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Mechanobiological rejuvenation of mesenchymal stem cell therapeutics
Full text linkAs the worldwide population continues to age, there is an increasing need for improved regenerative therapies. Mesenchymal stem cells (MSCs) are a promising therapeutic cell type for a wide range of age-related degenerative conditions. A major limiter of current MSC therapies is cellular senescence, a process in which cells stop growing and lose their regenerative properties. Senescence in MSCs can be caused by poor donor health or advanced age and through expansion in cell culture prior to therapeutic implantation. In this work, we examined the effects of mechanical conditioning on senescence in MSCs. Using a novel technology developed in our laboratory, we applied cyclic mechanical stretch and drug treatment to MSCs derived from aged patients. We demonstrated that conditioning with physiological mechanical loading rejuvenates a broad range of aged MSC functionality, including, proliferation, culture expansion, multipotency, metabolism, and DNA damage repair mechanisms. A detailed analysis of transcriptomic and epigenetic changes induced by mechanical loading revealed that these lasting functional enhancements are likely due to broad changes in chromatin organization, reversing some of the gene expression patterns that occur during MSC senescence. Computational modeling studies of the forces applied to the MSCs during mechanical conditioning demonstrated that the optimal mechanical conditioning regime may unravel chromatin and enable re-organization of the genetic landscape without inducing physical DNA damage. Taken together, this work demonstrates that mechanical conditioning can rejuvenate aged MSC function and improve their prospects in cell-based regenerative therapies.Biomedical Engineerin
Aging alters genomic instability at endogenous mutation hotspots in mice
Full text linkAging is one of the most important risk factors for cancer initiation and development, with the majority of new cancer cases occurring in individuals over the age of 65. One potential link between aging and cancer is genetic instability. Alternative non-B DNA-forming sequences are endogenous sources of genetic instability that co-localize with mutation hotspots in human cancer genomes, and are processed by both replication-dependent and -independent mechanisms. Here, we used several in vivo and in vitro techniques to analyze the relationship between aging and DNA structure-induced genetic instability. Utilizing a novel mutation-reporter mouse model containing a human H-DNA-forming sequence from a translocation breakpoint hotspot in Burkitt lymphoma, we identified age-associated, tissue-specific effects of aging on H-DNA-induced genetic instability. DNA sequencing of the mutants revealed a correlation of increasing large deletion mutations with increasing mutation frequencies, and vice versa. Together, the data suggest that changes in H-DNA-induced mutation frequency with age may largely be attributed to changes in large deletion mutations. This may be due in part to tissue-specific modulations of DNA repair proteins with age that process H-DNA. Indeed, we observed a significant decrease in nucleotide excision repair activity in aged brain tissues, a mechanism involved in the processing of H-DNA. Further, there was a modest increase in the cleavage of H-DNA structures in aged spleen tissues. We also identified distinct, structure-specific roles for flap endonuclease 1, previously reported to suppress H-DNA mutagenesis, in the mutagenesis of two other alternative DNA-forming sequences in human cell lines. Our findings provide novel insights into the relationship between aging and cancer-associated genomic instability, aiding in the delineation of the underlying mechanisms of age-associated cancer etiology.Pharmaceutical Science
Harmonizing ecosystem services into life cycle impacts of modern electricity generation options
Full text linkTo limit global warming and avoid the worst potential impacts of climate change, the IPCC has called for net-zero global greenhouse gas (GHG) emissions by around mid-century. Achieving this requires a transition of our energy system, especially related to electricity generation. In the U.S., natural gas, wind and solar are the predicted dominant sources of electricity generation in the decades to come. In this study, we combine life cycle assessment (LCA) and biodiversity and ecosystem (BES) models to compare the environmental impacts of combined cycle gas turbine (CCGT), wind, and solar facilities, all at utility-scale. For solar, we assess both conventional and low-impact development scenarios (also called agrivoltaics). The area of interest (AOI) for this study includes 33 counties centered on the Midland Basin, spanning over 10.5 million hectares of west Texas. We use OpenLCA and ReCiPe 2016 impact approach to quantify life cycle impacts from cradle-to-gate. For BES, we use the InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) modeling suite to evaluate impacts to six dominant services. A new workflow was developed that places potential facilities throughout the AOI using a statistical approach weighted according to proximity to high voltage transmission lines and protected lands, identifies the land footprint of each facility, and assesses impacts to BES at each location. To compare across technologies and methodologies, we develop a new normalization metric, the Relative Environmental Impact Index (REI), that facilitates aggregation of results. For this study, we reduce the number of impact categories from 24 (18 LCA and 6 BES) to six different emissions compartments. Over an operational period of 30 years, LCA results indicate lower GHG emission-related impacts (97% reduction) and particulate matter formation (67% reduction) for wind and solar when compared to CCGT, as well as lower water use (88-99% reduction), while water-pollution impacts for wind and solar are higher (25- to 74-fold) compared to CCGT. BES model results highlight the importance of local landscape configuration and development practices, with impacts varying significantly based on facility location and implementation assumptions. Combined LCA-BES results indicate that CCGT generates the highest median REI for four of the six defined compartments. This research offers a more comprehensive evaluation of the environmental impacts of electricity generation options and provides a workflow that can assist future assessments and siting decisions at various scales.Earth and Planetary Science
Light-based mechanical patterning in soft matter materials
Full text linkNatural materials are made highly durable through ordering across multiple length scales. However, the current synthetic tools needed to mimic such structures are lacking. The ability to control the mechanical properties of all-polymeric systems across multiple length scales is therefore an attractive manufacturing capability for the production of next generation materials. Most demonstrations of mechanical patterning focus on multi-material systems, in which the chemical composition or crosslinking density are varied spatially. Little work has been done in understanding how morphology can be used to change material properties in a single composition film. Interpenetrating polymer networks (IPN) are a special type of polymer blend in which two or more polymer networks are entangled and crosslinked in a matter preventing separation without breaking chemical bonds. The resultant materials are often kinetically trapped, giving rise to non-equilibrium structures, making them a useful platform for the study of morphological patterning. First, a brief study examining salt and water transport in low-water double network hydrogel IPN is presented. A series of IPN is prepared with different incorporation of hydrophobic and hydrophilic comonomers. The effect composition has on water uptake, salt transport, morphology, and mechanical strength is characterized. The ability of the double network architecture to produce robust, yet functional membranes and current challenges is discussed. Next, a novel platform for IPN synthesis is developed using a delayed initiation of two orthogonal polymerizations. Differences in the time between the formation of rigid polyurethane and soft polyacrylate networks lead to differences in final morphology. The relationship of delay time, mixing, and thermo-mechanical behavior is characterized. The effect mixing and network interactions have on fracture toughness is briefly explored, to demonstrate the ability to produce a broad range of properties from a single composition resin. The use of a light-based synthetic technique allows for the patterning of different mechanical properties on the millimeter scale to produce anisotropic behavior. Finally, three-dimensional mechanical patterning is explored using a hybrid acrylic-epoxy monomer resin. Printing speed, mechanical resolution, and the range of patternable tensile moduli, allow for single-step preparation of meta-materials with mechanical properties defined by macroscopic patterns.Chemical Engineerin
Integrated analysis of NMR and electrical resistivity measurements for enhanced calibration-free assessment of throat-size distribution, permeability, and capillary pressure in carbonate formations
Full text linkThe genesis and diagenesis of carbonate rocks usually comprise changes in depositional sequences and multiple post-depositional processes. The interplay of these factors drives complexities in terms of texture, composition, and pore structure. Conventional petrophysical models do not reliably assess these complexities, requiring extensive calibration efforts or pore-scale image analysis. A recently developed method enabled assessment of pore-network properties such as constriction factor, pore-body- and pore-throat-size distributions as well as permeability and capillary pressure based on joint interpretation of Nuclear Magnetic Resonance (NMR) transverse relaxation time (T₂) distribution and electrical conductivity measurements. This method was tested earlier in the pore- and core-scale domains. However, the workflow for its application to the well-log-scale domain is yet to be developed and evaluated. This thesis focuses on (a) depth-by-depth quantification of rock fabric features (i.e., constriction factor, effective pore-body-size, and pore-throat-size distribution), (b) depth-by-depth estimation of permeability and capillary pressure by integrating electrical resistivity and NMR well logs, and (c) validating the reliability of the new workflow against experimental measurements. I successfully applied the new method to pre-salt carbonates of Barra Velha Formation, Santos’s basin, Brazil. I validated the reliability of the introduced workflow for assessment of permeability, capillary pressure, and throat-size distribution against core measurements obtained from different rock types. The applications of the new method for assessment of throat-size distribution in the core- and the well-log-scale domains have proven successful in 87% and 73% of the cases, respectively. The permeability estimates from the new method showed more than 22%, 110%, and 135% improvement when compared against those obtained from porosity-permeability correlations and NMR-based permeability assessment methods (e.g., Timur-Coates and Schlumberger-Doll-Research (SDR) methods), respectively. The introduced method provides the option of performing real-time and depth-by-depth assessment of pore-throat-size distribution and capillary pressure, which was not possible in the past. This leads to enhancement in in-situ permeability assessment and rock classification. Moreover, the proposed method minimizes the need for core-based calibration efforts and eliminates the need for detecting cutoff values in NMR-based permeability assessment.Petroleum and Geosystems Engineerin
Artificial language systems reveal the functional organization of language in the brain
Full text linkTo derive meaning in language, the human cerebral cortex integrates information at several different timescales- from phonetic and lexical information at sub-second scales to discourse-level information accumulated over several minutes. While language neuroscience has investigated distinct aspects of language processing, like phonological mapping and the encoding lexical semantics, the functional organization of this system remains widely debated. In this work, we present a computational framework to model language processing in the brain using artificial neural networks (ANN). We show that by adding interpretability and control to the artificial networks, we can infer functional properties of different cortical regions. Following this, we discuss how these computational models can be used to simulate neuroimaging experiments in silico. Finally, we develop computational models of phrase-level processing to characterize the semantic and temporal integration properties of different cortical regions. Through this, we propose a revised account of the cortical organization of semantic processing.Computer Scienc
Improving network pavement performance management using machine learning
Full text linkHighway networks play a significant role in people’s daily life. With limited funding and increasing demand, it is critical for transportation agencies to maintain the condition of the highway network cost-effectively. This necessitates the development of sound network pavement performance models and optimization approaches to select appropriate maintenance and rehabilitation (M&R) strategies for pavement performance management at the network-level. A key challenge to apply the current pavement performance models to network-level pavement management resides in the discrepancy between the data used for model development and the network-level pavement performance data. This dissertation tackles this issue by developing pavement performance models with variables that can be readily accessed from network pavement management systems (PMS). The developed models are demonstrated to generate accurate and reliable predictions and correctly capture the effects of different variables. This dissertation provides insight to improve pavement performance models by exploring the data imbalance of the network pavement performance data. The study identified two types of data imbalance: one resulting from the intrinsic pavement characteristics and the other stemming from pavement life expectancies. The existence of data imbalance negatively impacts the deterioration model's performance for pavement classes with limited data. The effect of model accuracy on the treatment benefit has also been investigated. Based on a reliability-based approach proposed in this study, it is discovered that with the increase of the model error, the treatment benefit decreases, while the extra cost needed to achieve the same benefit as the reference scenario increases. Considering the need for a cost-effective friction management approach and the potential of deep reinforcement learning to solve sequential decision problems, this dissertation develops a friction management framework based on the double deep Q-network (DDQN) reinforcement learning algorithm. Through a case study, it is demonstrated that with the DDQN model, better network friction performance can be achieved than the current practice of TxDOT, proving the effectiveness of the DDQN-based network pavement friction management framework.Civil, Architectural, and Environmental Engineerin
Measures of arterial stiffness : standardization, various devices, and measurement conditions
Full text linkArterial stiffness, characterized by elevated pulse wave velocity (PWV), is a critical prognostic measure during the progression of arterial diseases that contribute to cardiovascular disease. The inability of the arterial wall to effectively buffer the pulsatile pressure results in disturbed blood flow and added stress on key end-organs including the heart, brain, and kidneys. The overall theme of this dissertation is centered around the utilization of PWV as a measure of arterial stiffness. A series of studies included in this dissertation aimed to better comprehend and improve the measurement of arterial stiffness via PWV. This dissertation is clinically important because it focuses on a critical issue in public health, measures for cardiovascular disease, which is the leading cause of death in most industrial countries. In the first study, we aimed to facilitate the comparison between different measures of arterial stiffness across different studies. We summarized and derived conversion equations for commonly used image-based measures of arterial stiffness to the standard measures of arterial stiffness carotid-femoral PWV. We gathered the conversion equations from the literature, created regression equations, and applied them to 49 healthy participants. Both equations produced local PWV values that were moderately and significantly associated with cfPWV. In the second study, PWV was assessed in 70 participants in varying body positions. Results showed that 10º upper body tilt-up was sufficient to increase PWV significantly from the reference fully supine position. For the last study, there was no significant difference between the recorded PWV when the participants wore different legwear. However, stiff fabric materials increased the measurement error to obtain cfPWV with tonometry. In conjunction, these findings highlight the importance of standardizing the measurement and the utilization of pulse wave velocity in both research and clinical settings to improve the assessment of arterial stiffness and, ultimately, cardiovascular health.Kinesiology and Health Educatio
Advanced data interpretation techniques for downhole seismic testing
Full text linkDownhole seismic testing is commonly used to determine constrained compression wave (P) and shear wave velocity (S) profiles in soil dynamics and geotechnical earthquake engineering investigations. The conventional downhole data analysis involves picking reference points, typically peaks or troughs on time-domain waveforms that are presented in waterfall plots. Wave velocities are calculated from the slopes of lines drawn through groups of waveforms with reference points that cover various depth ranges or from the time interval between reference points at different depths. This conventional analysis method has two shortcomings. First, selecting the reference points by visual “picks” can be subjective for reasons such as: (1) signal clarity, (2) mechanical and electrical interferences, and (3) significant attenuation with depth. Second, wavelength is not considered in calculating body-wave (P and S) velocities. As a result, it is implicitly assumed that velocities calculated from these arrival times have wavelengths that are equal to or smaller than the measurement interval. This implicit assumption ignores the phase velocity characteristics in body waves. To overcome these limitations and to correctly evaluate P- and S- wave velocities, two advanced data interpretation techniques are introduced which are the Integrated Continuous Wavelet Transform (ICWT) method and the Improved-Spectral-Analysis-of-Body-Waves (ISABW) method. The ICWT method uses the amplitude and phase outputs from the Continuous Wavelet Transform (CWT) to identify the reference points which can be automated. The reference points identified using the ICWT method coincide with the visual-selection process when the signal is strong. However, the ICWT method can identify reference points when visual-selection process is not feasible. By using the ICWT method, subjectivity between different individuals analyzing the records and/or the analyzer and reviewers is removed and the time and effort required to determine the wave velocity profile is significantly reduced. The ISABW method was developed from the SABW method to consider the wavelength in determining the wave velocity profile. The development involves correcting the slanted travel path to the vertical travel path and utilizing phase velocities (dispersion curve) generated from multiple receiver pairs. To determine the representative wave velocity profiles, two new plotting methods are proposed. These methods are averaged dispersion velocity (ADV) method which is the simplest way to calculate the representative phase velocity from the each dispersion curve and moving-averaged, center-spreading, dispersion velocity (MACDV) method which maximizes the overlapping characteristic of the dispersion curves and represents a way to interpolate phase velocities between measurements depths. The advantage in ISABW method that determines the higher-resolution profile is verified by comparing the profile determined using the ISABW method and independent crosshole seismic testing at the same location.Civil, Architectural, and Environmental Engineerin
Parent-infant engagement and parental verbal responsiveness during object-mediated play : implications for early language development
Full text linkThe primary aim of this dissertation was to explore the interaction between parent-infant engagement characteristics and parental responsiveness during object-mediated play, and the potential implications of these relations for early infant vocabulary development. Specifically, we focused on whether and how infant coordination and parental joint action during play may moderate the relationship between parental verbal responsiveness and infant vocabulary growth. To examine this relationship, we conducted a short-term longitudinal study with parent-infant dyads when infants were 13 months and 18 months. At both sessions, dyads completed a free-play session with a standardized set of objects and parents reported infant expressive vocabulary using the MacArthur Bates Communicative Development Inventories (CDI; Fenson et al., 2007). A series of regression analyses revealed that infant coordination and parental joint action at 13 months each uniquely moderated the relationship between parental responsiveness at 13 months and vocabulary growth from 13 to 18 months. Specifically, higher responsiveness was associated with higher vocabulary growth but only for infants with higher levels of coordination whereas higher responsiveness was associated with higher vocabulary growth but only for infants with low parental joint action. Our analysis of concurrent behavior at 18 months and vocabulary growth showed that infant coordination, parental joint action, and parental responsiveness each uniquely predicted vocabulary growth. Specifically, higher infant coordination and parental responsiveness was associated with higher vocabulary growth whereas higher parental joint action was negatively associated. The results from this study highlight the unique and combined roles of infant coordination and parental responsiveness in shaping language development while also raising questions about the influence of parental joint action.Psycholog