Open Research Oklahoma (Oklahoma State Univ.)
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Utilization of AI in top surgery literature: Insights from a cross-sectional study
No full textBackground: Artificial intelligence (AI) is reshaping the landscape of surgical research by optimizing data analysis, systematic reviews, and clinical applications. Despite its potential, the integration of AI introduces important challenges, including transparency, ethical usage, and the reproducibility of findings. Addressing these issues is essential to ensure responsible and effective implementation within the field. This study investigates how leading surgical journals confront these challenges and embrace emerging opportunities by analyzing their author instructions and editorial policies. Examining these guidelines offers valuable insights into the evolving role of AI in surgical research and its potential impact on the future of the field.Methods: A cross-sectional review was conducted on the top peer-reviewed surgical journals, ranked according to the 2023 SCImago SJR indicator. Data were collected from each journal’s “Instructions for Authors” to assess policies related to artificial intelligence, including AI-specific reporting guidelines, authorship criteria, and the use of AI in manuscript preparation and image creation. Correlational analyses were performed to examine potential relationships between the presence of AI policies and various journal characteristics. This study provides insights into how surgical journals are addressing the evolving influence of AI in academic publishing.Results: Out of the 100 journals reviewed, 84% addressed AI usage in their author instructions, with the majority prohibiting AI authorship while requiring disclosure of AI involvement in submissions. AIgenerated content was permitted by 46% of the journals, and 49% approved the use of AI-generated images. Journals with higher impact factors were more likely to feature detailed AI-related policies. However, significant gaps in standardization and clear guidance on AI use persist across the field.Conclusion: Although many surgical journals acknowledge the role of AI in research, only a few endorse AI-specific reporting guidelines, hindering the standardization and transparency of AI usage. To promote ethical, reproducible, and high-quality research in this era of AI-driven innovation, we recommend the adoption of comprehensive guidelines tailored to the responsible use of AI technologies
Using microbial competition in the CF lung to identify antibiotic activity
No full textCystic Fibrosis (CF) is a genetic disease characterized by the accumulation of thick and sticky mucus in the airways that serve as a substrate for the growth of a polymicrobial community. Within this community, microbes engage in cooperative and competitive interspecies interactions. Pseudomonas aeruginosa and other opportunistic respiratory pathogens produce secondary metabolites that modulate the growth and virulence of co-infecting microbes. To investigate these interactions, we analyzed inhibition patterns between a collection of CF bacteria isolated from people with CF against eight pathogens (Achromobacter xylosoxidans, Burkholderia cepecia, Burkholderia cenocepcia, Neisseria meningitis, Pseudomonas aeruginosa, Staphylococcus aureus, Streptococcus oralis). We assayed antimicrobial production from these bacteria through quantitative cross-streak assays. After incubation, we took liquid cultures of the eight pathogens and streaked them perpendicular to the initial CF bacterial sample streak, allowing them to grow in direct contact with samples such as the NB0275 bacterial isolate. The plates were then incubated for another 24 hours before assessing potential interactions between the organisms. We aim to uncover key interspecies interactions within the CF-lung environment and determine how secondary metabolites influence pathogen dynamics in these chronic infections. Our research seeks to identify secondary metabolites that could serve as alternative therapeutics to traditional antibiotics for people with cystic fibrosis. Identifying these compounds could represent a significant step forward in improving treatment strategies for CF patients.Microbiology and Molecular Genetic
Distortion effects of the relationship between sales control systems and motivation: A signaling theory perspective
No full textSalespeople are the primary boundary-spanning agents between the firm and customers, and it has long been assumed that intrinsic and extrinsic motivation are key drivers of performance. However, the link between sales control systems, motivation, and ultimately sales performance remains inconsistent. Under signaling theory, this dissertation reframes formal outcome control and process control as signals that originate from the firm and are received by the individual salesperson and moderated by a novel boundary condition, distortion. Distortion is defined as interference of the initial firm signals through managerial behaviors and actions and how this affects intrinsic and extrinsic motivation, and subsequently sales performance. A two-study design was conducted, the first study being a pretest to validate measures and the novel distortion constructs, and the primary research study that surveyed 416 U.S. sales professionals across B2C and B2B contexts. Results show that distortion significantly moderates the relationships between control system signals and I/E motivation, sometimes contrary to expectations. The hypothesis that distortion negatively affects the relationship between perceived outcome control signals and extrinsic motivation found support, as did the hypothesis that distortion negatively moderates the relationship between perceived process control and intrinsic motivation. However, I found a conflicting positive moderation effect for the relationship between perceived process control signals and extrinsic motivation. This supports the premise that managerial actions that run counter to initial control signal expectations can either dampen or heighten motivation of the individual salesperson. Both intrinsic and extrinsic motivation have positive effects on sales performance, and additional analyses support some indirect paths from the initial sales control signals to performance through I/E motivation. Contributions of this dissertation include: 1) positioning formal sales controls as organizational signals under signaling theory, 2) introducing and validating a novel concept, distortion, and its associated constructs, and 3) providing further evidence of how sales controls effects performance through motivation and the distortion effect. For managers, this research suggests that while actions by managers that run counter to original expectations set down by the firm can have a negative effect, cases exist where these same actions can act as a corrective mechanism
Mitigating runway incursions: Evaluating advanced technologies and methodologies for enhanced aviation safety
No full textRunway incursions are not a new problem in air travel, but they have become more dangerous and concerning recently. As travel demands increase, factors such as airport congestion, pilot distractions, technology, human factors, and communication play key roles in preventing runway incursions. This study aims to explore pilots’ opinions to identify potential causes and methods—both technological and human-based—to reduce runway incursions and improve aviation safety. Four main conclusions emerged: First, systemic causes remain the primary factors behind runway incursions, with communication failures and situational awareness issues consistently reported by pilots as major safety risks. Second, cockpit technologies like EFBs, moving maps, and ADS-B In significantly enhance safety but must be used carefully to avoid overreliance. Third, ground-based systems such as RWSL and ASDE-X are very effective but are adopted unevenly, making smaller airports more vulnerable. Finally, airport size and complexity affect incursion risk in different ways, underscoring the importance of tailored prevention strategies rather than a one-size-fits-all approach
Understanding the distinct roles of core and low-abundant human gut microbiota in host metabolism and inflammatory disease using defined microbial consortia in germ-free mice
No full textThe human gut microbiota, composed of trillions of microorganisms, plays a pivotal role in shaping host physiology, including metabolism, immune development, and disease susceptibility. Within this complex ecosystem, core microbiota, species consistently found across individuals, are thought to support homeostasis, while low-abundant taxa, though less represented, may exert disproportionately strong effects. Yet, the distinct contributions of these groups remain unclear.
In this study, we developed two defined, culturable human gut microbial consortia: CoreCom, consisting of highly prevalent taxa identified from global metagenomic datasets, and LowCom, comprising rare but potentially functional species from the same culture library. Germ-free (GF) mice were colonized with these consortia individually or in combination to systematically evaluate their colonization dynamics, functions, and impact on host physiology.
Chapter 1 characterized CoreCom assembly in GF mice using longitudinal 16S and metagenomic analyses. We found that CoreCom stably colonized the host and supported key immunomodulatory functions resembling those of complex human microbiota.
Chapter 2 explored microbial influence on host metabolism. Surprisingly, LowCom-colonized mice showed increased weight gain and adiposity, even under a standard chow diet, effects not seen in CoreCom mice, suggesting that rare taxa can drive metabolic phenotypes.
In Chapter 3, we assessed microbial contributions to intestinal inflammation using a DSS-induced colitis model. CoreCom protected against epithelial damage and inflammation, while LowCom exacerbated colitis, potentially due to its high Firmicutes content and pro-inflammatory traits.
Together, this work demonstrates distinct, non-redundant roles of core and low-abundant gut microbes and introduces a minimal human-derived microbiota platform for future mechanistic studies
Economic prosperity determinant and the role of political regime: A statistical and machine learning approach
No full textNations across the globe strive for sustainable economic growth as a means to achieve economic prosperity (Saxena et al., 2021). Advanced economies, especially those governed by democratic regimes, have achieved economic success, while emerging economies exhibit economic growth trends in both democratic and autocratic systems. In contrast, developing nations, governed by a combination of these regimes, still struggle to achieve economic prosperity. Despite scholars’ efforts to address these disparities, establishing explicit, meaningful, and significant links between economic prosperity and potential explanatory variables has proven challenging (Roll & Talbott, 2003). The extent to which economic growth inherently leads to prosperity, along with the role of political systems and the influence of internal and external factors, remains an area requiring further inquiry. Drawing on the foundational work of Solow’s neoclassic economic growth that was later extended by Islam (1995) to incorporate variations in steady-state income levels across countries, this study examines internal and external factors that contribute to a nation’s long-term prosperity, with a focus on variations in political, geographical, and other macroeconomic factors. This study utilizes a statistical and a machine learning algorithm approach to develop a precise and stable predictive model for estimating economic prosperity across three distinct groups of countries—advanced, emerging, and developing economies.
The findings of this study using both approaches indicate that the human development index is a key factor in promoting economic prosperity across all three levels of economic development, but its impact is more pronounced in developing nations. Additionally, the results suggest the presence of an optimal or convergence zone in which the influence of regime type is most pronounced by showing that when a political system becomes excessively democratic or overly autocratic, the moderating capacity of regime type on the relationship between economic freedom and economic prosperity, as well as between technological engagement and economic prosperity, appears to diminish. Consequently, moderate levels of democracy may provide the most conducive institutional environment for leveraging internet use and economic freedom to promote economic prosperity
Flight dynamic modeling and considerations for fixed wing aircraft in realistic urban wind environments
No full textA modular simulation framework has been developed in Simulink to evaluate aircraft response within urban wind fields. The framework allows users to modify various
aspects of the environment to support targeted studies relevant to aircraft operations in
urban settings. These customizable elements include arbitrary wind field domains, control
law architectures, and aerodynamic models such as vortex lattice methods or stability and
control derivatives obtained from flight testing. The studies that informed the development
of this environment, along with its application to urban wind field research, are presented
below.
The aerodynamic solvers employed in this work include an unsteady vortex lattice method
(UVLM), implemented via the aeroelastic toolset SHARPy, and a compact vortex lattice
method (CVLM) chosen for its computational efficiency. The UVLM was utilized in the
initial two studies to investigate how aircraft with varying wing loadings respond to wind
fields generated using Large Eddy Simulations (LES) and reduced-order models derived from
Proper Orthogonal Decomposition (POD). These studies provided valuable insights into the
level of wind modeling fidelity required for aircraft operating in urban environments.
Due to the high computational cost of wake modeling with UVLM and the limited control law
capabilities within SHARPy, a modular Simulink environment was developed to offer greater
flexibility for these analyses. The CVLM was selected as the primary aerodynamic model in
this environment, enabling efficient exploration of broad spatial domains within urban wind
fields. Furthermore, the simulation was integrated with ArduPilot control laws to support
waypoint tracking and disturbance rejection. The 4-point method was also integrated into
the state formulation to more accurately account for asymmetric loadings encountered as
the aircraft navigates through wind structures. For validation, the results for the CVLM are
compared to a flight-derived model created using system identification techniques.
Additionally, a machine-learning algorithm known as a Convolutional Autoencoder (CAE)
is employed to represent the urban wind field used in previous studies. This approach
achieved an order-of-magnitude reduction in storage requirements compared to raw look-up
tables, without compromising the accuracy of the reconstructed flight dynamic time histories.
Moreover, it demonstrated noticeable improvements over the POD models developed in
earlier work
Long-term effects of an early simulated viral infection on behavior and immunity in zebra finches (Taeniopygia guttata)
No full textEarly-life experiences can permanently shape physiology and behavior through developmental programming of neuroimmune and endocrine systems. This dissertation investigates how early-life immune challenge (ELIC) with the viral mimetic polyinosinic:polycytidylic acid (poly I:C) influences immune ontogeny, growth, cognition, and social behavior in zebra finches (Taeniopygia guttata). Across four integrative chapters, I examined the mechanistic and behavioral consequences of transient immune activation during a critical developmental window. In Chapter 1, I demonstrate that saline-injection alters antibody profiles, revealing that even brief injection stress can program lasting changes in immune phenotype. Chapter 2 shows that ELIC induces acute sickness behaviors—such as reduced begging and altered activity—and long-term reductions in mass, reflecting energetic trade-offs between immunity and growth. In Chapter 3, I report that early immune challenge impairs learning proficiency and increases sex-dependent cognitive differences, with males more strongly affected than females. Finally, Chapter 4 reveals that developmental immune activation reshapes adult social behavior and stress reactivity: poly I:C-challenged birds exhibit reduced aggression and anxiety-like behaviors, indicating a shift toward more passive coping strategies. Together, these findings demonstrate that early immune activation can reorganize neuroimmune–endocrine circuits to produce integrated, lifelong changes in physiology and behavior. This work highlights immunity as a central driver of behavioral plasticity, linking developmental perturbation to individual variation in fitness and disease ecology