114333 research outputs found
Sort by
Jamming as meditation: Unpacking the neurophysiological relationship between music improvisation and mindfulness meditation
No full textExperienced musicians report altered states of consciousness when an improvisation is progressing smoothly, which are often described using the terms “flow state”, “mindful”, and “meditative” (Forbes, 2021; Sol, 2021). Flow states consist of total concentration, a sense of control, and distortions in time perception (Csikszentmihalyi, 2002; Csikszentmihalyi, 2013). Mindfulness meditation practices, notwithstanding their variation, encourage focusing attention and concentration on sensations, letting go of control, and grounding into the present moment (Shapiro, Carlson, Astin, & Freedman, 2006; Bishop et al., 2004; Chiesa & Malinowski, 2011). The relationship between flow states experienced during music improvisations and mindfulness meditation, however, remains speculative as no study to date has conducted an in-depth comparison of the two experiences beyond qualitative interviews with experienced musicians (Sheldon, Prentice, & Halusic, 2015).
Separate research on flow states, music improvisation, and meditation suggests an overlap in their associated neurophysiology, particularly with heart rate variability (HRV) and electroencephalography (EEG) powerband activity (Brown et al., 2020). Low frequency (LF) HRV (0.04 - 0.15 Hz) is proposed to be a measurement of baroreflex, parasympathetic nervous system (PNS), and sympathetic nervous system (SNS) functioning, although these associations are largely debated (Martelli et al., 2014; Rahman et al., 2011). High frequency (HF) HRV (0.15 - 0.4 Hz) is a measurement of PNS modulation, sharing associations with respiratory activity (Berntson et al., 2007; Song & Lehrer, 2003; van Ravenswaaij-Arts et al., 1993). Evidence suggests that flow states during music improvisation are associated with increased HF-HRV pre-performance and decreased LF-HRV post-performance (Kalmeier, 2016). As such, decreased LF-HRV and increased HF-HRV are also observed following mindfulness meditation practice (Nijjar et al., 2014; Krygier et al., 2013; Attar et al., 2021). Convergence between music improvisation and mindfulness meditation neurophysiology extends to shared activity in EEG theta and alpha powerbands (Kalmeier, 2016; Cahn, Delorme, & Polich, 2010; Rodriguez-Larios, Wong, Lim, & Alaerts, 2020). Despite the evidence of neurophysiological overlap, no study to date has directly examined flow state associated HRV or EEG activity during music improvisation and mindfulness meditation. Hence, the overall goal of the present dissertation is to determine if flow states during music improvisation are comparable to flow states during mindfulness meditation. Fourteen experienced musicians completed both sessions of a randomized crossover cohort trial, using electrocardiogram (ECG) and EEG to assess whether there were neurophysiological differences between the two sessions.
The first aim of the present study was to compare flow states reported after music improvisation to flow states reported after mindfulness meditation. It was hypothesized that timestamped average subjective flow states reported after unstructured group music improvisation and after mindfulness meditation would change relative to baseline (time 1:00). The change in flow reported after mindfulness meditation was also hypothesized to change similarly to flow reported after music improvisation. The first hypothesis was not confirmed. However, overall flow state scores reported after mindfulness meditation and music improvisation were not significantly different.
The second aim of the study was to compare HRV during music improvisation and mindfulness meditation. Primary outcome measures included LF-HRV and HF-HRV. It was hypothesized that HF-HRV during music improvisation and mindfulness meditation would be significantly higher relative to controls. It was also hypothesized that there would be no difference in HF-HRV during mindfulness meditation and music improvisation. The first hypothesis was not confirmed, and the second hypothesis did find confirmation. Mindfulness meditation and music improvisation both resulted in greater LF-HRV and lesser HF-HRV relative to control conditions.
The third aim of the study was to compare flow state associated EEG powerband activity during music improvisation and during mindfulness meditation. Primary outcome measures included delta, theta, alpha, and beta power over prefrontal regions (AF7 and AF8). It was hypothesized that alpha (8 – 12 Hz) and theta (4 – 7 Hz) power associated with high flow states during music improvisation and mindfulness meditation would be significantly higher relative to low flow states. It was also hypothesized that there would be a similar electrophysiological change between high and low flow states during mindfulness meditation and music improvisation. The first hypothesis was not confirmed, and the second hypothesis did find confirmation. Music improvisation and mindfulness meditation high flow states both resulted in greater beta power (13 – 30 Hz) over frontal regions relative to low flow states in both conditions.
The results of all three aims help fill an essential gap in the literature on music improvisation and mindfulness meditation by providing evidence to suggest there is a disinhibitory overlap between the two experiences. Furthermore, the disinhibition during music improvisation and mindfulness meditation was perceived by the musicians as a positive experience. The findings, therefore, enhance our understanding of music improvisation- and mindfulness meditation-associated neurophysiology, providing evidence that both may be used as alternative, nonpharmacological interventions for motor and cognitive disinhibition, potentially through the shared experience of “letting go”. Studies mentioned in the following dissertation provide critical clinical evidence towards the overarching goal of targeting accessible, affordable, and inclusive practices that enhance wellbeing
Device fingerprint leakage resilient on/off-body authentication and adversarial analysis of deep RF classifiers in Wireless Body Area Networks
No full textWireless Body Area Networks (WBANs) are a critical component of emerging Internet of
Healthcare Things (IoHT) applications, enabling real-time monitoring through wearable and
implantable sensors. Ensuring the authenticity and security of data from these on-body devices is
paramount, especially at the physical layer where decisions must be made before higher-layer
cryptographic keys are established. This thesis develops a robust, propagation-based
authentication framework for WBANs and investigates the resilience of machine learning
classifiers for WBAN signals under adversarial conditions.
First, we introduce a leakage-resilient radio physical-layer scenario authentication mechanism
that reliably distinguishes on-body (legitimate) transmitters from off-body (external or
adversarial) transmitters using only the known preamble portion of wireless frames. The
framework neutralizes device-specific radio frequency (RF) fingerprints to prevent identity
leakage, focusing the decision on propagation characteristics unique to the on-body vs. off-body
channels. Evaluated on a multi-device Bluetooth Low Energy (BLE) WBAN dataset with
controlled on/off-body scenarios, the proposed scheme achieves near-perfect detection (approx.
100% accuracy and AUC ≈ 1.00) even on devices and sessions never seen in training. It meets
strict false-alarm requirements with calibrated thresholds, demonstrating that body-induced
creeping-wave propagation effects, rather than hardware signatures, can serve as a dependable
security indicator at the physical layer.
Second, we assess the vulnerability of deep learning-based RF classifiers in WBAN settings to
adversarial attacks. We develop a convolutional neural network (CNN) model to classify devices
and motion contexts from BLE signals, and then subject it to a suite of adversarial perturbations,
including gradient-based attacks (FGSM, PGD, and CW) and additive noise. Under benign
conditions, the CNN achieves high accuracy (80–90%) on device identification and on-body
motion recognition tasks. However, we show that small, carefully crafted perturbations can
drastically degrade performance: iterative attacks often reduce accuracy to near chance levels.
Untargeted attacks that broadly perturb inputs are especially devastating, causing
misclassification rates upwards of 95%. These findings expose a serious reliability threat for
learning-based WBAN security schemes in adversarial environments.
The thesis concludes with a synthesis of insights from both studies. By combining a
physics-grounded authentication layer with awareness of machine learning vulnerabilities, we
outline a multifaceted approach to securing WBANs. Key recommendations include integrating
adversarial training, exploring complex-valued neural networks for RF data, and leveraging
ensemble methods and randomization to harden WBAN classifiers. Overall, this work contributes
a novel lightweight authentication solution for resource-constrained wearable devices and
highlights the need for robust machine learning in future body-centric wireless networks
Real options valuation of power system investments in generation, transmission, and energy storage under uncertainty
No full textRising electricity costs, growing demand volatility, and the need for renewable deployment require planning tools that value flexibility under uncertainty, particularly for capital-intensive and largely irreversible grid investments. This dissertation develops three Real Options Analysis (ROA) frameworks that quantify the economic value and optimal timing of power‐system investments using market signals from Locational Marginal Prices (LMP) computed via Direct Current Optimal Power Flow (DCOPF). First, I value a generation expansion option when electricity demand follows a geometric Brownian motion. Demand is discretized on a binomial lattice, DCOPF yields nodal LMPs with and without the new unit, and the LMP cost reduction defines project benefits. Results show that investment timing is state-dependent and that option value is sensitive to demand volatility, horizon, discount rate, and construction cost. Second, I introduce an integrated framework for sequential investment in generation expansion followed by transmission expansion, modeled as a sequential compound option. This captures the interdependence between decisions and reveals when a transmission line provides incremental value following additional generation capacity. When volatility is low, the sequential strategy dominates, while rising volatility shifts value toward generation alone and reduces the marginal benefit of transmission. Third, I propose a Least-Squares Monte Carlo (LSMC) model to evaluate the addition of energy storage to a wind farm with uncertainty in hourly demand, storage investment cost, and wind variability. Storage mitigates congestion and curtailment, lowering LMP. Although economically feasible, optimal policy mostly favors deferral, and greater cost volatility increases the value of waiting. Collectively, these studies provide market-grounded, flexible valuation tools for generation, transmission, and storage planning, and deliver clear guidance on when to invest or defer investments
Cognitive function and its change over time: effects on depression trajectories in oldest-old adults
No full textObjectives
Using data from the Health and Retirement Study (1992–2020), this study investigates how cognitive function and its longitudinal changes influence trajectories of depressive symptoms among oldest-old adults.
Method
Latent growth curve modeling (LGCM) was applied to examine changes in cognitive function and depressive symptoms across five time points (ages 90, 92, 94, 96, and 98+). The analytic sample consisted of 289 participants at baseline (age 90), with sample sizes ranging from 289 to 231 across subsequent age groups. In comparison, we conducted an additional LGCM longitudinal analysis for participants at an earlier baseline at age 60 (N = 17036), following them from 60 to 70 (n = 11915) and 80 years (n = 8942).
Results
The unconditional LGCM (without covariates) showed that higher baseline cognitive function was significantly associated with lower baseline depressive symptoms (β = −0.304, p < 0.001), but did not predict changes in depression over time. In the conditional model (including gender, education, self-rated health, and activities of daily living as covariates), higher education was associated with higher cognitive functioning but also with higher depression at baseline. In contrast, better ADL functioning and better perceived health significantly predicted higher cognitive functioning and lower depression at baseline. The intercept of depression predicted its own slope (β = −0.934, p < 0.001), indicating that Individuals with higher initial depressive symptoms experience a slower increase (or less worsening) in depression over time. For the 60–80 group, the intercept of cognition significantly predicted the intercept of depression (β = −0.380, p < 0.001), consistent with the 90–98+ group. However, longitudinal associations differed across age groups, indicating distinct developmental patterns between mid-to-late and very late adulthood.
Conclusion
This study emphasizes that although cognitive and emotional health are interrelated at baseline, changes in one domain do not reliably predict changes in the other among the oldest-old adults population. Instead, education, physical functioning, and self-rated health emerged as key predictors of both cognitive and emotional well-being. These findings highlight the importance of multidimensional assessments and targeted support to promote resilience and quality of life in extreme old age.This accepted article is published Abaei, E., & Martin, P. (2026). Cognitive function and its change over time: effects on depression trajectories in oldest-old adults. Aging & Mental Health, 1–15. https://doi.org/10.1080/13607863.2026.2612728.Funding
This research was also supported by the United States Department of Agriculture, Hatch Project Grant, IOW05699
Reining in Multidrug Resistance Protein 1 via Binding Its Flexible Interdomain Linker with Sequence-Selective Peptide-Binding Nanoparticles
No full textOverexpressed ATP-binding cassette (ABC) transporters, such as multidrug resistance protein 1 (MRP1) encoded by ABCC1, are responsible for multidrug resistance in anticancer treatment due to their abilities to prevent drugs from reaching their lethal intracellular concentrations. Similar overexpression of drug efflux pumps is a major contributor to antimicrobial resistance seen in bacteria. We report sequence-selective, molecularly imprinted nanoparticles (MINPs) targeting MRP1 in human cancer cells. These nanoparticles mask different segments of the long, flexible linker connecting NBD1 (nucleotide-binding domain 1) and TMD2 (transmembrane domain 2) of MRP1. Binding of the protein near the inner membrane interface is found to strongly inhibit the function of the efflux pump and sensitize Dox-resistant cancer cells to the drug, reducing its IC50 value by ∼25%. These results illustrate a new strategy for inhibiting intracellular proteins and identifying potential functional linear motifs in unstructured regions of proteins, benefiting from the facile preparation of the MINPs for different peptide sequences, their highly specific binding abilities, and their ability to enter cells.This article is published as Ghosh, Avijit, Mansi Sharma, and Yan Zhao. "Reining in Multidrug Resistance Protein 1 via Binding Its Flexible Interdomain Linker with Sequence-Selective Peptide-Binding Nanoparticles." Biomacromolecules (2026). doi: https://doi.org/10.1021/acs.biomac.5c02567.We thank NSF (DMR-2308625) for supporting the research
Quantification and management of spatio-temporal variability in agricultural systems
No full textIgnoring the spatial and temporal variability of cropping systems decreases the economic and
environmental sustainability of agricultural systems. On-farm precision experimentation (OFPE)
offers a promising approach to quantify spatial and temporal variability in crop yield response to
inputs, such as nitrogen and seeding rates. However, challenges remain in processing large,
complex data sets from yield monitors and variable rate applicators, as well as accurately
capturing spatio-temporal yield variability. Here, we had three objectives: (i) develop and
evaluate computational methods for processing OFPE data with improved accuracy and
precision; (ii) create a framework to estimate planting and harvest dates using remotely sensed
vegetation indices; and (iii) assess the capability of crop simulation models to capture
spatio-temporal maize yield variability and quantify the risk associated with fertilizer
management. We developed an R package called Precision Agriculture Computational Utilities
(pacu), which included methods for yield monitor data processing. The methods implemented in
pacu were compared against simpler empirical filtering methods through simulations and case
studies. Nonlinear mixed-effects models and satellite-derived vegetation indices were used to
predict crop planting and harvest dates at the field scale. We calibrated a crop model spatially
using several years of OFPE data. The crop model was used to quantify the spatio-temporal
variability of crop response to fertilizer management. The methods implemented in pacu provided
more accurate and precise estimates of agronomic optimum nitrogen and seeding rates than
simpler methods. The nonlinear modeling framework improved prediction of planting and harvest
dates relative to naive averages, demonstrating applicability across diverse geographic regions.
Spatial calibration of the crop model successfully captured within-field spatio-temporal yield
variability, showing great variation in optimal nitrogen fertilizer requirements and associated risks
over 25 years. Our work contributes to agronomic science and precision agriculture by exploring
robust methods for data processing, planting and harvest date estimation, and crop modeling that
enhance understanding and management of spatial and temporal variability in cropping systems.
These contributions support more economically and environmentally efficient farming practices
through improved site-specific input recommendations
Environmental factors influencing detection of ring-necked pheasant broods during August roadside surveys
No full textThe August roadside survey is a population index used to monitor trends in productivity and population status of ring-necked pheasant (Phasianus colchicus) in several states of the United States. Inter-annual population changes from roadside surveys have occasionally implied biologically implausible outcomes, hinting at survey bias and constrained utility of the index under certain environmental conditions. Research has shown correlations between environmental conditions and ring-necked pheasant detections but range-wide rigorous assessment of factors influencing detection probabilities has never been considered. We sought to evaluate environmental factors influencing detection probability of ring-necked pheasant broods across a large geographic scale, where August roadside surveys are an important monitoring tool. State wildlife resource agencies conducted 1,000 August roadside surveys on 174 unique route-by-year combinations in 11 states during 2019–2021. We used a single-species N-mixture model in a Bayesian framework to examine factors influencing detection probability of broods. Wind speed and cloud cover negatively influenced detection probability of pheasant broods. Dewpoint depression, a proxy for morning dew conditions where higher values indicate less dew, had a significant negative effect on detection probability for pheasant broods. Soil moisture had a positive effect on the detection probability of pheasant broods. Observed variation in conditions across routes, among the years in our study, and across the geographic scale covered could constrain direct comparisons of uncorrected counts. Survey methodology can be adjusted to target mornings with few clouds, low winds, and favorable dew conditions to increase detection probability and consistency among surveys. However, soil moisture may be difficult to methodologically control for over larger scales of space and time. Posterior estimates from our model may be used to gauge intra- and inter-seasonal variation in conditions for detecting pheasant broods, which could improve inference from long-term population monitoring, especially across large spatial and temporal scales.This article is published as Janke, Adam K., Zachary R. Dienes, J. Scott Taylor, Philip M. Dixon, and Todd Bogenschutz. "Environmental factors influencing detection of ring‐necked pheasant broods during August roadside surveys." The Journal of Wildlife Management (2026): e70180. https://doi.org/10.1002/jwmg.7018
Clinical Presentation, Detection, and Immunopathogenesis of Mycoplasma hyosynoviae Field Isolates in Experimentally Inoculated Pigs
No full textMycoplasma hyosynoviae is a significant pathogen in swine populations, contributing to polyarthritis and lameness in growing pigs. This study characterizes the clinical presentation, pathogen detection, immune response, and lesion development following experimental inoculation with two distinct M. hyosynoviae strains. Pigs were inoculated with either a low- or high-virulence strain and monitored for 18 days. Lameness was observed throughout the study, with affected pigs exhibiting mild to moderate clinical signs. M. hyosynoviae was often detected in the tonsils, while detection in oral fluids was transient. Serum IgG levels increased significantly in the inoculated groups. IL-1β, IL-6, and TNF-α cytokines were elevated only at 7 DPI, whereas IL-8, IL-10, and IFN-γ levels were unchanged in both inoculated groups. Notably, only pigs inoculated with the high-virulence strain developed lesions, and M. hyosynoviae was detected only in the synovial fluid by PCR from this group. These findings highlight strain-dependent differences in the pathogenesis of M. hyosynoviae. The pathological differences between these strains suggest variations in adherence factors, immune evasion capabilities, or metabolic adaptability. Further research is warranted to elucidate the genetic determinants of virulence and the protective role of humoral and cellular immune responses in M. hyosynoviae infection.This article is published as Macedo, Nubia R., Bailey L. Arruda, Luis G. Giménez-Lirola, Ganwu Li, Locke Karriker, Jordi Mora, and María J. Clavijo. "Clinical Presentation, Detection, and Immunopathogenesis of Mycoplasma hyosynoviae Field Isolates in Experimentally Inoculated Pigs." Pathogens 15, no. 1 (2026): 66. doi: https://doi.org/10.3390/pathogens15010066.This study was funded by PharmGate Animal Health #140896
The influence of communication to youth engagement in agripreneurship in the Kamuli District, Uganda.
No full textYouth engagement in agripreneurship is critical to the sustainability and modernization of agriculture in Uganda, where the majority of the population depends on farming for their livelihoods. This study explored the role of communication in influencing youth engagement in agripreneurship in Kamuli District, Uganda. Specifically, the research examined the types and sources of agripreneurship information accessed by youth, the communication channels used, challenges encountered in information access, and how agricultural information was utilized in agribusiness. The study employed a qualitative case study design, using semi-structured interviews with 17 youth agripreneur’s affiliated with the Iowa State University–Uganda Program (ISU-UP). Data were analyzed thematically, guided by the Model of Communication Engagement adopted from Lasswell’s Model of Communication and the Multilevel Model of Communication Engagement.
Findings revealed that youth accessed a range of information types, including agronomic guidance, market updates, weather forecasts, and value addition strategies, primarily through trusted channels such as mobile phones, community radios, extension officers, and peer networks. However, challenges such as high data costs, poor network connectivity, limited access to government extension services, and unclear sources of information hindered consistent engagement. The study also found that youth actively utilized the information to make decisions on crop selection, market timing, and diversification, though their capacity to do so was influenced by education levels, trust in the source, and economic constraints. The results highlight the need for inclusive, youth-centered communication strategies that improve the reach and credibility of agricultural information. These insights offer valuable implications for policymakers, educators, and development practitioners working to enhance youth participation in agripreneurship
Polyhydroxyalkanoate fibers: Applications, important properties, and optimization
No full textA wide variety of industries globally are attempting to become more environmentally friendly and reduce pollution. One of these industries is textiles, in which undegradable polymers are often used and cause buildup in the world’s natural environments, such as oceans, lakes, and forests. The replacement of these undegradable polymers used to create fibers for textiles with more biodegradable and bio-friendly materials will help to aid environmental conservation efforts.
One promising class of materials for use in environmentally conscious applications is polyhydroxyalkanoates (PHAs). These polymers have been shown to be biodegradable in all environments (marine, soil, compost) by passing ASTM standards for biodegradation. However, these materials are difficult to spin into fibers due to rapid thermal degradation that is onset upon melting of PHAs, which is the most common method for spinning PHA fibers. Other, solution-based spinning methods have been tried, but very little optimization of solution-based PHA fiber spinning parameters has been conducted.
To gain more information on whether PHAs can be used for spinning fibers that are high enough quality for varying textile applications, this study entails the spinning and analysis of three different PHAs, poly(3-hydroxybutyrate) (PHB), poly(96% 3-hydroxybutyrate-co- 4% 4-hydroxybutyrate) (sP3HB4HB), and poly(68% 3-hydroxybutyrate-co- 32% 4-hydroxybutyrate) (aP3HB4HB). The goal of this analysis is to gain information on the fibers spun with these materials, such as the optimal wet spinning parameters for each of these materials and the outgoing properties of these fibers.
Based on the results provided in this study, it has been determined that sP3HB4HB was the best of the materials tested for spinning PHA fibers. PHB fibers were unable to be drawn in any meaningful capacity before breaking. aP3HB4HB fibers did not solidify quickly enough to prevent them from sticking to (and merging with) themselves, which caused difficulties in creating a long, steady fiber to draw and collect for further study. Further testing and optimization of these PHA fibers is still a worthwhile endeavor to pursue, in which slower rates of production are useful to give fibers more time to solidify before being pulled through the wet spinning and drawing setup