Wei Chen - US grants

[unreadable] DESCRIPTION (provided by applicant): The mammalian olfactory system has a tremendous capability to discriminate thousands of different odor molecules. Recent advances in molecular biology and functional imaging have established that odor information is encoded as spatial patterns of activated glomeruli distributed on the olfactory bulb surface. How these glomerular coding patterns are transformed within the olfactory bulb circuits is critical to odor discrimination and recognition, but remains to be understood. As a unique feature of olfactory bulb neuronal circuits, most of the cell-cell communication is mediated by synapses and gap junctions made between dendrites of principal mitral/tufted cell and local interneurons. Our previous research has established how signals encoding odor information are transmitted and regulated dynamically along the mitral cell primary and secondary dendrites. The aim of this renewal application is to continue this line of research by combining patch-clamp recording, two-photon calcium imaging and green fluorescence protein-targeted transgenic/knock-in mice to analyze how the bulbar neurons interact with each other at a level of circuits to process glomerulus-specific odor information. Specifically, we will test three major hypotheses. First, one important function ofperiglomerular cells is to orchestrate the glomerulus-specific firing synchrony through both dendritic synaptic transmission and electrical coupling in the glomerulus. Second, neuronal activities within different glomeruli can also achieve temporary synchrony, and both periglomerular and granule cells play a important role. Third, signal transmission in granule cell dendrites and spines has a spatio-temporal dynamics that can mediate the coupling and uncoupling of mitral cells from different glomeruli as in response to different odorants. These experiments are in line with our long-term objective to obtain critical information on the functional principles of olfactory bulb dendritic circuits and how these principles are involved in odor signal processing and discrimination. The progress toward this objective will yield novel insights into how the olfactory system can recognize thousands of different odors, and into the neural basis of smell-related disorders. [unreadable] [unreadable]

0084477
Chen
Model-Based Simulation (MBS) has provided designers with flexible and cheaper means to explore design alternatives before physical part deployment. However, question of the confidence level on a particular model considering the full range of uncertainties in prediction and in physical tests has hampered wide applications, especially when dealing with real-life engineering problems. This collaborative research represents the joint efforts from two universities and General Motors for the development of a rigorous and practical approach for model validation (Model Validation via Uncertainty Propagation -- MVUP). The approach will utilize the knowledge of system variations along with computationally efficient uncertainty propagation techniques to provide a stochastic assessment of the validity of a modeling approach. The proposed methodology will be demonstrated through validating the simulation models for sheet metal forming processes.

The research is expected to lead to a model validation procedure that can provide a general-purpose stochastic assessment of model validity with the least amount of statistical assumptions and possibly one physical experiment. The research results developed will have an immediate impact on simulating springback in sheet metal forming process. It is our expectation that it can be generalized to other engineering problems and will make a significant impact on how we view numerical simulations by considering all the physical and numerical uncertainties. The research results will also contribute to the development of new courses on model-based simulation, modeling and optimization of manufacturing processes, and uncertainty analysis in engineering design.

This collaborative research between University of Illinois at Chicago and Northwestern University is funded under NSF 00-26, Exploratory Research on Model-Based Simulation.
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DESCRIPTION (Verbatim from Applicant's Abstract): Electrical injury is one of the common traumas in the civilized countries. It is important to understand the mechanisms of electrical injury in order to develop efficient therapeutic techniques and management procedures for electrically injured patients. The current accepted mechanism involved in electrical injury is cell membrane electroporation resulting in leakage of ions and cellular contents out of the cells. Our hypothesis is that electro-coupled conformational damages in the membrane proteins, especially the voltage-dependent proteins may also play an important role in electrical injury. This is because approximately 40 percent of the cell membrane, byweight, consists of proteins functioning as ionic channels, transporters and signal receptors, which are often sensitive to the changes in the membrane potential. Therefore, these membrane proteins are vulnerable to an intensive electric field. In the last few years, we have systematically investigated the supramembrane potential shock induced functional changes in the voltage-dependent Na+ and K+ channels on the membrane of skeletal muscle fibers. We found that these shock field induced channel functional alteration was mainly not due to the Joule heating effects but most likely due to the field induced protein conformational changes in the channel gating system. To prove our hypothesis and reveal insight mechanisms involved in the electrical injury, we will continuously study the electroconformational damages in the membrane proteins. We will study the shock field induced changes in the Na+ channel gating currents to provide direct evidence of the proteins conformational changes. Based on our previous studies, we will further consider the skeletal muscle fiber as a system, instead of individual proteins, to investigate the intensive shock effects on the fiber electrical excitability and the muscle fiber excitationcontraction coupling process. These studies will lead to a comprehensive understanding of the underlying mechanisms involved in electrical injury.

This award provides funding for the continued conduct of an open workshop on decision-based design. The workshop will be conducted via a series of face-to-face meetings, and with continuing interaction between all interested parties via electronic and Internet technologies throughout the three-year continuation period. Specifically, the workshop will include the continuation of a web site for conduct of the workshop. A primary component of the workshop will be the continual, open development and dissemination of the philosophical, theoretical and practical results of the workshop discussions and activities. The workshop will continue to concentrate on a diversity of research efforts directed at the development of a theory of engineering design based on the emerging notion that engineering design is a decision making process amenable to the tools and theories that have developed in the field of decision theory. Specific goals for this phase of the workshop include continued development of a mathematically sound theory of engineering design, the building of a repository of foundational materials (a lexicon, case studies, references, text materials), the establishment of ties between decision-based design and other areas of science, and the transfer of decision-based design tools to industry. The workshop web site address will be located at:
http://dbd.eng.buffalo.edu/

This workshop will provide new directions for research and education in engineering design. New theoretical concepts should lead to approaches to engineering design that will enable the design of better products, products that have greater market appeal, cost less, have lower environmental impact, and that are more friendly for customers. Further, these concepts are likely to lead to entirely new approaches to engineering education, with a strong focus on engineering design as a decision making process.

This research deals with the integration of a Discrete Choice Analysis (DCA) based approach to demand analysis with a Decision-Based Design (DBD) framework that supports a company's goal of making profitable products. The research will address four major topics, including (1) improving predictive capability of demand analysis, (2) incorporating behaviors of multiple market players in demand modeling, (3) predicting uncertainties in demand analysis, and (4) extending engineering modeling for DBD implementation. The proposed approach and techniques will be demonstrated through a real vehicle engine design problem, and verified and validated based on the basic economic principles and the properties of a rigorous alternative selection method.

If successful, the proposed research will have an immediate impact on improving the predictive capability of demand analysis in product design. The proposed method can incorporate many important factors that are currently ignored, such as the behavior of competitors and other players in a market and the uncertainty issues in demand estimation. The proposed DBD framework will offer a rigorous engineering design approach that supports a company's goal of making profitable products. The work on extending engineering modeling is beneficial for bridging the gap between business and engineering modeling in product design. The proposed research is expected to extend the extant knowledge in engineering design as well as in market research. The research is also expected to have a broader impact on increasing industrial competitiveness and improving educational activities. Research is anticipated to contribute to education in the areas of engineering design theory and methodology, decision analysis in engineering design, quality engineering, and market research for engineering design. A strong collaboration is planned among the university and industry teams including regular seminars, visits, and co-supervisions of graduate students.

DESCRIPTION (provided by applicant): The metabolic syndrome, a concurrence of obesity, insulin resistance, hypertension and dyslipidemia, is associated with the risk for developing cardiovascular disease and type 2 diabetes. Factor analysis has generally been used in cross-sectional studies to describe features of the syndrome determined by the underlying latent factors. However, it is not clear whether the different dimensions of the metabolic syndrome change together over time and if the relationships among the various parameters change from childhood to adulthood. Path analysis (structure equation modeling), which can combine factor analysis and multiple regression models, can be an invaluable approach to study such questions. This proposal is aimed at examining longitudinally from childhood to adulthood the complex interrelationships among the long-term trends of the metabolic syndrome components and the latent factors by the path analysis. The Bogalusa Heart Study, a long-term biracial (black-white) epidemiologic study of cardiovascular risk factors from birth through 44 years of age, offers an unparalleled database to use for these analyses. The study cohort includes 1794 subjects who were screened at least 4 times beginning in childhood, with 12,346 observations. Four criterion metabolic syndrome risk variables included in the analyses are body mass index, homeostasis model assessment index of insulin resistance, systolic blood pressure and triglycerides/HDL cholesterol ratio. The complex pathways among the four metabolic syndrome components and the latent factors will be elucidated by the path analysis separately in childhood, adolescence and adulthood by race and sex. In addition, serial data will be used to develop a random effects model for each metabolic syndrome component to calculate the area under the curve (AUC, a measure of long-term burden) incremental AUC (total AUC - baseline AUC, a measure of linear and nonlinear long-term trends) and linear slopes for each subject. These AUCs will be analyzed to examine the clustering patterns and the complex pathways in terms of observed/expected ratio, intra-class correlation, factor analysis and path analysis by race and sex. The findings from the proposed analyses will extend our knowledge of the longitudinal evolution of the metabolic syndrome in blacks and whites, and males and females from childhood. Importantly, the analyses will aid the development of rational preventive strategies beginning in early life.

Membrane active transporters function as biological pumps by consuming energy from ATP molecules to move ions against their concentration gradients in order to build up an electrochemical potential across the cell membrane. This potential is critical for cell functions. Because many of these biological pumps are sensitive to electric fields, a fundamental question is whether it is possible to electrically control their function. Recently, this group developed a synchronization modulation technique for entrainment of Na/K pump molecules. By application of specially designed oscillating electric field to cells, the group has shown that it is possible to, first, force the pumps to work at the same frequency, and then, gradually accelerate the pump rate manyfold. In this project the group will continue to investigate the underlying mechanisms for this process and to explore potential applications. The broader impacts of this project are potential applications of this technique to treat diseases that involve a loss of the electrochemical potential across the cell membrane.

0736172
Chen
Detecting highly enriched uranium (HEU), consisting of greater than 20% U-235, is a critical concern due its potential for use in nuclear terrorism. Although there have recently been significant improvements in the development of scintillator materials and numerous advances in our understanding of the basic physical processes governing the transformation of ionizing radiation into scintillation light, no current scintillator has the ideal combination of stopping power, light output, and decay time. This research has the objective of developing a novel kind of nanostructured phosphor for radiation detection.

DESCRIPTION (provided by applicant): Modern neuroimaging such as fMRI and PET has opened up enormously exciting frontiers for studying brain function and human behavior. Understanding the relation between the imaging signals and the underlying brain physiology has become increasingly important and urgent. Although tremendous insights about neurovascular coupling have been gained, it is still elusive of the quantitative relation between brain metabolic energy and neuronal activity (i.e., neurometabolic coupling). Particularly important and intensely debated question is how much increases of cerebral metabolic rate of oxygen (CMRO2) are needed, or can be provided by brain for supporting task-evoked neuronal activity. The major challenge in this regard is the lack of a fast, noninvasive and quantitative method able to directly image absolute CMRO2 at both basal and activated brain states. The high-field 17O MRS imaging (MRSI) approach for imaging CMRO2 which has been successfully developed by us over the first funding cycle could overcome this hurdle. This innovative and unique approach offers a golden opportunity for studying the neurometabolic coupling and its impact on brain function. A large body of our preliminary results has not only demonstrated the feasibility, reliability and applicability of this 17O-based CMRO2 imaging approach, but also provided crucial evidence showing the importance and possible mechanism of cerebral oxidative metabolism for supporting brain energy at rest and during activation. These findings lead us to hypothesize that cerebral oxidative metabolism should be the major energy source for both resting and activated brain due to the tight neurometabolic coupling;however, its capacity might be limited which in large is used for intrinsic brain activity at rest;thus, there exist a strong correlation between task-evoked CMRO2 change and baseline CMRO2, and an energy compensation mechanism for utilizing brain energy during brain activation with optimal efficacy. This central hypothesis will be examined in the cat brain through four testable hypotheses and four specific aims by using 17O-based CMRO2 imaging approach combined with other established approaches including electrophysiology recording. This research will (i) highlight the importance of cerebral oxidative metabolism at both baseline and activated brain states;(ii) elucidate the mechanism of cerebral bioenergetics associated with neuronal activity and brain function;and (iii) provide the neurophysiology basis for modern neuroimaging techniques. Relevance to public health: This research could provide new insights about crucial impact of cerebral oxidative metabolism on the brain disorders and dysfunctions associated with oxidative metabolism abnormality. The success in developing the 17O-based CMRO2 imaging modality will further enhance the ability of MR technology in neuroscience discovery and potentially in diagnosis of brain diseases.

[unreadable] DESCRIPTION (provided by applicant): The long-term goal of this research is to understand how the olfactory system encodes odor information, and how olfactory sensory codes are transformed sequentially through different processing stages along the central projection pathways. Olfactory coding and processing have been extensively studied with two major approaches: (1) electrophysiology of single neurons, which can record neural activity at any tissue depth, but blindly without knowing network context in reference to upstream coding patterns; and (2) CCD camera imaging of spatiotemporal pattern of activated glomeruli, which is ideal for revealing the initial glomerulus-based codes, but lacks single-cell resolution and deep penetration required for exploring odor codes beyond the glomerular layer. This grant is aimed at bridging such a gap between single-cell physiology and large-scale CCD camera imaging, so as to unify the two large datasets already available in the literature. First, using a new transgenic mouse model, we will provide a direct comparison between the pre- and postsynaptic odor maps within the glomerular layer, and test the hypothesis that lateral circuits intrinsic to this layer can support interglomerular lateral inhibition and/or excitation for initial odor-map transformation. Second, by combining in vivo two-photon calcium imaging and targeted single-glomerulus dye labeling, we will perform a systematic analysis of odor ensemble codes carried by the mitral/tufted cells associated with a common glomerulus. We will test the hypothesis that both the overall size and distribution pattern of a glomerulus-defined active cell ensemble can be effective coding factors for odor intensity at least and maybe also identity. Finally, by imaging the mitral cell population with diverse glomerular projections, we will analyze the cross-glomerular odor ensemble responses in the context of corresponding glomerular activation patterns. We will study how the glomerulus-based odor codes break down into distributed mitral-cell population codes, and ask what is the benefit of redistributing odor signals which have just converged via the nose-to-bulb projection. Collectively, these studies should not only have a significant impact on our understanding of the neural basis of odor processing and discrimination, but could also yield novel and more general principles on how the brain transforms neural codes for achieving sensory and perceptive functions. PUBLIC HEALTH RELEVANCE The sense of smell plays an important role in our daily life style involving flavor and fragrance appreciation. Dysfunction of the olfactory system happens in many human diseases such as eating-related obesity and early development of Alzheimer's disease. The general goal of this grant in understanding the neural basis of odor coding and processing will not only help the diagnosis and treatment of these diseases, but will also promote people's life quality in general. [unreadable] [unreadable] [unreadable]

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. Adenosine triphosphate (ATP), which mainly formed in mitochondria through oxidative phosphorylation and F1F0-ATPase, provides energy for driving most cellular activities in the brain. This oxidative ATP metabolism plays essential roles in brain bioenergetics, function and disease. The sole approach possible for directly assessing the metabolic rate of ATPase reaction in situ is the use of in vivo 31P MRS combined with magnetization transfer (MT). However, previous work of perfused heart suggested that the apparent ATP synthesis rate measured by in vivo 31P MT approach was not coupled with the net rate of oxidative phosphorylation and its change. In contrast, our recent work has shown that the measured cerebral metabolic rate of ATP (CMRATP) via ATPase reaction is closely matched with the net oxidative phosphorylation rate in anesthetized animals and awaked human, and it is also sensitive to the change of brain activity induced by varied baseline activity and/or brain stimulation. These compelling findings have led to our central hypothesis: In vivo 31P MT approach should be suitable for measuring and imaging CMRATP, which directly reflects the net rate of oxidative phosphorylation of ADP for producing the majority of brain ATP molecules;and establishment of this in vivo approach can provide an invaluable, completely noninvasive neuroimaging modality for studying the central roles of oxidative ATP metabolism in regulating neuroenergetics associated with brain function and dysfunction. This hypothesis will be tested by four specific aims: 1) to further optimize and improve in vivo 31P MT measurements and quantification methods for accurately determining CMRATP using an animal model at high field;2) to conduct concurrent noninvasive measurements of CMRATP and the cerebral metabolic rate of oxygen (CMRO2) using our newly developed high-field in vivo 17O MRS imaging approach in resting brains with varied baseline activity levels, and to examine if CMRATP is sensitive to brain activity change, and if CMRATP correlates to the rate of oxidative phosphorylation under a wide physiological range of brain activity;3) to conduct functional activation studies using visual stimulation to examine if CMRATP increases in the activated visual cortex for supporting higher energy demand and stimulus-evoked neuronal activity;4) to conduct extracellular neuron-recording studies under resting and activated conditions, and to correlate electrophysiology results with CMRATP results for providing new insights into the neuro-ATP-metabolic coupling relationships in the resting and activated brain. The significance of this research lies in two layers: to establish a unique neuroimaging modality for imaging CMRATP: a most fundamental and direct measure of brain energy;and to understand the possible roles of oxidative ATP metabolism in neuroenergetics and neurophysiology for supporting brain function and work.

The University of Central Oklahoma, in collaboration with eight other universities and community and technical colleges, is continuing to enhance a statewide network of institutions to increase recruitment, retention and graduation of students in STEM disciplines. The Oklahoma project, STEM Double Bridge: Connecting High Schools, Community Colleges, and Universities for Tomorrow's Leaders in Science, Technology Engineering and Mathematics, has used their experience with a previously funded STEP award to develop activities and initiatives that target incoming high school students and community college transfers. The project's Summer Bridge program will provide a four-week, residential lecture and research experience to incoming freshmen who have expressed an interest in a STEM discipline. New to the Oklahoma program, this program element will incorporate five area high school teachers who will serve as advisors to the program and who will work with the students on their research projects. A second bridge, called the Fall Bridge, will target community and technical college students who are entering four-year programs. This one-week residential program will provide the students with a campus orientation and academic advisement, provide assistance in developing writing and mathematics skills, introduce the students to the library system, and provide sessions on research methods and guidance in reading scientific articles.

DESCRIPTION (provided by applicant): Low birth weight, an indicator of intrauterine growth restriction, has been demonstrated to be associated with adult cardiovascular (C-V) disease, type 2 diabetes and metabolic syndrome components, including body fatness, insulin resistance measures, blood pressure, and lipoproteins. The interrelationship of low birth weight to multiple components of the metabolic syndrome as a complex entity and the underlying genetic factors can be elucidated further using an advanced sophisticated statistical method like path analysis. The Specific Aims of the proposed research are 1) to examine the impact of low birth weight on metabolic syndrome components by using a path analysis model, longitudinally and cross-sectionally by growth periods, in black versus white individuals, and 2) to investigate the modifying effects of relevant candidate genes on the relationship of low birth weight to longitudinal trends of metabolic syndrome components from childhood to adulthood. These specific aims will be examined using existing phenotypes and candidate gene genotype data available in the Bogalusa Heart Study, a long-term biracial (65% white, 35% black) community-based study of the Natural History of C-V Disease beginning in childhood, since 1973. Study Cohort I for cross-sectional analyses by growth periods consists of 6,051 individuals who have State birth certificate information and metabolic syndrome components;Cohort II for longitudinal analyses consists of 2,775 individuals who have data on birth weight and serial measurements of metabolic syndrome components measured at least 2 times each in childhood and in adulthood during 1973-2008, with 16,276 observations;Cohort III (n=1,447, 990 whites, 457 blacks) for genetic analyses is a subset of Cohort II with candidate gene genotype data available. Single nucleotide polymorphisms (SNPs, n=618) in 21 candidate genes related to both birth weight and metabolic syndrome will be analyzed as individual SNPs, haplotypes and gene-gene interactions. Path analyses will be performed cross-sectionally and longitudinally to examine the relationship of birth weight to the metabolic syndrome components by haplotype groups. The modulating effect of the candidate genes will be tested in terms of significant differences in the path analysis parameters between haplotype groups. The genetic loci identified in the proposed research will provide a basis for further research on the pleiotropic effects and additive effects of multigenes, and more dense sequencing in significant candidate genes in subjects who have a low birth weight and extremes values of the metabolic syndrome variables. This has important implications for improving prenatal care and developing strategies beginning early to prevent adult C-V disease. PUBLIC HEALTH RELEVANCE: Low birth weight is an indicator of baby growth restriction before birth, and associated with adult heart disease, diabetes and risk factors such as obesity, high blood pressure, high cholesterol and high blood sugar. The findings from this research has important implications for improving prenatal care and developing strategies beginning early to prevent adult heart disease.

The proposed project is preparing scientists and mathematicians to enter the workforce or pursue graduate degrees in STEM fields by supporting between 30 and 42 academically talented but financially disadvantaged undergraduates pursuing degrees in biology, chemistry, computer science, engineering, mathematical sciences, and physics. The project is doing so by dovetailing with an existing NSF STEP grant at the University of Central Oklahoma that aims to create a seamless transition to university life for a cohort of incoming freshman STEM majors during a four-week Summer Bridge program. The project is sustaining efforts initiated with Summer Bridge initiative, providing financially disadvantaged Bridge students with academic support that enhances faculty and peer interactions by engaging them in a comprehensive orientation program, effective academic advisement and tutoring, and undergraduate research.

Abstract
1039068
Wei Chen
University of Texas/Arlington

This interdisciplinary proposal brings together expertise in nanophysics, particle physics, and nuclear engineering to develop a novel radiation detector, while simultaneously establishing a strong academic program for Homeland Security through integrated research and education activities at the University of Texas, Arlington. Cerium-doped nanocomposites have shown promise as scintillators, but typically emit in the ultraviolet (UV) and are difficult to convert into uniform and transparent crystals. The PI proposes to use energy transfer to quantum dots to convert the UV emission into the visible range, and to develop methods for encapsulating nanoparticles in thin films that can then be combined into crystals to form practical detectors. Various parameters, such as the size of the quantum dots and the details of the crystallization process, provide significant technical challenges, but the payoff of demonstrating a new scintillator combining the desirable properties of nanocomposites, such as high stopping power, high sensitivity, and excellent energy resolution, with the high quantum efficiency and short decay lifetime of quantum dots is potentially enormous. Replacing traditional crystals with these new nanocomposite crystals would have a huge broader impact, allowing low dose detection of nuclear materials to help prevent the illicit transport of radiological materials into the United States.

DESCRIPTION (provided by applicant): Although the relationship between low birth weight and elevated blood pressure (BP) levels at various stages in later life has been well established, the association of birth weight with BP variations from childhood to adulthood has not been fully examined. In addition, the complex relationships among prenatal and postnatal growth, BP and subclinical changes of cardiovascular (C-V) structure and function need to be elucidated. The Specific Aims of the proposed research are 1) to demonstrate the inter-relationships among birth weight, longitudinal obesity measures and BP (levels and variability) from childhood to adulthood in black and white individuals, and 2) to determine the effect of birth weight in conjunction with BP (variability and levels) and postnatal growth on adulthood subclinical changes of C-V structure and function (arterial wall thickness, vascular stiffness and left ventricular structure and function). These specific aims will be examined using available longitudinal database from the Bogalusa Heart Study, a long-term biracial (65% white, 35% black) community-based study of the Natural History of C-V Disease beginning in childhood. The proposed study Cohort I for cross-sectional analyses consists of 6,458 individuals who have data on birth weight, growth parameters and BP in preadolescence, adolescence and adulthood;Cohort II for longitudinal analyses consists of 1,695 individuals who have data on birth weight and serial measurements of BP obtained at least 3 times each in childhood and at least 3 times in adulthood;Cohort III consists of 1,194 individuals who have data on birth weight, serial measurements of BP since childhood and subclinical changes of C-V system in adulthood. Long- term BP levels will be measured as the area under the curve derived from serial BP measurements from childhood to adulthood. Four BP variability measures (age-related trend, deviations around age-predicted values, deviations from mean and yearly rate of variability) will be calculated using longitudinal BP measurements. Path analysis (linear structural equation modeling) will be performed cross-sectionally by growth periods as well as using long-term BP level and variability measures. Interaction regression models along with principal components analysis will be applied to examine the joint effect of birth weight, obesity measures and BP on subclinical changes of C-V system. Findings from this research will provide further insights into the "fetal origins" of the development of hypertension. Deepening our understanding of the relationships among prenatal and postnatal growth, hemodynamics and subclinical C-V disease later in life has important implications for improving prenatal care and developing strategies beginning early in life to prevent adult C-V disease from hypertension. PUBLIC HEALTH RELEVANCE: Low birth weight is an indicator of baby growth restriction before birth and associated with adult heart disease, diabetes and hypertension. The findings from this research have important implications for improving prenatal care and developing strategies beginning early to prevent adult heart disease.

DESCRIPTION (provided by applicant): Tobacco use is one of the most potent avoidable causes for cardiovascular (C-V) disease. Epidemiologic studies indicate that even small amounts of secondhand smoke (SHS) exposure increase C-V disease risk. However, the long-term effect of childhood SHS exposure on the life course C-V disease risk is not clear. Importantly, data on dose-response relationship, interaction of SHS with C-V risk factors and racial (black-white) divergence are limited. The proposed research is designed to test the hypothesis that childhood SHS exposure and its impact on C-V disease risk from childhood to adulthood vary by race within a black-white population. In response to the FOA (PA-11-244) Effects of Secondhand Smoke on Cardiovascular and Pulmonary Disease Mechanisms (R01), the proposed research is directed to the following hypothesis-based Specific Aims: 1) to examine the impact of childhood SHS exposure on longitudinal C-V risk variables profile from childhood to adulthood in black versus white nonsmoking cohorts. The C-V risk variables include obesity measures, glucose, insulin, lipids and blood pressure measured serially since childhood, and markers of inflammation, oxidative stress and endothelial function measured in adulthood; 2) to determine the independent effect of childhood SHS and its joint effect with C-V risk variables on adult subclinical C-V structure/function changes in black versus white nonsmokers. The subclinical C- V structure/function measures include carotid artery intima-media thickness, aorta-femoral pulse wave velocity, arterial wall compliance, and left ventricular geometric remodeling and function; 3) to determine the joint effect of childhood SHS and birth weight on longitudinal C-V risk variables profile from childhood and adult subclinical C-V structure/function changes in black versus white nonsmokers. The above specific aims will be examined using a longitudinal cohort followed since childhood in the Bogalusa Heart Study, a long-term biracial (black-white) community-based epidemiologic study of C-V disease beginning in childhood since 1973. The proposed study cohort consists of 800 white and 800 black adults, aged 29-52 years, who already have data on C-V risk factor variables measured serially 6 times from childhood and birth weight in the entire cohort and adulthood subclinical C-V structure/function measures on 50% of the cohort. The findings from the proposed research will provide further insights into the potential underlying mechanisms of C-V disease and help in the implementation strategies of primordial prevention in early life. PUBLIC HEALTH RELEVANCE: Cigarette smoking is one of the avoidable causes for cardiovascular disease. Even small amounts of secondhand smoke exposure increase cardiovascular disease risk. Findings from this study will provide further insights into the potential underlying mechanisms of C-V disease associated with the long-term influence of secondhand smoke exposure in childhood, help strengthen the preventive strategies in early life, and promote comprehensive smoke-free legislation.

Many engineering programs struggle to meet the accreditation requirement that all engineering students have "the broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context." As a result, engineering students receive meaningful contextual experiences in a piecemeal fashion and graduate with a lack of concrete competencies that bridge knowledge and practice in the global world in which they live and work. By considering products as designed artifacts with a history rooted in their development, the product archaeology framework combines concepts from archaeology with advances in cyber-enhanced product dissection to implement pedagogical innovations that address the significant educational gap. With an archaeological mindset, students approach product dissection with the task of evaluating and understanding a product's (and its designers') global, societal, economic and environmental context and impact. These hands-on, inductive learning activities require students to move beyond rote knowledge to hone their engineering judgment, extend and refine their knowledge, and apply their knowledge in meaningful ways to realistic challenges. This pedagogical framework thus provides students with formal activities to think more broadly about their professional roles as engineers. This project, which is a collaboration among 6 universities, focuses on assessing the learning outcomes of exercises developed within the product archaeology framework. By documenting the implementation characteristics of the exercises at each school (for example, is the course a required or elective course, how many students are enrolled, is it a design or analysis course, etc.) and assessing the learning outcomes both quantitatively and qualitatively, the project is developing strong evidence of what factors enable engineering students to develop an understanding of the broader impacts of their decisions.

DESCRIPTION (provided by applicant): The cardiovascular (C-V) aging in terms of arterial and cardiac structure/function (CV indices) reflects complex interplay between the intrinsic aging effect, burden of CV risk factors in a genetic background and environmental changes beginning in early life, including in utero. This interplay is governed by gene expression mediated in part by epigenetic mechanisms. This renewal application focuses on these aspects in a community-based, black white cohort entering midlife and followed since childhood in the Bogalusa Heart Study. The Specific Aims of the proposed research are: 1) to continue characterizing the trajectories of cardio-metabolic risk variables since childhood and the familial longevity trait in relation to the arterial and cardiac structure/function (C-V indices); 2) to test the hypothesis tht birth weight affects longitudinal changes of DNA methylation (genome-wide and in candidate genes) during adulthood by race; 3) to test the hypothesis that there is a temporal relationship between DNA methylation patterns (global and gene-specific) and C-V risk variables measured over 12-20 years; and 4) to determine effect of global and gene-specific DNA methylation in conjunction with candidate gene variants and birth weight on subclinical C-V indices. The proposed study cohort consists of 800 white and 550 black unrelated adults, aged 29-52 years, who have C-V risk factor variables measured serially 6-15 times from childhood, birth weight, adulthood C-V indices, genotype data (38 candidate genes) and stored blood samples (baseline and follow-up, 12-20 years apart). The C-V indices include left ventricular mass and geometric remodeling, carotid artery wall thickness and compliance; candidate genes include those related to C-V risk, fetal growth and one-carbon metabolism. DNA methylation will be measured for the whole genome and 38 genes using Illumina HumanMethylation450 BeadChip. Multivariable regression analyses will be used to examine the birth weight-methylation, gene-methylation and methylation-outcome associations. Findings from this research will further the understanding of the predisposing factors that influence C-V aging in a biracial population reaching mid-life, which have implications for preventive strategies.

DESCRIPTION (provided by applicant): Lasers and nanomaterials are two of the most significant technological advances of the 20th century. The combination of lasers and nanotechnology holds great promise in the fields of biology and medicine. The long- term objective of this project is to develop a multifunctional bionanoprobe for targeted drug delivery, tumor imaging, and photoimmunotherapy for metastatic cancers. The PIs plan to use a novel method (patent pending) to construct a stable solution of single-walled carbon nanotube (SWNT) using a highly potent immunostimulant, glycated chitosan (GC), as an effective surfactant. In this project, the PIs plan to construct, simulate, and calibrate this bionanoprobe, determine its physical and biological characteristics, study its interactions with normal and tumor cells, investigate its toxicity, and determine its photoimmunological effects for treatment of metastatic tumors both in vitro and in vivo. We assume that this bionanoprobe can carry different molecules into tumor cells. We further hypothesize that this novel bionanoprobe, when localized inside tumor cells and combined with laser irradiation of appropriate wavelengths, can induce temporally and spatially synchronized photothermal and immunological reactions for treatment of metastatic cancers. The novelty of this bionanoprobe lies in the unique molecular structures of SWNT and GC so that a strong non- covalent bond can be formed for a stable nanotube solution. Furthermore, selective photothermal interaction can be achieved using in situ SWNT-GC with an absorption peak in the near-infrared region. This ensures a synchronized spatial and temporal thermo-biological effect in target tissue during non-invasive laser irradiation. In this project, the PIs will modify the bionanoprobe with fluorescent molecules to study its cellular distribution and to explore its capability to carry different peptides and therapeutic agents into tumor cells. The PIs will also use magnetic resonance thermometry to determine real-time, 3-dimensional temperature distributions in target tumor tissue during laser irradiation, which could help establish a correlation between temperature increases in tumor tissue and induced immunological responses. Specifically, the PIs plan to achieve the following aims: (1) To construct, calibrate, simulate, investigate, and modify the multifunctional bionanoprobe; (2) To investigate the cellular effects of the multifunctional bionanoprobe; and (3) To determine in vivo efficacy of laser-SWNT-GC for treatment of metastatic cancers. Successful completion of this project will pave the way to future explore the potential applications of this novel multifunctional bionanoprobe in biology and medicine, particularly in cancer treatment.

Understanding customer preferences and needs is critically important in developing successful products. This award supports an interdisciplinary research to develop a data-driven mathematical approach for analyzing and predicting consumer preferences in design of sustainable engineering products, such as alternative fuel vehicles and smart appliances. The relations among customers and products are conceptualized as a complex network and analyzed using network theory and techniques. This study will help industry produce more competitive products in shorter time to market. The findings will contribute to the development of new techniques for analyzing large complex networks. Workshop and panel sessions on analyzing customers and products as networks will be organized for dissemination to a broader community. Research will be integrated with education through interdisciplinary undergraduate Design Certificate and graduate Design Cluster programs.

Analytical modeling of customer preferences in product design is inherently difficult as it faces challenges in modeling heterogeneous human behavior and product offerings. The novelty of the research lies in the employment of a Multidimensional Customer-Product Network (MCPN) framework, where separate networks of "customers" and "products" are simultaneously modeled, and multiple types of relations, such as consideration and purchase, product associations, and customer social networks are considered. The research will extend the Exponential Random Graph Model (ERGM) as a unified statistical inference framework for analyzing multidimensional customer-product relations and predicting unknown customer preferences (consideration or choice) under new design scenarios. Social influences on adopting "green" technology are analyzed in the same framework. Our approach overcomes the limitations of the traditional statistical analysis and utility-based preference modeling by considering the dependency among product choices and the social influence induced "irrationality" of customer behavior. We will also explore the use of text analysis of customer-generated data in social media thereby creating crowdsourced "virtual labs" for advancing data analytics and computational social science in product design.

? DESCRIPTION (provided by applicant): Magnetic resonance (MR) imaging (MRI) and in vivo MR spectroscopy (MRS) techniques have become indispensable tools for imaging brain structure, function, connectivity, neurochemistry and neuroenergetics, and for investigating neurological disorders. However, it remains a challenge to achieve superior MRI/MRS detection sensitivity, spatial and temporal imaging resolutions adequate for addressing fundamental and challenging neuroscience questions even with the most advanced technology. The prevailing paradigms for improving MRI/MRS performance largely invoke increasing the magnetic field strength, which may have reached practically achievable limits for human studies due to many technological and safety (i.e., high specific absorption rate (SAR)) concerns, and increasing the receiver channel count which is also ultimately limited due to noise characteristics of coils of decreasing size. To alleviate these major limitations, this R24 proposal relies on the interdisciplinary research efforts and expertise of leading experts across two institutions to pioneer an entirely innovative engineering solution that uses the ultra-high dielectric constant (uHDC) material incorporated with ultrahigh-field MRI/MRS techniques for synergistically increase signal-to-noise ratio and concurrently reduce RF power demand, and for achieving unprecedented improvements in spatial/temporal resolution over the current state-of-the-art MR technologies. We will develop and optimize prototypes of uHDC material for human brain studies using 7 Tesla (T) and 10.5T whole-body human scanners. Moreover, we will exploit and assess the new utility and capability of the innovative uHDC-MR technology for cutting-edge neuroscience research. One pilot study is the functional mapping of neural circuits and resting-state connectivity at the level of columns and cortical layers in the human visual cortex with ultrahigh spatial resolution 1H MRI at 7T, complemented with anatomical connectivity derived from diffusion weighted images for tractography. The other one is to combine the uHDC technique with newly developed in vivo 31P and 17O MRS techniques for noninvasively and reliably imaging the cerebral metabolic rates of oxygen consumption and ATP, cerebral blood flow, oxygen extraction fraction and nicotinamide adenine dinucleotide (NAD) redox state in the human brain at resting and activated states. The proposed research will shift the current paradigm of neuroimaging development towards an efficient, cost-effective engineering solution that will attain multiplicative gains from uHDC and ultrahigh fields, and lead to next generation o MRI/MRS technology and instrument. Such advancement will accelerate human brain imaging and neuroscience research beyond what can be achieved through existing technology, promote new research directions, and transform our understanding regarding the human brain function and dysfunction.

DESCRIPTION (provided by applicant): Age-related macular degeneration (AMD) is a leading cause of blindness in the elderly population of Western countries. In the past few years, over one dozen AMD risk loci have been identified through genome-wide association studies (GWAS), either by individual studies or through meta-analyses of multiple studies from the National Eye Institute (NEI) supported AMD Gene Consortium. An ongoing exome chip experiment on 38,000 AMD/Control subjects will further expand the list by discovering additional rare variants. However, the analyses and statistical methods are still lagging behind the pace of data generation. Emerging genetic and phenotypic data from our collaborators, the AMD Exome Chip Consortium, and public databases (e.g. the dbGaP) will allow us to test new hypotheses, develop and calibrate statistical methods to facilitate ongoing consortium studies in which we are involved. In particular, we are interested in systematically studying the genetic causes and prediction of AMD progression, identifying disease-susceptibility loci in a cohort of African Americans, and developing association methods for family-based studies with binary traits. To achieve these goals, we propose specific aims as follows: 1) To develop a bivariate survival framework to jointly model AMD progression in both eyes and to perform a genome-wide association study of AMD progression using over 4,000 eligible samples from AREDS (Age-Related Eye Disease Study), AREDS2, and the AMD study conducted at the University of Michigan; 2) To develop and validate rigorous statistical models for prediction of AMD occurrence and progression based on demographic, clinical, and genetic information from the results of Aim 1 and to obtain predictive probabilities accounting for different study designs and the correlation between two eyes; 3) To develop and apply novel methods to identify loci associated with AMD risk in 725 unrelated African Americans, combining signals from both association and admixture mapping; and 4) To develop a statistical method for rare variant association tests of binary traits in families under the framework of generalized linear mixed model using a functional modeling approach and to apply the method to our UCLA- Pittsburgh family-based study of 2,188 samples. Our results will advance our understanding of pathogenesis and prevention of AMD occurrence and its progression. The methods we developed and applied will be available to other study groups and will benefit the analysis of ongoing AMD consortium data. In addition, our methods can be applied to other vision research as well. Unique strengths of our research team include: extensive prior experience in the applied analyses of AMD data sets, outstanding statistical genetics expertise, and clinical consultants with deep insight into the AMD data sets they collected. Successful completion of our Aims, where we will develop and apply state-of-the-art statistical methods, will enrich our understanding of AMD pathogenesis and improve individual risk prediction, and therefore will help enhance clinical practice.

DESCRIPTION (provided by applicant): Emerging sequencing technologies have made whole-genome sequencing become available for researches to study various phenotypes/diseases of interest, particularly focusing on rare variants sites. Although the first batch of sequencing projects has mainly focused on the analysis of unrelated individuals, numerous sequencing studies including related individuals have been carried out or launched recently as the sequencing cost reduces rapidly. However, the methodologies for analyzing family-based sequence data are largely falling behind partially due to the complexity of family structures and computational barrier. In this study, our primary goals are to efficiently and accurately infer individual genotypes and haplotypes - the key component of any sequencing project - by combining information from both family and population levels, and to study how differential sequencing errors will affect downstream association analysis. To achieve these goals, we propose specific aims as follows: 1) We will propose a novel statistical framework for genotyping calling and haplotype inference of sequence data including relative individuals. The new method takes advantages of both short stretches shared between unrelated individuals and long stretches shared between family members in a computationally feasible manner while retaining a high degree of accuracy via the synergy between two classic approaches: hidden Markov model (HMM) for linkage disequilibrium information and Lander-Green algorithm for inheritance vectors; 2) We will develop an exact algorithm for HMM computation to speed up a class of widely use genetics programs, including the method developed in Aim 1, without any sacrifice of accuracy; 3) We will assess the impact of sequencing errors on family-based association methods for rare variants and use the intrinsic stochastic nature of the proposed methods in Aim 1 to reduce the false positives under a framework of multiple imputation; 4) We will test and recalibrate our developed methods in collaboration with ongoing sequencing projects and systematically investigate different study designs. Successful completion of these aims will yield state-of-the-art statistical methods and software, which will facilitate the fast growing sequencing projects including family members and guide the design and analysis of future studies.

Project Description Functional MRI (fMRI) based on the blood oxygenation level dependent (BOLD) contrast has become a powerful neuroimaging modality and has gained a prominent position in neuroscience for imaging brain activation at working state and functional connectivity at rest. However, most of fMRI research focus on functional mapping of brain activity at the system level with macroscopic scale. Recently, high-resolution fMRI at ultrahigh field has shown the feasibility of mapping the functional activity of elementary computational units from ocular dominance to orientation column. Such unprecedented neuroimaging ability opens up exciting opportunities for studying brain function, connectivity and circuitry at the mesoscopic scale. Nevertheless, the neural computational processes are distributed across six cortical laminae spanning from the pial surface to the white matter, and engage feed-forward, feed-backward and local connections that are segregated according to the cortical depth. Ability to map such laminar and columnar dependent functionality and connectivity across large networks is extremely challenging and has not been achieved to date. Moreover, the BOLD signal only reflects the secondary effect of neuronal activity, the transformation between the BOLD measure and the underlying neural activity becomes complicated at varied spatial scale, and the neuro-BOLD correlation at the laminar/columnar level has not been studied due to a variety of technical hurdles. Another highly relevant unanswered question in fMRI is how does neuronal inhibition change the neural dynamics and networks, and the fMRI BOLD signal. Owing to the high complexity of normal brain activities unavoidably involving both excitatory and inhibition processes, it is a daunting challenge to selectively study the neural correlate of BOLD to inhibitory neuromodulation. To address these questions and challenges, this proposal aims to push the technology envelope beyond the current level by developing innovative multimodal fMRI approaches capable of simultaneous neural stimulation, recording and fMRI acquisition with functional mapping specificity and resolution down to the mesoscopic scale. The cutting-edge technology and developed tools will allow us to investigate brain function and connectivity at cellular columnar and laminar levels?two most fundamental neural computational units for micro-circuits essential for brain function, and still cover large networks through thalamo-cortical and cortico-cortical connections in the cat brain. For the first time, the research will provide new knowledge about the neural dynamics in space and time, and neural correlates of fMRI BOLD signal in response to excitatory or inhibitory neuromodulation at laminar/columnar levels. Such knowledge is impossible to gain from the human brain research, but should lead to transformative breakthroughs in understanding the structure-function relationship of defined computational units, dynamic functions and networks of the human brain; and provide new insights into electrophysiology basis and mapping specificity of fMRI at the laminar and columnar levels.

The immediate goal of this application is to understand how osteoclast (OC) differentiation is regulated through negative endogenous regulators, which may provide novel therapeutic targets for bone diseases, such as osteoporosis and rheumatoid arthritis (RA). Although positive regulators of OC differentiation through the receptor activator of nuclear factor kB (RANK) ligand (RANKL)-RANK signaling axis have been extensively studied, understanding of negative regulators of OC differentiation is elusive. To identify key negative regulators of OC differentiation, we utilized microarray, gene downregulation, and osteoclastogenesis assays, leading to the isolation of guanine protein alpha subunit 13 (G?13, encoded by the Gna13 gene) as a potential OC negative regulator. To enable in vivo investigation of the role of G?13 in OC differentiation, we generated an OC-lineage specific Gna13 conditional knockout (CKO) mouse model by crossing Gna13f/f mice with LysM-Cre (OC precursor specific) mice. Our preliminary data showed that Gna13f/fLysM-Cre mice exhibited a severe osteoporosis from a drastic increase in OC differentiation. We also noted that G?13 deficiency attenuates RhoA activity and promotes Akt activity in OCs. Consistently, we showed that, as a proof of principle strategy, local constitutively active G?13 overexpression can protect against bone and cartilage loss while also attenuating inflammation in a mouse model of RA. Based on our preliminary data, we hypothesize that G?13 is a key negative regulator of OC that controls OC cell lineage commitment and differentiation for bone homeostasis under physiological and pathological conditions through activating RhoA and attenuating AKT signaling pathway. Three specific aims are proposed to test our hypothesis. In Aim 1, we will characterize the bone phenotypes of Gn?13f/fLysM-Cre mice and elucidate the mechanism underlying how G?13 activates RhoA signaling and attenuates Akt signaling to negatively regulate OC cell lineage commitment and differentiation. In Aim 2 we will determine the upstream G?13 signaling cascade in OCs through characterization of G protein coupled receptors (GPCRs) coupled with G?13. We will characterize the mechanism underlying the roles of G?13 in bone remodeling and pathological bone loss by using dendritic cell (DC)-, monocyte- and OC-specific Gna13 CKO and overexpression transgenic mice in the loss-of-function and gain-of-function analysis, respectively. This study may provide important insights into the roles of negative regulators of OC differentiation in bone homeostasis and osteolytic bone diseases of excessive OC differentiation. Knowledge gained from this study may generate potential therapeutic targets, through characterization of G?13 signaling in OCs that may be targeted in treating osteolytic bone diseases by mimicking normal OC inhibitory signaling pathway to control OC formation. A multidisciplinary research team, including a bone biologist with expertise in OC biology and animal models of bone diseases; a bone biologist with expertise in OC biology and cell signaling; and an immunologist with expertise in autoimmunity diseases including RA has been established to achieve the research goal.

Project Summary Metastasis causes treatment failure and 90% of cancer-related deaths. We have developed laser immunotherapy (LIT) for the treatment of metastatic cancers. LIT combines local laser photothermal therapy (PTT) and immunotherapy using glycated chitosan (GC), a novel immunoadjuvant, to induce systemic antitumor immune responses. LIT has shown success in clinical trials for late-stage, metastatic cancer patients who had failed conventional treatment modalities. Specifically, LIT has demonstrated its ability to eliminate treated primary tumors and eradicate untreated metastases at distant sites. However, the mechanism of LIT, particularly how it controls tumor metastases, has not been fully investigated. Based on our previous studies, we believe that PTT and GC each induces unique immunological reactions, which, when synergized, produce a systemic, long-term antitumor immunity. Specifically, we hypothesize that: 1) PTT induces immunogenic tumor cell death (ICD), providing antigen sources and damage-associated molecular patterns (DAMPs) to trigger host antitumor immune responses; 2) GC enhances uptake and presentation of antigens generated by PTT-induced ICD, amplifying antitumor T cell response; and 3) LIT synergizes and amplifies the immune responses induced by PTT and GC, particularly when combined with other immunotherapies, such as checkpoint inhibition, providing potent, systemic therapeutic effects, especially on tumor metastases. To test these hypotheses, we plan to achieve the following aims: (1) to determine the photothermal-immunological effects of PTT by determining temperature distribution in tumor tissue and by characterizing the ICD and DAMPs generated by PTT; (2) to determine the effects of GC on immune system during LIT, particularly on activation of T cell as well as enhancement of antigen uptake and presentation; and (3) to determine the effects of LIT, as well as the combination of LIT and checkpoint inhibition, on the enhancement of tumor immunogenicity and on controlling tumor metastasis. The successful completion of this project will achieve the following goals: (1) to obtain a comprehensive understanding of the immunological mechanism of LIT in eliminating and inhibiting metastases; (2) to lay the foundation necessary to advance LIT into an effective treatment modality for patients with a variety of metastatic cancers; (3) to facilitate the development of new, novel treatment strategies using synergistic immunological mechanisms similar to that of LIT, in combination with other complementary therapies, for patients with metastatic cancers.

The long term goal of this study is to develop a safer and more effective therapeutic approach to cure aging- associated osteoarthritis (OA). The immediate goal of this study is to characterize the mechanism underlying how core-binding factor beta (Cbf?) mediates articular cartilage regeneration and repair in aging-associated OA. Current therapeutic options for aging-associated OA are still limited to pain management and surgical intervention representing a significant concern in the aging population. Recent studies have shed light on the nature of OA genetic susceptibility and confirmed a number of candidate genes involved in damage of the articular cartilage, including Sox9, YAP, Wnt/?-catenin signaling and TGF?/BMP signaling. However, the root causes of the disease remain unclear. In our preliminary studies, we found that the expression of Cbf? decreases with age in mouse articular cartilage, while postnatal induced chondrocyte-specific Cbf?-deficient mice developed spontaneous OA-like phenotype characterized by articular cartilage degradation and subchondral bone intrusion, which was exacerbated with age. Notably, heatmap analysis of RNA-seq data showed that Cbf?f/fCol2?1-Cre and aging mice articular cartilage share very similar changes in the gene expression profiles compared with that of two-month-old mouse articular cartilage. Our qPCR data confirmed that the OA related gene expression changes in articular cartilage of aging-associated and Cbf? deficiency induced OA included downregulated Sox9, BMP7, ALK3 and upregulated Yap, Wnt5a/b, Wnt/?-catenin BMP2/4. In addition, AAV-CMV-Cbf? mediated Cbf? overexpression with local administration protected against surgical OA in mice. Based on our preliminary data, we hypothesize that deficiency of Cbf? is one of the main causes of articular cartilage degeneration in aging-associated osteoarthritis (OA), and Cbf? enhances articular cartilage regeneration and repair by modulating multiple key signaling pathways, including Wnt/?-catenin, BMP/TGF?, YAP and Sox9 signaling pathways. We will test this hypothesis through three specific aims. In Aim1, We determine the roles of Cbf? in articular cartilage regeneration and repair in aging-associated osteoarthritis through analyses of adult and aged Cbf?f/fAggrecan-CreER mice, and aged wild type mice in physiological and pathological conditions via a loss-of-function approach. In Aim 2, we characterize the function of Cbf? in articular cartilage regeneration and repair and preventing OA genesis in adult and aged mice via a gain-of-function approach using AAV-CMV-Cbf? and Cbf?OEf/fAggrecan-CreER gene overexpression models. We will dissect the mechanism underlying how Cbf? enhances articular cartilage regeneration and repair by regulating the Wnt/?-catenin, BMP/TGF?, YAP, and Sox9 signaling pathways in aging-associated OA in Aim 3. Insights gained from the proposed study will not only address the basic scientific question about the pathogenesis of aging-associated OA, but also will provide the foundation for the ultimate goal of facilitating the design of safer and novel therapeutic approach for aging-associated OA.

Middle age is a critical period for cardio-metabolic diseases, including coronary artery disease, hypertension and type 2 diabetes. The importance of impact of childhood adverse growth experience on adult cardio-metabolic diseases has long been recognized. Telomere length and DNA methylation (DNAm) age are highly correlated with chronological age and considered as biomarkers of human aging. Recent studies have clarified that changes in telomere length and DNAm predict cardio-metabolic disease, atherosclerosis and mortality. Despite extensive studies on the early origins of cardio-metabolic disease and biological aging, the combined effect of prenatal experience, early life developmental patterns and cardio-metabolic aging process is not well understood and remains an important research area. This secondary data analysis proposal is directed to the following Specific Aims: 1) To examine the impact of early life growth trajectory patterns on telomere age, DNAm age and cardio-metabolic disease in middle-aged black and white adults; 2) To investigate the temporal relationship of BMI-telomere and BMI-DNAm and the effect of their temporal sequence patterns on adult cardio-metabolic disease; 3) To determine the mediation effect of telomere and DNAm aging biomarkers on the association of early life growth and risk burden with adult cardio-metabolic risk; 4) To examine whether race, gender and birth weight modify the association and mediation effect parameters. The cardio-metabolic outcomes include hypertension, type 2 diabetes, coronary artery disease and subclinical cardiovascular measures. Telomere length and DNAm age will be used as mediators. The specific aims will be achieved by leveraging the existing longitudinal cohort of the Bogalusa Heart Study followed from childhood since 1973. We propose to analyze two cohorts: a longitudinal cohort of 3,627 adults (2,285 whites and 1,342 blacks), aged 28-61 years, who have height, body mass index (BMI), skin folds measured 3-9 times in childhood and 2-6 times in adulthood, and a sub-cohort of 1,168 adults (747 whites and 421 blacks) who also have telomere data and genome-wide 450K DNAm profiles measured at two time-points 5-12 years apart in adulthood. Cross-lagged models for temporal relationship analysis, mediation analysis models, regression models and interaction analysis models will be performed. Findings on the impact of early life growth on cardio-metabolic disease in relation to biological aging markers will facilitate identification of high risk individuals and selection of novel therapeutic and intervention strategies in early life.

? DESCRIPTION (provided by applicant): This proposal is for the competitive renewal of the training grant, Integrative Training Program in Neuroimaging, at the University of Minnesota. Trainees in this program are pursuing a Ph.D. through the Graduate Program in Biomedical Engineering or the Graduate Program in Neuroscience that spans both College of Science and Engineering and Medical School involving faculty trainers from 11 departments including multiple engineering departments, radiology department, and various basic and clinical neuroscience departments throughout the University. We propose to train the next generation of scientific and technical leaders in the important emerging field of integrated neuroimaging. The primary objectives of the program are two-fold: first, it will provide integrative training to graduate students in biomedical engineering in order that they have a broad understanding of neuroscience and clinical applications and are capable of developing innovative neuroimaging techniques; second, it will provide training to neuroscientists so that they will have in- depth exposure to the imaging sciences. We believe it will benefit both students and faculty from multiple disciplines. Engineering students will be exposed to a wealth of challenging and real problems in neuroimaging to address important questions in basic and clinical neurosciences, while neuroscience students will gain expertise in new quantitative methods and imaging techniques. The proposal incorporates the strengths, resources, and administrative structures of existing graduate programs in biomedical engineering and neuroscience, with an interdisciplinary faculty with diverse research interests, to provide a new paradigm in graduate education. The predoctoral fellows will be trained across a variety of tools including special interdisciplinary coursework, research rotations, dual thesis advisors, special seminars and symposia and unique training opportunities including career skills development and training in responsible conduct of research. An advisory system will help guide students through the program. Summer industrial internships will be implemented to help train students to understand the needs of medical imaging and devices industry. On completion, the trainees will be well prepared for research career in academia, industry and government. The broader impacts include advancing our understanding of the brain, cross-fertilization of the disciplines, and establishing a new model for interdisciplinary training integrating neuroscience with imaging science. The successful completion of the proposed training program promises to change the landscape of the future neuroimaging field.

PROJECT SUMMARY    Understanding how neural circuits operate and interconnect at mesoscopic (sub-­millimeter) scale, and how  neuroenergetic  metabolism  and  neurotransmitters  support  brain  function  at  resting  and  working  state  is  essential to brain research and BRAIN Initiative. Magnetic resonance (MR) imaging (MRI), including functional  MRI  (fMRI)  and  in  vivo  MR  spectroscopic  imaging  (MRSI),  is  the  sole  modality  enabling  to  imaging  neural  activity,  functional  connectivity  and  brain  structure  at  cortical  layer  and  column  level,  neuroenergetics  and  neurotransmitters  in  human  brain.  However,  it  remains  challenging  to  address  fundamental  neuroscience  questions requiring much higher sensitivity and spatiotemporal resolution currently unavailable. Increasing MR  field  strength  has  been  the  prevailing  paradigm  to  tackle  the  challenge,  however,  beside  high  cost,  it  poses  a  safety concern from elevated specific absorption rate (SAR) of radiofrequency (RF) power in the brain tissue.     To  address  the  technical  challenges  and  limitations  faced  by  the  MR-­based  imaging  techniques,  we  have  pioneered  an  innovative  and  cost-­effective  engineering  solution  by  introducing  the  ultra-­high  dielectric  constant  (uHDC)  former  incorporated  with  RF  coils  for  large  improvements  of  sensitivity  and  spatiotemporal  resolution  for  fMRI and MRSI, and synergistically reducing SAR at ultrahigh field (UHF). With the NIH R24 funding support, we  have  made  progress  with  promising  results  for  proof  of  concept.  In  this  U01  proposal,  we  will  further  develop  and  integrate  three  advanced  technologies:  i)  fixed  and/or  tunable  uHDC  formers  incorporated  with  advanced  RF coil technology for maximizing MR sensitivity and minimizing SAR;? ii) SPectroscopic Imaging by exploiting  spatiospectral  CorrElation  (SPICE)  technique  for  significantly  boosting  signal-­to-­noise  ratio  (SNR)  and  spatiotemporal  resolution;?  iii)  UHF  MR  technology  for  further  improving  sensitivity  and  spectral  resolution  of  MRSI. The integration of these technologies will achieve cumulative and unprecedented improvements at UHF  and  break  current  barriers  of  spatiotemporal  resolution,  ultimately  enable  i)  ultrahigh-­resolution  fMRI  mapping  of neural activity, circuits and dynamics, and functional connectivity and networks at mesoscopic scale at 3 and  7  tesla(T);?  and  ii)  very  high  resolution  and  whole-­brain  multinuclear  MRSI  for  functional  mapping  of  neuroenergetic and neurotransmitter changes in response to brain stimulation at ultrahigh fields (7T and 10.5T)  with  an  superior  (£?5mm  isotropic)  resolution  comparable  to  conventional  fMRI.  The  technology  developments   will  be  carried  out  by  a  consortium  among  interdisciplinary  researchers  from  University  of  Minnesota,  Penn  State  University  and  University  of  Illinois  at  Urbana-­Champaign.  Success  of  this  project  will  usher  the  next   generation  of  MR-­based  multimodal  neuroimaging  technology  offering  superior  spatiotemporal  resolution  fully  transformative  for  broad  brain  research,  and  generate  comprehensive  and  high  fidelity  database  of  healthy  human brain that can be shared by scientific community.      

PROJECT SUMMARY Glioblastoma (GBM) is the most aggressive form of human cancers with very high fatality rate and short survival time, and the cancer cells aggressively infiltrate the brain and are intrinsically resistant to chemotherapy and radiation therapy. Intra-tumoral heterogeneity is a major challenge in therapeutic development for GBM patients because surgical acquisition of clinical specimens cannot be used to monitor the tumor progression and/or the underlying metabolic changes. Various neuroimaging methods have been used to study the morphology of the brain tumors. However, the need for noninvasively characterizing the brain tumors and their metabolic features has not been met, which should be critical for prognosis or for monitoring the tumor progression and response to treatment. It is well known that a common hallmark of the cancer cells is disrupted glucose metabolism, in which upregulated glycolysis is accompanied by inhibited mitochondrial oxidation, i.e., the ?Warburg effect?. Imaging the ?Warburg effect? and its spatial variability in brain tumors is a new attempt that can have a major impact on cancer research, particularly in the treatment of GBM, because therapies aimed at reversing the Warburg effect have shown promise in GBM ; however, great efforts are needed to develop novel metabolic imaging techniques to achieve the capabilities sought by clinicians. We have recently initiated a project aiming to develop a neuroimaging technique based on deuterium (2H) MRS (DMRS) detection of 2H-labeled brain metabolites following an administration of D-Glucose-6,6-d2 (d66). Our preliminary results indicate that the dynamic DMRS imaging can determine the cerebral metabolic rates of glucose (CMRGlc) and TCA cycle (VTCA), thus, the lactate production rate (CMRLac) in addition to the concentrations of deuterium-labeled glucose (Glc), mixed glutamate/glutamine (Glx) and lactate (Lac) in living brains. Furthermore, we demonstrated for the first time that the uncoupling between the glycolysis and oxidation in brain tumor can be quantitatively imaged via mapping the [Lac]/[Glx] ratio defined as an index of Warburg effect (IWE); and it has been shown that IWE is highly sensitive for distinguishing brain tumor from surrounding normal tissues. In this application, we are seeking NIH funding support to move forward with the DMRS imaging development through: i) integrated hardware and software development and the ultrahigh field MR technology to further boost signal-to-noise ratio (SNR), spectral resolution and spatiotemporal resolution; ii) testing the ultrahigh resolution DMRS imaging in healthy subject, and tumor patients and establishing a quantification model and imaging processing pipeline for future application; and iii) comparing the DMRS imaging results with the neuropathological and immunohistochemical findings of the biospecimens to understand the correlation between the DMRSI measurements and biological features of brain tumor. Our interdisciplinary research team with unique expertise is ready for a full-scale development of this highly innovative and cost-effective neuroimaging essential for basic research and clinic application in neuro-oncology.

The objective of this research is to investigate what product customers consider and what they eventually purchase using a hierarchical, multidimensional network-based design approach. Motivated by the need to model socio-technical interactions in engineering design, this research combines design theory with network science to explore three interrelated topics: 1) two-stage multidimensional network models for customer preference modeling that consider product associations and social influence; 2) dynamic network models for predicting the impact of multi-competitor strategic decisions, and 3) knowledge transfer to demonstrate generalizability and creation of shared data resources to benefit research community. This project will advance design theories of complex systems and develop quantitative methods for modeling socio-technical interactions in engineering design. Integrated with enterprise-driven design, the methods developed will enhance US industry?s competitiveness within changing markets. The test cases include a primary case study on the design of electric vehicles and small SUVs and a secondary case study on the design of household products. The project will also foster student training in data science, network science and Artificial Intelligence, with particular emphasis on the participation of underrepresented groups, females, and undergraduates.

The intellectual merit of this research is manifested in four aspects. First, the hierarchical network model studies customers? consideration and choice as distinct, but integrated, behaviors. It identifies distinctive driving factors underlying the consideration and choice stages. Second, this research overcomes the practical challenges of missing data on customers' social networks. The solution relies on an innovative approach to assess how individuals? preferences are influenced by their own egocentric social contacts through a synergistic integration of autologistic actor attribute model (ALAAM) with the Multidimensional Customer-Product Network (MCPN) framework. Third, using temporal Exponential Random Graph Model (t-ERGM), the dynamic network modeling approach will allow the prediction of future market competition considering the present competition structure and multi-competitor design decisions. Finally, a crowdsourcing-based data collection platform integrating online product data and reviews will be developed for eliciting customer preferences in multi-stage decision making.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Age-related macular degeneration (AMD) is a leading cause of irreversible blindness worldwide. Successful genome-wide association studies (GWAS) of AMD have identified many disease-susceptibility genes. Through great efforts from international GWAS consortium and large-scale collaborative projects, massive datasets including high-quality GWAS data and well-characterized clinical phenotypes are now available in public repositories such as dbGaP and UK Biobank. Clinically, color fundus images have been extensively used by ophthalmologists to diagnose AMD and its severity level. The combination of wealthy GWAS data and fundus image data provides an unprecedented opportunity for researchers to test new hypotheses that are beyond the objectives of original projects. Among them, predictive models for AMD development and its progression based on both GWAS and fundus image data have not been explored. Most existing prediction models only focus on classic statistical approaches, often regression models with a limited number of predictors (e.g., SNPs). Moreover, most predictions only give static risks rather than dynamic risk trajectories over time, of which the latter is more informative for a progressive disease like AMD. Recent advances of machine learning techniques, particularly deep learning, have been proven to significantly improve prediction accuracy by incorporating multiple layers of hidden non-linear effects when large-scale training datasets with well-defined phenotypes are available. Despite its success in many areas, deep learning has not been fully explored in AMD and other eye diseases. Motivated by multiple large-scale studies of AMD development or progression, where GWAS and/or longitudinal fundus image data have been collected, we propose novel deep learning methods for predicting AMD status and its progression, and to identify subgroups with significant different risk profiles. Specially, in Aim 1, we will construct a novel local convolutional neural network to predict disease occurrence (AMD or not) and severity (e.g., mild AMD, intermediate AMD, late AMD) based on (1a): a large cohort of 35,000+ individuals with GWAS data and (1b): a smaller cohort of 4,000+ individuals with both GWAS and fundus image data. In Aim 2, we will develop a novel deep neural network survival model for predicting individual disease progression trajectory (e.g., time to late-AMD). In both aims, we will use the local linear approximation technique to identify important predictors that contribute to individual risk profile prediction and to identify subgroups with different risk profiles. In Aim 3, we will validate and calibrate our methods using independent cohorts and implement proposed methods into user-friendly software and easy-to-access web interface. With the very recent FDA approval for Beovu, a novel injection treatment for wet AMD (one type of late AMD) by inhibiting VEGF and thus suppressing the growth of abnormal blood vessels, it makes our study more significant, as it will provide most cutting-edge and comprehensive prediction models for AMD which have great potential to facilitate early diagnosis and tailored treatment and clinical management of the disease.

Puerto Ricans (PRs) share a disproportionate burden of childhood asthma in the United States. Although genome-wide association studies (GWAS) have identified genetic variants associated with asthma and lung function, such variants explain a small proportion of the heritability of these complex traits. The next step is to identify epigenetic and transcriptomic risk markers for asthma and lung function, and to understand how they jointly influence these traits in children. However, most epigenomic and transcriptomic studies of asthma have thus far focused on blood. DNA methylation and gene expression in nasal (airway) epithelium are well correlated with those in bronchial (airway) epithelium, and sampling nasal epithelial cells is safer and more cost-effective than conducting bronchoscopies for studies of airway epithelium and asthma in children. We recently showed promising results in an epigenome-wide association study (EWAS) of childhood asthma using DNA from nasal (airway) epithelium. Despite their facts, nasal ?omics? data are rarely available and not included in the Genotype-Tissue Expression (GTEx) database. From 2006 to 2019, we used funding from NIH grants HL079966 and HL117191, as well as internal resources, to obtain genome-wide (GW) genotypic data, as well as GW DNA methylation and GW gene expression data in nasal epithelium, in a well-characterized cohort of Puerto Rican children and adolescents (aged 9 to 20 years) who participated in the Epigenetic Variation and Childhood Asthma in Puerto Ricans study (EVA-PR). Using data generated for EVA-PR and other studies, we have conducted: 1) a meta-analysis of GWAS of asthma in Puerto Ricans, 2) a GWAS of lung function in children with asthma, 3) an EWAS of serum total IgE in Hispanic children, and 4) an EWAS of atopy and atopic asthma in nasal epithelium from Puerto Rican children, with replication in two independent cohorts. On the basis of our preliminary studies, we hypothesize that understanding complex interaction among multi-omics data in nasal epithelium will further reveal epigenomic and transcriptomic profiles that are associated with asthma and lung function in Puerto Rican children and in children in other ethnic groups. To test this hypothesis, we will use an approach integrating our ?omics? data from nasal epithelium in EVA-PR with ancestry inference and an admixture mapping analysis. In particular, we will perform admixture mapping and integrate association signals for asthma and lung function measures with analyses of GW genotypic data, and GW DNA methylation and GW gene expression data from nasal epithelium in Puerto Rican children (Specific [Sp.] Aim 1). We will then examine the interplay among multi-omics data and quantify their relative contributions to asthma and lung function measures in Puerto Rican children (Sp. Aim 2). This proposal should identify markers of asthma and lung function in a high-risk population (Puerto Rican children), and advance our knowledge of the pathogenesis of asthma, which will ultimately help us develop new methods to prevent or treat childhood asthma in general, and in Puerto Ricans in particular.

A key to combat the Coronavirus Disease (COVID-19) pandemic is to prevent the pandemic from overloading the public healthcare system, so that sufficient medical resources could be available for hospitalized patients. This project will develop new mobile sensing and Artificial Intelligence (AI) techniques for in-home evaluation of COVID-19 infection in order to pursue automated and non-invasive screening of potential viral disease carriers. It aims to timely identify negative cases caused by other diseases with similar symptoms, and hence avoids unnecessary hospital visits as many as possible.

The proposed techniques will use commodity smartphones to measure the changes of humans? airway mechanics, which are uniquely correlated to COVID-19 infection. These measurements build on acoustic sensing with smartphones? built-in speakers and microphones. First, new acoustic waveforms will be designed to minimize acoustic signal distortion in human airways. Second, new signal processing techniques will be developed for accurate measurements. Third, deep learning techniques will be used to develop generic models that depict the core characteristics of airway mechanics. These techniques will be evaluated by lab testing and experiments with student volunteers. This research will enable identifying false positives of COVID-19 infection out of the clinic and could contribute to the containment of the virus spread and damage. The proposed technologies will be applicable to a wide variety of commodity smartphones and could also be used in daily practice with handmade mouthpieces. Broader impacts will also result from a variety of education and outreach activities. New courses will be developed to incorporate the outcome of this research, and the research outcome will be disseminated through technology transfer to industry. The outcome of this project, including source codes and collected data from student volunteers, will be maintained at the project repository (http://www.pitt.edu/~weigao/research_COVID19.html) for at least five years, and will be made available to the public community.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.