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The synthesis, characterization and properties of metal tetrel pnictides
No full textMetal tetrel pnictides (Group 14 = Si, Ge, Sn; Group 15 = P, As, Sb) are an emerging class of materials which have potential applications in thermoelectrics, electrocatalysis, nonlinear optics, and energy storage. The development of these materials is of interest due to their improved performance, sustainable nature, and cheap and abundant constituent elements. Herein, we investigated five semiconducting ternary materials in the [Fe, Co, Ni, Sn]-Si-P phase spaces and two metallic ternary materials in the Ni-Ge-P and Pt-Si-Sb phase spaces for non-linear optical and electrocatalytic applications and focusing on usual chemical bonding patterns. Though materials have garnered much attention, we are objectively focused on the development of this family of compounds by building and understanding the structure-property relationships and the opportunity to realize new structures with tuned properties
From tourist to volunteer: Understanding transformational processes through the development of the tourist-to-volunteer transformative learning scale (TVTLS)
No full textVolunteer tourism is a rapidly expanding form of meaningful travel that promotes personal growth, fosters cross-cultural understanding, and encourages long-term civic engagement. While Transformative Learning Theory (TLT) provides a robust framework for explaining these shifts, existing tools do not accurately measure the full sequence of Mezirow’s ten transformative steps or the process through which tourists become volunteers. This study addresses this gap by developing and validating the Tourist-to-Volunteer Transformative Learning Scale (TVTLS), a comprehensive instrument designed to assess the transformation from initial travel experiences to the adoption of a volunteer identity.
Using a sequential mixed-methods design, Phase 1 generated an item pool through thematic analysis of 14 interviews with individuals who first visited a destination as tourists and later volunteered there. Phase 2 refined items through expert review, pilot testing, and Exploratory Factor Analysis (EFA), yielding an eight-factor structure aligned with key TLT domains. Phase 3 employed Confirmatory Factor Analysis (CFA) with a sample of 420 participants to validate the measurement model, demonstrating strong reliability and validity. Phase 4 tested structural relationships using Structural Equation Modeling (SEM), confirming that transformative learning follows a largely linear and cumulative pattern, where reflection, dialogue, role exploration, and skill-building predict transformation and reintegration.
Theoretically, The TVTLS advances TLT within experiential tourism contexts. Practically, it offers organizations and program designers a diagnostic tool to assess readiness, tailor volunteer experiences, and support long-term engagement. This study provides a foundational measurement framework for future research on transformation and prosocial behavior in tourism
Modular multistatic (220 -269.5 GHz) millimeter wave 3D imaging system
No full textAbstract— This thesis presents the design of a compact, modular millimeter-wave frequency modulated continuous wave (FMCW) radar that operates in the 220-269.5 GHz frequency range. The prototype FMCW radar is composed of a commercially available integrated transceiver, PLL, and USB-serial interface for digital control. The modular design is then expanded into a linear array consisting of four elements to form a multiple input multiple output (MIMO) array used for synthetic aperture radar (SAR) imaging. The array is then calibrated by scanning a metallic spherical scatterer. The imaging capabilities of the array are tested by raster scanning several samples and producing high resolution SAR images. Example images produced with the system include USAF resolution targets, and polycarbonate with embedded foreign object debris (FOD). These imaging examples demonstrate how the modular imaging system can be used in non-destructive evaluation (NDE) applications
Advanced analyses for mass spectrometry: From atomic to molecular
No full textResearch toward establishing advanced analysis workflows for mass spectrometry data is presented in this dissertation. Applications of machine learning workflows are explored for the classification of particles and the spatial distributions of stable isotope labeled plant metabolites. In addition to these applications, the use of Monte Carlo simulations to better understand sources of uncertainty in single-particle measurements were also studied. In Chapter 1, a generalized overview of mass spectrometry is discussed, followed by more detailed discussions of single-particle inductively coupled plasma time-of-flight mass spectrometry (spICP-TOFMS) and matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). At the end of the chapter, an introduction to machine learning for mass spectrometry is provided. The following chapters (Chapters 2-5) feature published research or research that is currently under peer-review. The last chapter (6) provides a general summary of the conclusions for all research presented here.
In Chapters 2 and 3, classification of cerium- and titanium-containing particles measured by spICP-TOFMS are presented. Data from spICP-TOFMS measurements of bastnaesite, ferrocerium mischmetal, and engineered cerium (IV) oxide particles were subjected to a semi-supervised machine learning workflow to classify single-particle (sp) signals as natural, incidental, and engineered, respectively. The resulting machine learning model produced false-positive rates (FPRs) greater than 10% for engineered and incidental particle classes; this high FPR will result in a substantial number of particles incorrectly classified. To overcome large misclassification rate, a second semi-supervised machine learning model was trained from data categorized as natural, incidental, and engineered but those particles that were incorrectly classified by the first model were recategorized as unclassifiable engineered or unclassifiable incidental. Employing this second semi-supervised machine learning yielded FPRs for natural, incidental, and engineered less than 5%; therefore, significantly reducing the likelihood of false-reporting contamination levels of anthropogenic particles. To further validate this two-stage semi-supervised machine learning workflow, titanium-containing particles were classified with the same workflow and the results showed less than 5% FPRs for the desired particle classes.
Particle classification with two-stage semi-supervised machine learning was limited, as all supervised machine learning models are, by the quality of the training data. These models required measurements of known particle sources. To overcome this inherent limitation, particle distributions were simulated using Monte Carlo methods and bias in the measured signals was explored, as shown in Chapter 4. Detection limitations and Poisson-distributed noise contributions were considered in the creation of the simulations. These Monte Carlo simulations allowed for systematic comparisons of simulated elemental mass distributions to measured data. Simulated signals and distributions were compared to measured signals from cerium-containing particles. Accurate reflection of spICP-TOFMS data for single- and multi-elemental particles was demonstrated and major deviations from theoretical analyte distributions were identified.
Moving away from spICP-TOFMS, the usefulness of unsupervised machine learning to segment MALDI-MSI data of Lemna minor (duckweed) is demonstrated in Chapter 5. Duckweed is a simple, commonly-used model plant system that typically has a matured parent frond with a budding pouch from which two daughter fronds sprout. Untargeted analysis for MALDI-MSI data is inherently challenging due to its high dimensionality; this analysis is further complicated by the addition of stable isotope labels (isotopologues), which are observed for macromolecules as distinct binomial distributions. By employing spatial shrunken centroid segmentation (SSC; an unsupervised machine learning algorithm) to cluster pixels according to their mass spectral similarity, data from unlabeled, two- and three-day 13CO2-labeled, and five-day D2O-labeled duckweed samples could be analyzed rapidly. In the unlabeled duckweed, SSC revealed four tissue segments: the outer parent frond, the intermediate/daughter frond, the budding pouch, and the tissue edge; major differences between these segments were attributed to intensity differences in the matrix as well as metabolite localizations. Feature t-statistic calculations aided in determining which metabolite signatures were abundant or absent in the various regions of the duckweed plant; positive t-statistics were observed for metabolites such as sucrose in the outer parent and intermediate/daughter tissues. Analysis of three-day 13CO2-labeled duckweed showed that the more developed duckweed plant possessed five distinct regions: the outer and inner parent fronds, the budding pouch, as well as the inner and outer daughter fronds. These regions exhibited different metabolite turn-over rates, as demonstrated by calculations of the fraction of de novo synthesis. In addition to these findings, evidence of D2O-induced stress was observed in the ~200% abundance increase of asparagine compared to the unlabeled duckweed sample, due to reduced protein synthesis rates. Without the use of an unsupervised machine learning algorithm, rapid and systematic comparisons of various tissue regions to identify differences in metabolic activity would not have been achievable.
The final chapter considers general conclusion on the work presented in Chapters 2-5, in addition to future considerations to overcome existing limitations in this work
Therapeutic strategies to target organophosphate-induced brain injury
No full textEpilepsy is one of the most common neurological disorders, characterized by spontaneous recurrent seizures. Among the various types of epilepsies, acquired epilepsy accounts for more than half of all cases. Acquired epilepsy can arise from different causes, with one most common causes being prolonged seizures, known as status epilepticus (SE), which may result from exposure to seizurogenic chemicals, such as organophosphates (OP) or neurotoxins. Several OP compounds are synthesized for use as pesticides and chemical warfare agents, including nerve agents. Acute exposure to OP results in SE due to the irreversible inhibition of acetylcholinesterase (AChE), leading to increased acetylcholine and a cholinergic crisis. While current medical countermeasures can treat the acute symptoms of SE and other cholinergic symptoms, the persistent cellular and molecular changes that occur during the epileptogenic phase are still largely unknown. During the post-OP exposure phase of epileptogenesis, we discovered an upregulation of inducible nitric oxide synthase (iNOS) and Src family kinases (SFK) in the brain, which contribute to nitrooxidative stress, behavioral deficits, neuroinflammation, and neurodegeneration. This dissertation aims to evaluate the efficacy of the two novel investigational drugs that inhibit iNOS (1400W) and SFK (saracatinib, SAR, AZD0530) in a pesticide/nerve agent surrogate model (DFP) and a real-world nerve agent (soman/GD) rat model.
We optimized the dosing regimens of 1400W and SAR in both the DFP and soman rat models in short-term and long-term studies. Treatment with 1400W at a dosage of 15 mg/kg for two weeks significantly reduced DFP-induced brain pathology in the short-term study. Based on the findings from the short-term DFP study and pharmacokinetics data, we optimized the dose of 1400W for the soman model to 20 mg/kg due to higher SE severity than DFP and administered daily over a two-week period. The treatment with 1400W effectively reduced neuronal hyperexcitability, gliosis, and neurodegeneration at 3.5 months post-soman, indicating its potential disease-modifying effects.
In animal models of epilepsy, the upregulation of Fyn/Src tyrosine family kinases, which mediate the Fyn/tau/NR2B/PSD95/nNOS and Fyn-PKCδ signaling pathways in the brain, has been reported. Therefore, we hypothesized that a broad-spectrum SFK inhibitor (saracatinib, SAR, AZD0530) would effectively target these pathways and modify the development of epilepsy. Short-term treatment with SAR had minimal effects in previous rat DFP studies. Therefore, we conducted a comprehensive pharmacokinetic study in naïve animals, comparing SAR incorporated in the diet (SAR-in-diet) with repeated daily oral dosing of SAR for a week. Both approaches achieved optimal SAR concentrations in the brain and serum. Notably, the SAR-in-diet approach offers advantages for chronic studies and reduces handling stress for the animals, which is an important ethical consideration and controls variables between experimental groups. Using the SAR-in-diet approach, we optimized the dose of SAR (10-20 mg/kg/day, high-dose or 5-10 mg/kg/day, low-dose) in a DFP-exposed mixed-sex cohort of rats fed for four weeks. The SAR-in-diet significantly reduced nitro-oxidative stress (serum) and reduced the levels of pro-inflammatory cytokines and chemokine in both the cerebrospinal fluid (CSF) and serum. We observed sex-specific effects in certain neuroinflammatory markers, with differences across dosages and brain regions. Building on the outcomes of the four-week treatment regimen, we extended the study in the long-term soman model. SAR was administered through the diet over a 17-week period. Long-term treatment with SAR-in-diet effectively prevented behavioral deficits, brain pathology, and reduced the severity of SRS.
Collectively, the findings from these studies suggest that optimizing the dosing regimen for SAR and 1400W could progress toward drug development and future clinical trials for the treatment of OP-induced SE or epilepsy. Future studies will focus on evaluating the therapeutic potential of SAR and 1400W in combination, as this approach may provide superior benefits compared to monotherapy while reducing the dose of both test drugs in combination. Additionally, considering SAR is a Pgp inhibitor (Pgp regulates blood-brain barrier function), combining SAR with antiseizure drugs (ASDs) could enhance the efficacy of ASDs while reducing the required dose for long-term treatment and could minimize ASD-associated side effects in the treatment of epilepsy
Quality Matters in Practice: An Online Biology Course Case
No full textThe goal of this paper is simple: to offer practical tips that can help other instructors, especially those in STEM and at ISU, feel confident and supported as they consider Quality Matters (QM) certification for their courses. In this paper, we walk through lessons learned from certifying BIOL 3140, highlight strategies that worked well for us, and share resources available at Iowa State University (ISU) to support instructors throughout the process
Fast and accurate prediction of the future LEO space environment via advanced statistical approach
No full textSpace domain awareness (SDA) is a critical aspect of mission design for spacecraft operating in low Earth orbit (LEO). Many objects from a variety of sources, such as active satellites, derelict spacecraft, and debris, pose significant hazard to future spacecraft launches when not adequately accounted for.
The JASON scientific advisory group recently released a report which found that the existing models for estimating the future LEO environment are not adequate to meet projected space sustainability needs.
Recently, the MIT Orbital Capacity Analysis Tool (MOCAT) was developed. MOCAT is an open-source estimation tool that models the LEO environment over time, and can perform either high-fidelity Monte-Carlo (MOCAT-MC) estimation or low-fidelity Source-Sink Evolutionary Model (MOCAT-SSEM) estimation. The parameters used in MOCAT-SSEM are highly sensitive to initial conditions, causing the estimate to rapidly diverge from the more accurate MOCAT-MC model.
This work describes in detail the process of merging the accuracy of MOCAT-MC with the speed of MOCAT-SSEM via several advanced statistical information fusion approaches. By applying Kalman filtering, estimation smoothing, and multiple-model adaptive estimation, new parameters for MOCAT-SSEM are identified that result in better agreement between MOCAT-SSEM and MOCAT-MC models
Computational and structural investigations of class I diterpene synthases
No full textThe labdane-related diterpenoids (LRDs) represent a massive reservoir of natural chemical and structural diversity, with over 100,000 such compounds having been identified to date. These compounds derive much of their structural diversity from the activity of the terpene synthases which convert the linear isoprenoid precursors into complex polycyclic carbon skeletons. These enzymes develop these complex structures in a few steps through carbocation cascade reactions that pass through multiple highly reactive carbocation intermediates. This reactivity results in a reaction that is highly sensitive to its active site and thus also to minute changes therein. Many single residue switches have previously been identified in terpene synthases, redirecting significant activity toward new products. Understanding these switches and their resulting changes to reaction pathways allow for targeted redesign of terpene synthases. Recent work has highlighted the efficacy of a novel docking protocol -- TerDockin -- in predicting the product outcome of a serine switch in a bacterial kaurene synthase from Bradyrhizobium japonicum (BjKS). Building on this work, here we have leveraged the same protocol to predict switches which truncate the reaction pathway to specifically yield a specific olefin isomer of a deprotonated intermediate. We have expanded this approach further to identify switches which enable additional unnatural carbocation rearrangements to yield a rearranged carbon skeleton. Further, we have expanded the application of this system beyond bacterial terpene synthases to the abietadiene synthase from abies grandis (AgAS) where it allowed for the analysis of single residue switches identified from residue conservation within conifer diterpene synthases. This system depends on accurate structural information of the terpene synthase active sites; therefore, x-ray crystallographic investigations have been undertaken for BjKS. Supplementing these are also NMR investigations which have indicated dimerization of the wild-type enzyme
Pedology and cropping patterns in relation to plinthite and farmer-based soils knowledge
No full textPedology provides the foundation for understanding, mapping, and making soil management plans. In Uganda, where most livelihoods depend on agriculture, soil information is key in decision-making and advancing the sector. This dissertation utilizes a pedological approach to understanding soil-landscape relationships in Kamuli District. The study integrates three lines of inquiry: (1) the influence of pedology on cropping decisions; (2) the mineralogy and formation of plinthite, which is a major feature of the catena; and (3) assessing how farmers in Kamuli understood and classified their soils.
Parent materials in the Kamuli catena ranged from Precambrian granite-derived residuum and colluvium on the uplands to Holocene or even modern age alluvium in the lowland valleys. As a result, the catena comprised a mix of highly weathered Alfisols, Oxisols, and Ultisols in the uplands to more recent lowland Alfisols. The cropping patterns reflected these pedological realities, with uncultivated pedons linked to plinthite in the uplands and poor drainage in the lowlands. For farmers who had no option but to cultivate areas in the upland with plinthite, it was at a shallower depth compared to uncultivated areas due to erosion and profile truncation. Two chemical properties, pH and available phosphorus (in the lowland), were higher in cultivated pedons. The other properties were consistently lower, indicating depletion under continuous cropping. These patterns partially supported the hypothesis that better soils are more likely to be cropped, while also revealing how management influences changes in soil properties.
Plinthite, a key pedogenic feature in the Kamuli catena, varied systematically with landscape position. It occurred at shallower depths on summits than backslopes. Although uncommon in the lowland, when present at the footslope, it lacked the distinctive concretionary characteristics in upland settings. Mineralogical characterization of plinthite samples identified two distinct mineral assemblages. Group 1 samples showed iron oxides (hematite and goethite), kaolinite, manganite, and quartz. Group 2 samples had iron oxides, kaolinite, manganite, and muscovite. Scanning electron microscopy also provided additional insights, including the presence of titanium, in addition to the previously mentioned minerals. The formation of plinthite was attributed to past poor drainage, which led to the accumulation of iron oxides. Subsequent downwasting repositioned these plinthic soils on the current summits and backslopes.
Farmer groups in three villages in Kamuli classified their soils into four main groups: Lirugavu, Lubalebale, Mukyanga, and Mutaala. These groups were based on observable characteristics of color, texture, and crop performance. Farmers’ knowledge extended beyond surface traits to include subsurface features and spatial insights by affiliating certain soil groups with specific landscape positions. The farmers’ qualitative ranking of the soil groups’ productivity aligned closely with the Corn Suitability Rating, a quantitative metric that considers the soil characteristics. A comparison with USDA soil taxonomy suborders at grouping soil based on different soil properties, showed some insights. The farmer-based classifications showed they could differentiate the soils by depth of mollic colors, clay content, soil organic matter (SOM), total nitrogen (N), and cation exchange capacity (CEC). However, they lacked the granularity of suborders at capturing the change in soil properties with depth
Effects of a stage combat learning intervention on movement variability and social-emotional imagination in novice actor-combatants: A proof-of-concept pilot study
No full textArts-based health interventions hold great promise for rehabilitation and general health promotion. Theatre-based interventions in particular may provide social, emotional, and cognitive benefits along with providing a means to engage in physical activity, with its myriad benefits. Stage combat is an understudied subdiscipline of theatre acting which makes concrete through action the objectives and tactics actors usually employ through language. In this dissertation, stage combat and its underlying processes will be discussed and emerging research will be presented.
Stage combat is an aesthetic martial art built on complex motor skills. Much of the literature on motor learning has focused on simple motor skills and movements (Wulf & Shea, 2002) like tracing a path with the hand while resisting perturbations (Heald, Lengyel, & Wolpert, 2021) or on functional skills like prehension (Yan et al., 2022), locomotion (Shpakov et al., 2021), and object interception (Barany et al., 2020). Little work has been done on complex motor skills (Sternad et al., 2014), and almost no scientific investigation has been done on stage combat (there are two known exceptions: Lee et al., 2022; Brockshus, 2021).
Target interception is a functional motor skill critical to activities such as catching and striking. It is closely related to object avoidance, and both functional motor skills are supported by similar neural and cognitive processes (Merchant et al., 2009). Stage combat relies on many functional skills, including locomotion, prehension, and bimanual coordination. However, it could be argued that target interception and object avoidance are the most critical functional skills to successfully performing stage combat. Consistent targeting with a bodily effector or tool (e.g. prop sword) is required for almost every action in choreographed stage combat sequences.
While stage combat is an aesthetic martial art, it is also built on the art of acting. Acting requires interaction with fellow actors and an audience to build imaginary scenarios. Social-emotional imagination (Gottlieb et al., 2016) is a multi-dimensional psychological construct which supports social interactions. It involves perspective-taking, empathizing, regulating the emotions of oneself and others, and imagining various outcomes to given situations. Thus, social-emotional imagination is critical to the art of acting and may be critical to the discipline of stage combat.
Results of a new study assessing effects of stage combat training on target interception and social-emotional imagination will be presented. Feasibility of such study will be discussed. Stage combat training holds promise as an engaging, active, and potentially health promoting practice