to use and adapt to new types of videos, issues that have so far been roadblocks to widespread adoption of existing video tracking tools. The ability to share both movement data and videos will stimulate collaboration among researchers. Both functionalities will enable fundamentally new advances in such areas as animal group behavior, behavioral genetics, cell biology, and collective robotics, and other fields that record the movements of many individuals. The software, because of its ease of use and enabled access to research videos from the database, will also serve as a tool in teaching at the K-12 and college level. In addition, this project will serve to train several college and graduate students in both biology and computer science; such interdisciplinary training is essential for advances in biological research today.

This project will implement a unique combination of clear, current graphical user interface design to improve usability with state-of-the-art machine learning techniques to improve movement tracking accuracy. In addition, the developed software will enable users to visualize results for validation and analysis, and include functionality for users to correct any remaining tracking errors. This will enable users to get scientific-quality data output without having to employ multiple software applications and without having to manually post-process data files. In the context of the project, several workshops will be held and a website developed to improve accessibility for students and researchers in biology. The project will also develop a direct link to the existing KNB scientific data repository, such that users can access the repository, compare their results, or complete meta-analyses easily. Besides advancing biological research, this will also generate an extensive resource for computer vision scientists by providing a large collection of videos with accurate user annotation for improving core algorithms such as object detection and tracking. More information may be found at

Agency: NSF | Branch: Standard Grant | Program: | Phase: DATANET | Award Amount: 4.00M | Year: 2016

Data discovery and data analytics often rely on the use of multiple data sources and data residing in distributed locations. This project builds infrastructure that encourages data-driven discovery from distributed, fragmented datasets without requiring movement of massive amounts of data and without exposing sensitive raw datasets to end users. The capability will be applied to a wide range of science topics: to the large sky surveys of astronomy, for which the collecting instruments are distributed nationally and internationally; to classify Earth science satellite data; for the management of sickle-cell disease and antimicrobial resistance surveillance studies; and to integrate the highly distributed and fragmented data sources needed for multi-hazard mitigation and for sustainable and resilient human-building ecosystem research. The project outlines an ambitious and will enable interdisciplinary training in multiple universities and institutions, and contribute to the training of early career researchers

A Virtual Information-Fabric Infrastructure (VIFI) is created, allowing scientists to search, access, manipulate, and evaluate fragmented, distributed data in the information fabric (the infrastructure to facilitate data sharing) without directly accessing or moving large amounts of data. The system addresses the challenges of coordinating loosely federated infrastructure, distributed data management, security and privacy. The architecture combines a set of loosely coupled components representing some proven capabilities with several emerging components. The VIFI infrastructure includes a novel orchestration layer for on-site analytics and hybrid-infrastructure (GPU, CPU) management, a dynamic secure container-based infrastructure which enables online adaptive analytics from unshareable data at distributed locations, and enhanced data and code management tools. The layer also provides search, access and query based on improvements using persistent identifiers and automated semantic descriptions (or metadata) of raw data using semantic data mining techniques. By integrating several NSF-funded components into a coherent whole, VIFI allows researchers to search, access, manipulate and evaluate data elements without requiring detailed familiarity with the data infrastructure itself. The system contributes to and expands the sets of resources serving diverse communities, and is extensible to additional communities. The project contains a substantial outreach effort, including training of early career scientists.

Agency: NSF | Branch: Standard Grant | Program: | Phase: ROBUST INTELLIGENCE | Award Amount: 449.42K | Year: 2016

One of the most significant challenges in education is to simultaneously provide personalization and scale. How can each learner in an online class of hundreds or thousands be provided with knowledge and challenges that suit them personally? This project will develop an AI system for personalized learning that is inspired by cognitive models of curiosity, creativity and intrinsic motivation. Pique (short for the Personalized Curiosity Engine) is based on understanding what makes an individual learner curious, and then recommending resources that will stimulate their curiosity. Piques cognitive model of its learner uses natural language processing techniques to figure out what sequences of resources will be familiar enough to be accessible, but sufficiently new to not be boring.

Pique is a novel cognitive system drawing on technologies from intelligent tutoring systems, computational creativity, and natural language processing. Its key contribution is combining a cognitive model of curiosity with educational recommender systems. We will evaluate the effectiveness of Pique first with simulations and then with students at a large comprehensive public university. Evaluation will take the form of a comparison between the full Pique system and a modified version with its cognitive model of curiosity disabled. This will enable us to determine whether recommending resources that are simultaneously curiosity-stimulating and fit to the task is more effective than recommending resources that are just fit to the task. Given the interdisciplinary nature of this research we will disseminate our results broadly, including to the educational technology, cognitive systems, information retrieval, and computational creativity communities.

Agency: NSF | Branch: Standard Grant | Program: | Phase: COMPUTING RES INFRASTRUCTURE | Award Amount: 399.28K | Year: 2016

This infrastructure project will develop an open source software toolkit, called OpenMR, to support building mixed reality data analysis systems that project data into the physical world using a new class of display devices such as Microsoft Hololens and Oculus Rift. Through OpenMR, these lightweight, wearable, mobile devices will tap into data-intensive infrastructures hosted in the cloud, with the goal of developing systems that allow users to perform data-intensive tasks from anywhere, without requiring heavy dedicated large-format displays supported by dedicated local computers. To pursue this research, the investigators will acquire both dedicated cloud-computing servers (to support data analysis) and mixed reality hardware devices (to create the interfaces). They will develop OpenMR to connect this hardware, to support common analysis tasks such as selecting, filtering, and classifying data, and to create data displays in the physical world. To both demonstrate the toolkit and advance data analysis research, they will build a number of prototype mixed reality interfaces for researchers whose work requires analyzing a large amount of data in domains including weather, biology, and medical imaging. In addition to advancing those specific research areas, studying these prototypes with real users will support research around the underlying data analysis techniques, the cognitive science of how people interact with data in the physical world, and the design principles needed to build mixed reality systems. This, in turn, will make these emerging technologies more likely to succeed and spread, and increase the chance of finding potential killer apps for these systems. The infrastructure will also directly support education and research at the partner universities around data visualization, computer graphics, computer vision, and machine learning, while the release of the toolkit will benefit the wider community. This research is timely and important because as smart devices, in particular virtual and mixed reality devices such as Google Glass, Microsoft Hololens, Oculus Rift and Google Cardboard, become commonplace, these devices will play an increasingly important role relative to traditional laptop and digital computers when interacting with digital information.

The long-term vision of the project is to develop a mixed reality research infrastructure to support everywhere data-centric innovations, providing immersive, intuitive, location-free, advanced machine learning, data analysis, reduction, summary and storage tools. This includes advanced support for the full pipeline of data-centric work in mixed reality spaces through the OpenMR open source toolkit, including front end visualization and interaction that leverages awareness of available rendering spaces and hardware along with effective visualization patterns in 2D and 3D spaces to optimize interaction; key components of data analysis and machine learning on the middle layers including automatic, generic feature engineering and joint optimization of classification performance and effective identification of discriminating features; and high-performance computing and cost-sensitive job management on the server. The team will evaluate OpenMRs efficiency, stability, scalability, functionality, flexibility, and ease of adoption through a number of mechanisms, including self-evaluations and documentation of the design process, review from domain experts, and evaluation with both expert and novice users on data analysis tasks that cur across the specific application domains described above. The toolkit itself will be released on the GitHub open source platform during the third year of the project after it has reached an initial level of maturity and usefulness. The investigators will publicize OpenMR through a Youtube channel with a set of demonstration videos; outreach to relevant researchers interested in immersive visualization, visual analytics, multi-sensory human-computer interaction, machine learning with human-in-the-loop, and high-performance computing; and collaboration with undergraduates in the Students, Technology, Academia, Research, and Service Computing Corps consortium.

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