Arlington, TX, United States

University of Texas at Arlington
Arlington, TX, United States

The University of Texas at Arlington is a state university located in Arlington, Texas. The campus is situated southwest of downtown Arlington, and is located in the Dallas–Fort Worth–Arlington metropolitan area. The university was founded in 1895 and served primarily a military academy during the early 20th century. After spending several decades in the Texas A&M University System, the institution joined the University of Texas System in 1965. In the fall of 2014, UTA reached a student population of nearly 35,000, a gain of 65% from autumn 2001, and is currently the second-largest institution within the UT System. UTA is classified by the Carnegie Foundation as a "High Research Activity" institution and named one of the fastest growing public research universities in the nation. The university offers 80 baccalaureate, 74 masters, and 31 doctoral degrees.The university also operates the Fort Worth Education Center and the UTA Research Institute, with campuses at the Fort Worth ITC and River Bend Park. Wikipedia.

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Feschotte C.,University of Texas at Arlington | Gilbert C.,CNRS Ecobiological Interactions
Nature Reviews Genetics | Year: 2012

Recent studies have uncovered myriad viral sequences that are integrated or 'endogenized' in the genomes of various eukaryotes. Surprisingly, it appears that not just retroviruses but almost all types of viruses can become endogenous. We review how these genomic 'fossils' offer fresh insights into the origin, evolutionary dynamics and structural evolution of viruses, which are giving rise to the burgeoning field of palaeovirology. We also examine the multitude of ways through which endogenous viruses have influenced, for better or worse, the biology of their hosts. We argue that the conflict between hosts and viruses has led to the invention and diversification of molecular arsenals, which, in turn, promote the cellular co-option of endogenous viruses. © 2012 Macmillan Publishers Limited. All rights reserved.

Magnusson R.,University of Texas at Arlington
Optics Letters | Year: 2014

We present wideband resonant reflectors designed with gratings in which the grating ridges are matched to an identical material, thereby eliminating local reflections and phase changes. This critical interface thus possesses zero refractive-index contrast; hence "zero-contrast gratings." We design reflectors with zero-contrast gratings and highcontrast gratings and compare the results. For simple gratings with two-part periods, we show that zero-contrast grating reflectors outperform comparable high-contrast grating reflectors. An example silicon-on-glass reflector exhibits a 99% reflectance bandwidth of ∼700 nm for zero refractive-index contrast Δn = 0, whereas a high-contrast device with Δn = 2 yields a bandwidth of ∼600 nm. It follows that local Fabry-Perot modes residing in the grating ridges and reflecting off a high-contrast interface are not the root cause of wideband reflection. © 2014 Optical Society of America.

Passy S.I.,University of Texas at Arlington
Ecology Letters | Year: 2012

The relationships of local population density (N) with body size (M) and distribution (D) have been extensively studied because they reveal how ecological and historical factors structure species communities; however, a unifying model explaining their joint behaviour, has not been developed. Here, I propose a theory that explores these relationships hierarchically and predicts that: (1) at a metacommunity level, niche breadth, population density and regional distribution are all related and size-dependent and (2) at a community level, the exponents b and d of the relationships N ~ M b and N ~ D d are functions (f) of the environment and, consequently, species richness (S), allowing the following reformulation of the power laws: N ~ M f(S) and N ~ D f(S). Using this framework and continental data on stream environment, diatoms, invertebrates and fish, I address the following fundamental, but unresolved ecological questions: how do species partition their resources across environments, is energetic equivalence among them possible, are generalists more common than specialists, why are locally abundant species also regionally prevalent, and, do microbes have different biogeography than macroorganisms? The discovery that community scaling behaviour is environmentally constrained calls for better integration of macroecology and environmental science. Idea and Perspective Ideas and Perspectives © 2012 Blackwell Publishing Ltd/CNRS.

Agency: NSF | Branch: Standard Grant | Program: | Phase: INFO INTEGRATION & INFORMATICS | Award Amount: 500.00K | Year: 2016

This project investigates new robust large-scale data mining and machine learning algorithms to solve critical computational challenges in mining massive depression thought records for cognitive behavior therapy. Depression is rapidly emerging as one of the major problems in our society and is also related to many other health conditions, such as stroke, diabetes, hypertension, HIV/AIDS, etc. Cognitive behavior therapy is the most extensively researched form of psychotherapy for depression, and the depression thought records from patients is the key component of cognitive behavior therapy. However, the process of reviewing and analyzing the depression thought records is extremely time consuming, which inhibits both clinical interviews and the training of new therapists. This project builds a novel data mining system to automatically discover knowledge from depression thought records for assisting therapists in selecting potential interventions and aiding new therapists in their development of cognitive behavior therapy skills. This project will facilitate the development of novel educational tools to enable new courses and enhance current courses. This project engages minority students and under-served populations in research activities to give them a better exposure to cutting-edge science research.

To effectively and efficiently analyze large-scale depression thought records, this project explores the following research tasks. First, the project develops a robust semi-supervised learning model to categorize logical thinking errors of depression thought records. Second, the project investigates a joint multi-task method to simultaneously recognize the categories of thinking errors and emotions of depression thought records. Third, new multi-label and multi-instance learning is studied for identifying coping activities. Fourth, to analyze the multi-language depression thought records, robust transfer learning methods are developed for cross-language knowledge transfer. Meanwhile, parallel computational algorithms are designed and applied for large-scale depression thought record data mining. These novel data mining algorithms are designed to solve large-scale applications and automate the depression thought record data mining, which holds great promise for smart health.

Agency: NSF | Branch: Standard Grant | Program: | Phase: Big Data Science &Engineering | Award Amount: 1.32M | Year: 2016

The research objective of this proposal is to address the computational challenges in an innovative BIGDATA application on imaging-omics based precision medicine. Recent advances in high-throughput imaging (such as histopathology image) and multi-omics (such as DNA sequence, RNA expression, methylation, etc.) technologies created new opportunities for exploring relationships between histology, molecular events, and clinical outcomes using quantitative methods. However, the unprecedented scale and complexity of these imaging-omic data have presented critical computational bottlenecks requiring new concepts and enabling tools. This project builds a new computational framework to integrate novel big data mining algorithms with cloud and high-performance computing strategies for revealing complex relationships between histopathology images, multi-omics, and phenotypic outcomes. This project is innovative and crucial not only to facilitating the development of new big data mining techniques, but also to addressing emerging scientific questions in imaging-omics and many other biomedical applications. The developed methods and tools are expected to impact other cancer genomics research and enable investigators working on cancer medicine to effectively test their scientific hypothesis. This project facilitates the development of novel educational tools to enhance several current courses. University of Texas at Arlington is a minority-serving institution and has large population of Hispanic and Black Americans. This project engages the minority students and under-served populations in research activities to give them a better exposure to cutting-edge science research.

To solve the key and challenge problems in big imaging-omics data mining, this project explores the following research tasks. First, the large-scale non-convex sparse learning models are developed for identifying outcome-relevant phenotypic traits from big histopathology images. Second, the biological domain knowledge is utilized to guide the sparse learning models to uncover the molecular bases of complex traits. Third, the data integration models are designed to integrate imaging-omics data from multiple sources and discover the heterogeneous biomarkers. Fourth, the Baysian learning model is explored to predict longitudinal cancer outcomes. Fifth, the cloud computing and high-performance computing strategies are developed to support the big imaging-omics data mining, such as optimizations for various data mining workloads on heterogeneous hardware (e.g. GPU and NUMA multicore processors) to fully unlock the potential of data center hardware. It is innovative to integrate big data mining algorithms with cloud and high-performance computing to imaging-omics that hold great promise for a systems biology of the precision medicine.

Agency: NSF | Branch: Standard Grant | Program: | Phase: BIOLOGICAL OCEANOGRAPHY | Award Amount: 220.33K | Year: 2017

Coral diseases have increased significantly throughout the past 30 years. Climate change and other detrimental environment factors are likely to blame. Unhealthy coral reefs cannot support the fish and other life that make the reef a vibrant and diverse ecosystem. Corals reefs in the Caribbean Sea are disease hotspots and many reefs have experienced population collapses due to outbreaks of disease. Importantly, coral species vary in their susceptible to disease, but the reasons behind this variation are unknown. This project will quantify coral susceptibility to disease by examining coral immunity using several novel approaches and experiments. Seven species of coral that differ in disease susceptibility, growth rates, growth form and reproductive strategies will be used. Immune responses of each species of coral will be measured by exposing the corals to bacterial immune stimulators. Susceptibility to white plague disease, a prevalent disease affecting many species of corals, will also be measured by exposing the corals to active white plague disease and calculating disease transmission rates. The immune response and disease transmission data for each coral species will be used to develop a predictive model to determine how different coral communities will respond to disease threats under climate change scenarios. This project will support graduate students at University of Texas, Arlington (Hispanic-serving Institution) and University of Virgin Islands (Historically Black University) and many undergraduate students at all three institutions (Mote Marine Laboratory). This research will be highlighted at outreach events at all three institutions which take place regularly and include Earth Day Texas in Dallas, TX, Motes Living Reef Exhibit and Aquarium in Sarasota, FL and Reef Fest and Agricultural fairs in the U.S. Virgin Islands.

Environmental changes, such as ocean warming, have led to an increase in the prevalence of coral diseases, causing region-wide population collapses in some locations. However, not all coral species, or even populations within species, are affected by disease equally. Some species are host to many different types of diseases, but have limited mortality. Other species suffer significant disease-related mortality. How and why disease susceptibility differs among species and the effects of this differential susceptibility on reef community structure and composition are currently unknown. This project will use immune-challenge experiments that will quantify novel components of the innate immune system of corals, coupled with the application of a trait-based model, to fulfill three goals: 1) Determine variability of coral immune traits in seven common coral species found on Caribbean reefs, 2) Determine the variability in resistance to white plague disease transmission in the same coral species 3) Develop a predictive model of coral community assemblage that incorporates immune traits. Quantification of coral immunity will also incorporate unique approaches, such as combining full transcriptome sequencing with protein activity assays for a gene-to-phenotype analysis. Data will be mapped onto immune pathways for comprehensive pathway evaluation between coral species and these will serve as trait inputs into a traitspace model. These traits will provide continuous data within the model, which will create a probability density function (PDF) for the trait distributions of each species. These PDFs will then be used to determine the probability of species under different disease exposure scenarios. Model analyses will determine which traits influence community structure and characterize how disease exposure and the immune response will predict community assemblages through space and time. The completion and application of a trait-base model that incorporates extensive immunity parameters (none of which have been applied to trait models within coral ecosystems) is a distinct product from this project.

Agency: NSF | Branch: Standard Grant | Program: | Phase: THERMAL TRANSPORT PROCESSES | Award Amount: 507.00K | Year: 2016

This project describes an integrated research and education plan for measuring and enhancing thermal transport through key materials and material interfaces in electrochemical energy storage devices such as a Li-ion cell (battery). Li-ion cells offer excellent energy density and electrochemical performance in multiple applications, including electric vehicles. However, overheating due to poor thermal conduction is a well-known technological barrier, which directly impedes performance and results in severe safety concerns, as shown in recent incidents of fire in aircraft and car battery packs. There is an urgent need to identify and alleviate the fundamental material-level root cause of poor thermal behavior of Li-ion cells. This can potentially transform Li-ion cell performance and safety, but is also particularly challenging due to the highly coupled nature of thermal and electrochemical transport in a Li-ion cell over multiple length scales, and due to the importance of preserving electrochemical performance while improving thermal transport. This work will lay the foundation of microscale thermal engineering of electrochemical energy storage materials for current and future devices. Experimental and theoretical research methods developed in this work will be applicable to several other related engineering systems, such as super-capacitors. Education and outreach initiatives will address learning challenges among undergraduate students, particularly non-traditional commuter students and those from under-represented groups.

This proposal addresses scientifically and technologically relevant problems related to poor thermal transport in Li-ion cells, which is a major impediment to performance and safety. A unique test platform capable of in situ, material-level thermal and electrochemical measurements in real time on an operating Li-ion micro-cell will be built. This interdisciplinary research will, for the first time, measure and enhance thermal transport in materials and material interfaces in Li-metal and solid state electrochemical devices through interfacial chemical bridging and microstructural changes. These research thrusts will quantify the nature of microstructure-property-function relationships for key Li-ion materials. Improved cell-level thermal performance due to thermal enhancement of rate-limiting processes will directly result in safe and high performance batteries that will transform the nature of energy conversion, transportation and electronics through applications that are simply not possible with present batteries.

Agency: NSF | Branch: Standard Grant | Program: | Phase: NANOSCALE: INTRDISCPL RESRCH T | Award Amount: 667.11K | Year: 2016

Electrospinning is a nanomanufacturing process that enables the single step processing of self-supported, three-dimensional, and/or hierarchical networks of nanofibers, typically of polymers and their composites. This process has been exploited in recent years for the synthesis of nanoscale ceramics. However, the amount and quality of the electrospun mats produced are still close to a laboratory-scale. The focus of this award is to advance ceramic nanofiber electrospinning to ensure high process yield, process and product repeatability and reproducibility, along with optimized quality control. The anticipated result is a commercially-viable, high-throughput, nanomanufacturing process that produces functional nano-ceramics in large volumes and at a low cost. Processing of advanced photocatalysts for solar energy conversion to hydrogen fuel through water splitting is one of the targeted applications for the electrospun oxides addressed by this award. There are multiple anticipated benefits to the US economy and the welfare of the society, in terms of material availability and its use in harvesting energy from the sun. This multidisciplinary project brings together expertise in materials manufacturing, nanomaterials synthesis, electrochemistry, mechanical engineering, and computational modeling. This award will add to the skilled workforce that will guide the growth of new industries for nanomaterials manufacturing based on ceramic electrospinning, thus creating more jobs.

This award addresses fundamental issues related to the mechanism of formation of large-scale 3D mats, comprised of self-supported, high surface area, ceramic oxide nanostructures. It spans several disciplines, and it is the joint effort between four collaborators with complementary expertise in nanofibrous materials processing, structural and mechanical property characterization and modeling, and photocatalytic property assessment. The structural features of the as-spun and calcined nanofibrous mats will be modeled to enable the fine-tuning of their processing conditions and to optimize the final design of the high-throughput process. The measurement of the mechanical properties of the electrospun mats will determine how they will perform as photocatalytic blankets. Assessment of the photoelectrochemical properties of the mats will guide their tailored synthesis. The methods and techniques employed in this work are expected to revolutionize industrial processes for the nanomanufacturing of self-supported / non-dispersed ceramic nanofibrous mats for energy-related applications.

This project is jointly funded by the Engineering Directorate and the Division of Materials Research in the Mathematical and Physical Sciences Directorate.

Agency: NSF | Branch: Standard Grant | Program: | Phase: Exploiting Parallel&Scalabilty | Award Amount: 799.95K | Year: 2016

Todays datacenters enable a wide range of applications with diverse service-level
objectives (SLOs), e.g., user-facing applications such as web searches and disaster
recoveries that require real-time or near-real-time responses, calling for stringent job
latency and throughput guarantees. However, due to the lack of a proper abstraction,
the existing SLO-aware job resource provisioning approaches are platform dependent
and trial-and-error by design. The proposed solution presents a new abstraction that
provides an effective separation of concerns and thus makes it possible to develop
platform-independent, portable algorithms that translate diverse job SLOs into exact
performance objectives (a.k.a. budgets) for constituent tasks, resulting in
SLO-guaranteed job resource provisioning. The approach proposed can be proven
by design and is expected to provide (a) important insights into computer systems
and architecture designs with high performance guarantees and cost-effectiveness;
and (b) significant improvements in performance guarantees and resource utilization,
and the reduction of operating cost for the increasingly popular cloud computing
environment. It is also expected to encourage academia-and-industry,interdisciplinary
and cross-layer collaborations.

The proposed research develops a sound theoretical foundation to enable
SLO-guaranteed job resource provisioning. It explores fundamental design principles
and is cross-layer by design, involving a two-layer design, from applications to runtime
system and system architecture. At the upper, application layer, with any given job
workflow represented in the form of Directed Acyclic Graphs (DAGs), the job SLOs are
translated into precise latency budgets for individual task nodes in the DAG, independent
of the underlying system to be used to run the job. At the lower, runtime system layer, the
subsystems for individual task nodes are selected and the resources are allocated to meet
all the task performance budgets and hence the job SLOs. This proposed research will
enable us to develop job resource provisioning algorithms with SLO guarantee, while
allowing for service consolidation and achieving high datacenter resource utilization.

Agency: NSF | Branch: Standard Grant | Program: | Phase: INFRASTRUCTURE PROGRAM | Award Amount: 500.00K | Year: 2016

The Mathematics Department at the University of Texas Arlington will run a three-year bridge project involving 30 students to transition them to doctoral programs in the mathematical sciences. The targeted students are those, especially from underrepresented or underserved groups, who are not yet as competitive for doctoral programs as those who already had strong preparation in analysis and advanced linear algebra. The project is intended to be a comprehensive support system that benefits from the existing mathematics learning community in the mathematics doctoral program at the University of Texas Arlington and faculty mentoring and peer mentoring. This project will provide participating students with the opportunity to develop their full intellectual and academic potential in the mathematical sciences, by providing them with a comprehensive preparation and an innovative curricular and training program that emphasizes transition from an undergraduate program to a comprehensive PhD program. By addressing critical issues related to transition to doctoral studies in the mathematical sciences, particularly for those from underrepresented or underserved groups, the project has the potential to contribute to a significant increase in the quantity, quality, and diversity of the future leaders in the mathematical sciences, by serving as a model program for other institutions or other Science, Technology, Engineering and Mathematics (STEM) fields. This project has also the potential to impact curriculum improvements in the undergraduate mathematical training in underserved institutions in the nation through collaboration with institutions offering doctoral programs, benefiting both parties. By addressing critical issues related to transition to doctoral studies in the mathematical sciences, particularly for those from underrepresented or underserved groups, the project also has the potential to contribute to a significant increase in the quantity, quality, and diversity of the future leaders in the mathematical sciences. This project has also the potential to impact curriculum improvements in the undergraduate mathematical training in underserved institutions in the nation through collaboration with institutions offering doctoral programs, benefiting both parties.

This three-year project aims at increasing the quality and quantity of US doctoral students in the mathematical sciences, particularly those among underrepresented or underserved groups, through strong mentoring and a bridge-to-doctorate program with a BS-MS fast-track component. It targets those talented students among underrepresented groups or from small colleges or minority serving institutions, who are not yet considered competitive for rigorous national doctoral programs due to deficiency especially in analysis and advanced linear algebra. The project aims to train and mentor the participating students to make them well prepared in analysis and advanced linear algebra and to become competitive for a comprehensive PhD program in Mathematical Sciences. The project builds on two cornerstones: (1) a bridge-to-doctorate program with a BS-MS fast track that focuses on students in transition from undergraduate to graduate programs to strengthen their background in analysis and advanced linear algebra, and (2) a comprehensive system for such students providing strong mentoring and aggressive recruitment in the Gulf States Math Alliance, a regional branch of the National Alliance for Doctoral Studies in the Mathematical Sciences covering the states of Texas, Louisiana, and Mississippi.

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