University of North Texas

Introduction The severe exercise intensity domain can be defined as the range of work rates or speeds over which VO2max can be elicited. Objectives: Our purpose was to determine if critical speed (running analog of critical power) identifies the lower boundary of the severe domain and to identify the upper boundary of the domain. Methods Twenty-five individuals performed five running tests to exhaustion, each lasting > 2.5 min and < 16 min. The two-parameter speed vs time-to-exhaustion relationship generated values for critical speed and the three-parameter speed vs time-to-reach-VO2max relationship generated values for the threshold speed above which VO2max can be elicited. The relationships were solved to calculate the minimum time needed to elicit VO2max. Results Critical speed (3.00 ± 0.38 m·s⁻¹) and the threshold speed above which VO2max can be elicited (2.99 ± 0.37 m·s⁻¹) were correlated (r = 0.83, p < 0.01) and did not differ (p = 0.70), confirming critical speed as the lower boundary of the severe domain. The minimum time needed to elicit VO2max (103 ± 7 s) and the associated highest speed at which VO2max can be elicited (4.98 ± 0.52 m·s⁻¹) identified the upper boundary of the severe domain for these participants. Conclusion The critical power concept, which requires no metabolic measurements, can be used to identify the lowest speed at which VO2max can be elicited. With addition of metabolic measurements, mathematical modeling can also identify the highest speed and shortest exercise duration at which VO2max can be elicited. Evidence Level I; Validating cohort study with good reference standards. Keywords: Exercise; Running; Maximal Voluntary Ventilation; Energy Metabolism

Introduction The severe exercise intensity domain can be defined as the range of work rates or speeds over which VO2max can be elicited. Objectives: Our purpose was to determine if critical speed (running analog of critical power) identifies the lower boundary of the severe domain and to identify the upper boundary of the domain. Methods Twenty-five individuals performed five running tests to exhaustion, each lasting > 2.5 min and < 16 min. The two-parameter speed vs time-to-exhaustion relationship generated values for critical speed and the three-parameter speed vs time-to-reach-VO2max relationship generated values for the threshold speed above which VO2max can be elicited. The relationships were solved to calculate the minimum time needed to elicit VO2max. Results Critical speed (3.00 ± 0.38 m·s⁻¹) and the threshold speed above which VO2max can be elicited (2.99 ± 0.37 m·s⁻¹) were correlated (r = 0.83, p < 0.01) and did not differ (p = 0.70), confirming critical speed as the lower boundary of the severe domain. The minimum time needed to elicit VO2max (103 ± 7 s) and the associated highest speed at which VO2max can be elicited (4.98 ± 0.52 m·s⁻¹) identified the upper boundary of the severe domain for these participants. Conclusion The critical power concept, which requires no metabolic measurements, can be used to identify the lowest speed at which VO2max can be elicited. With addition of metabolic measurements, mathematical modeling can also identify the highest speed and shortest exercise duration at which VO2max can be elicited. Evidence Level I; Validating cohort study with good reference standards. Keywords: Exercise; Running; Maximal Voluntary Ventilation; Energy Metabolism

Racism in medicine has adversely affected minorities in the US for centuries. This scoping review explores the literature on how medical schools in the US teach students how to address racism and its effects on the practice of medicine, especially regarding adverse health outcomes for racially minoritized patients. This review used the Critical Race Theory framework to investigate whether teaching interventions had inclusive learning design features and if these effectively sensitized the students to racism while training to be healthcare workers. The results suggest that while a lot has been accomplished, there is still much to be done to achieve racial equity in healthcare.

As educational institutions increasingly rely on technology to reach learners, there is a need to interrogate the inclusiveness, equity, or bias inherent in these tools. This scoping review of literature evaluated research studies to assess the presence or lack of inclusive learning design in educational technologies targeting English Language Learners in K-12 schools in the United States. Through this study, we note although creators of educational technology are striving to incorporate UDL guidelines in K12 learning technologies, much remains unaddressed to realize these goals. Keywords: Inclusion, English language learners, equity, Universal Design for Learning, social justice.

Introduction: While usability/user experience (UX) has a long and intertwined history with technical and professional communication (TPC), it is unknown how usability/UX is reflected within TPC research and how that reflection has shifted over time. Literature review: Prior studies on the role of usability/UX in TPC have found that usability/UX appears infrequently in TPC research and curriculum requirements. However, usability/UX remains a routinely referenced core identity of TPC. Research questions: 1. To what degree is usability/UX studied in TPC scholarly journals? 2. When TPC researchers study usability/UX, what are they studying? Methodology: A database of TPC-based usability/UX articles was collected through a defined search method. Articles were coded for primary or secondary emphasis on usability/UX, contribution to TPC, object of analysis, method of data collection, and major takeaway. Results: Less than 8% of the total publications in the field are tied to usability/UX, though the percentage has increased in the most recent timeframe (2020–2022). Publications are shifting from research that expands usability/UX knowledge to research that uses usability/UX to explain TPC phenomena. In addition, the object of analysis has shifted to process-centric analysis, design thinking has become an increasing component of TPC usability/UX research, and over a quarter of the research on usability/UX provided did not provide enough methodological description to enable replicability. Conclusion: Although usability/UX has been consistently published in the TPC research journals, the amount of research suggests that usability/UX is not core to TPC's field identity.

Background: The intertwined fields of technical and professional communication (TPC) and user experience (UX) have positioned graduates of TPC programs as strong candidates for careers in UX. Literature review: Although there is some scholarship addressing competencies required for UX positions as well as some investigation into UX course content within TPC programs, there is still a need for a comparative analysis of outcomes in UX courses in TPC and industry expectations for UX positions. Research questions: 1. What qualifications are essential to current UX industry positions? What qualifications are stated in current UX industry advertisements? 2. How do these qualifications compare to a sample of existing UX outcomes within TPC programs? Research methodology: A qualitative content analysis of two datasets—a collection of UX job advertisements and a collection of UX course outcomes—was conducted through a systematic coding of texts. Qualifications and outcomes were categorized by UX competencies needed prior to employment. Results/discussion: Results show job ads prioritize on project management including Agile and Scrum, and other skills such as writing, designing prototypes, software and coding languages, and portfolios. Course outcomes reflect strengths in writing and design, but do not include significant reference to specific concepts or tools. Suggestions for improving TPC/UX courses include diversifying existing skills and addressing deficient skills in project management and digital literacies. Conclusion: Challenges for re-envisioning UX courses in TPC programs are considered and addressed.

CE time series are generated by critical dynamic interactions among units within complex dynamic ONs that after disruption spontaneously reorganizes themself into states of self-organized temporal criticality (SOTC). The time intervals between events are statistically independent and therefore the CE statistics are renewal having an inverse power law (IPL) for the waiting-time probability density function (PDF). The IPL index \(\mu \) is a direct measure of the complexity level the CE time series is also given by the fractional dimension \(D(=\mu )\); for \(2<D<3\) the CE time series has a finite average time between CEs (ergodic); whereas for \(1<D<2\) the time average between events diverges (non-ergodic). Information-gradients not energy-gradients dominate living networks, and one version of this role-reversal we call the Wiener Hypothesis (WH); another is related to the principle of complexity management (PCM) whereby the IPL index of the power spectral density \(\beta \) is related to the fractal dimension by \(D=3-\beta \). The transfer of information is restorative in the arm-in-arm walking of young therapists with elderly individuals having impaired gait and becomes the archetype for the CE rehabilitation therapy (CERT), with maximum information being exchanged between two interacting ONs having equal fractal dimensions.

Operator algebra is introduced without being explicit about how we do things but hopefully conveys in words what we do not show with formal mathematics. The nomenclature “fractal” calculus replaces the historic misnomer “fractional” calculus and thereby restricts CERT to the non-integer calculus applied to phenomena that satisfy the fractal paradigm. The scaled PDF is shown to be the solution to a fractal kinetic equation (FKE) and to be necessary for obtaining the scaling by the fractal dimension observed in CE time series. The arguments for replacing integer-order rate equations with fractal-order rate equations, when the empirical evidence forces us to do so, are presented, as is the fractal probability calculus having IPL solutions. The latter solution satisfies the scaling behavior of simultaneously measured time series from the brain, heart, and lungs and provides a new kind of synchronization [complexity synchronization (CS)]. Two or more time series may not have their time series in synchrony and yet have their scaling indices in CS because the latter occurs at the level of the IPL index. This provides new insight into health, disease, and rehabilitation of ONs. We argue that a morbid ON can be rehabilitated to healthy functionality using a CS protocol.

Herein theoretical ideas are presented to demonstrate the utility of CEs in uncovering what is often hidden in multiply interacting ON time series. We focus on what can be learned about ONs’ resilience using what we know about modified diffusion entropy analysis (MDEA) to reveal the influence of CEs on an ON’s ability to perform its healthy function. We emphasize processing empirical time series hosting invisible CEs that consists of a mix of CEs and non-CEs. These notions have recently been applied to heart rate variability (HRV) time series, and we review those applications to convince the reader of their wider range of utility in the form of CERT. The utility of CE time series is explored to determine the efficacy of a noninvasive type of rehabilitation for certain kinds of neurodegenerative diseases. We hypothesize how complexity matching among complex dynamic ONs can be used to define this kind of rehabilitation, one that mimics nature’s own strategy for healing injuries and recovering from disease based on the noninvasive driving of a morbid ON by a healthy ON of the same kind. The first question we need to answer is: How can invisible CEs be detected?

The potential role that CERT can play in rejuvenating the human brain functionality following the pathophysiology of neurodegenerative diseases is discussed. CERT assumes that ONs form a communications web within the body. The ONs exchange information with one another through fractal time series, each healthy ON having an internal balance of crucial and non-crucial events. In addition to carrying out the normal functions of the body, these ONs play a central role in using complexity synchronization (CS) to alleviate the disruption of brain dynamics caused by neurodegenerative disease and/or injury. Of particular interest in this essay is the noninvasive rehabilitation of the neurodegeneration of the brain once a given pathophysiology has been initiated. The rehabilitation discussed herein is theoretical in that CERT is based on several theoretical assumptions that have only been partially tested, but whose veracity can be further vetted by processing additional empirical ON datasets. Can we mitigate the processes associated with the neurodegeneration within the brain associated with Alzheimerís Disease (AD) and/or Parkinsonís Disease (PD) using CERT? To address this question, we examine in some detail the breakdown of communication among neurons in a somewhat better understood pathophysiology process than that available for either AD or PD.

Introduction: Digital technologies have completely transformed the way humans work, communicate, and socialize over the last three decades. Human beings are now completely dependent on their digital devices to interact with the world. That transformation has not come without challenges. Digital technologies have impacted the mental well-being of humans like no other technology before it has done. Methodology: The book chapter examined the complex relationship between the digital world and how it is impacting the mental well-being of individuals. The preceding chapters present both pros and cons of the digital era with regard to brain health as well as policy recommendations. Conclusion: Digital technologies have the potential to provide solutions to the most pressing questions humanity is facing today. The perfect balance will require moderation and oversight when interacting with the digital world.

This opening essay provides the medical practitioner, biophysical researcher, and student (of any age) with an introduction to a new vocabulary based on the universal notion of fractals and thereby the reason for the essay’s title. A brief history as to how complexity developed into a science since WWII based on the critical dynamics of nonlinear networks provides a rational for the importance of complexity in the understanding of physiologic time series. A measure of the complexity of a dynamic organ network (ON), e.g., the brain, is given by the fractal dimension of the fluctuating time series that is uniquely related to the inverse power law (IPL) index of the power law spectrum. Renewal statistical process called crucial events (CEs) describe the statistics of ON time series. How to think about CEs time series, the fractal paradigm, and the fractional calculus is discussed in terms of simple random walk concepts, used to generate both monofractals and multifractals, the latter describing processes with time-dependent fractal dimensions. The spectral width of a multifractal measures ON’s health and therefore of the human body. A review of recent experiments on mouse models of Alzheimerís disease motivates the development of a rehabilitation therapy based on CEs.

Aerosol Optical Depth (AOD) is a crucial atmospheric parameter in comprehending climate change, air quality, and its impacts on human health. Satellites offer exceptional spatiotemporal AOD data continuity. However, data quality is influenced by various atmospheric, landscape, and instrumental factors, resulting in data gaps. This study presents a new solution to this challenge by providing a long-term, gapless satellite-derived AOD dataset for Texas from 2010 to 2022, utilizing Moderate Resolution Imaging Spectroradiometer (MODIS) Multi-angle Implementation of Atmospheric Correction (MAIAC) products. Missing AOD data were reconstructed using a spatiotemporal Long Short-Term Memory (LSTM) convolutional autoencoder. Evaluation against an independent test dataset demonstrated the model’s effectiveness, with an average Root Mean Square Error (RMSE) of 0.017 and an R² value of 0.941. Validation against the ground-based AERONET dataset indicated satisfactory agreement, with RMSE values ranging from 0.052 to 0.067. The reconstructed AOD data are available at daily, monthly, quarterly, and yearly scales, providing a valuable resource to advance understanding of the Earth’s atmosphere and support decision-making concerning air quality and public health.

ACIGS solar cells are exposed to targeted radiation to probe the front and back interfaces of the absorber to assess the impact of space environments on these systems. These data suggest ACIGS cells are more radiation‐hard than early CIGS devices likely due to the lower defects densities and more ideal interfaces in the ACIGS system. A combination of J‐V and EQE measurements indicate some improvement in the performance of the device due to the effects of local heating in the dominant ionizing electronic energy loss regime of proton irradiation that anneal the upper CdS/ACIGS interface. However, non‐ionizing energy losses at the base of the solar cell also appear to inhibit minority carrier collection from the back of the cell at the ACIGS/Mo interface, which is discussed in terms of defect‐mediated changes in the doping profile, the Ga/Ga+In ratio, and impurity composition after proton irradiation. This article is protected by copyright. All rights reserved.

Wildfires can be devastating for social and ecological systems, but the recovery period after wildfire presents opportunities to reduce future risk through adaptation. We use a collective case study approach to systematically compare social and ecological recovery following four major fire events in Australia and the United States: the 1998 wildfires in northeastern Florida; the 2003 Cedar fire in southern California; the 2009 Black Saturday bushfires in Victoria, southeastern Australia; and the 2011 Bastrop fires in Texas. Fires spurred similar policy changes, with an emphasis on education, land use planning, suppression/emergency response, and vegetation management. However, there was little information available in peer-reviewed literature about social recovery, ecological recovery was mostly studied short term, and feedbacks between social and ecological outcomes went largely unconsidered. Strategic and holistic approaches to wildfire recovery that consider linkages within and between social–ecological systems will be increasingly critical to determine if recovery leads to adaptation or recreates vulnerability.

Smart healthcare becomes one of the popular research areas in recent years. This research proposes to expand the state-of-art of smart healthcare by incorporating solutions for obsessive compulsive disorder (OCD). Classification of OCD by analyzing oxidative stress biomarkers (OSBs) through a machine-learning mechanism is a significant development in the study of OCD. However, this procedure requires the collection of OCD class labels from hospitals, collection of corresponding OSBs from biochemical laboratories, integrated and labeled dataset creation, use of suitable machine-learning algorithm for designing OCD prediction model, and making these prediction models available for different biochemical laboratories for OCD prediction for unlabeled OSBs. Further, from time to time, with significant growth in the volume of the dataset with labeled samples, redesigning the prediction model is required for further use. The entire process demands distributed data collection, data integration, coordination between the hospital and the biochemical laboratory in real-time, dynamic machine-learning model design for OCD prediction, and making the machine-learning model available for the biochemical laboratories. Considering these requirements, Accu-Help a fully automated, smart, and accurate OCD detection conceptual model is proposed to help the biochemical laboratories for efficient detection of OCD from OSBs. OSBs are classified into three classes: Healthy Individual (HI), OCD Affected Individual (OAI), and Genetically Affected Individual (GAI). The main component of this proposed framework is the machine-learning-based OCD class prediction model design. Accu-Help uses a neural network-based approach with an OCD class prediction accuracy of \(86\pm 2\%\).

In this chapter, I discuss various strategies for mentoring graduate students, particularly in terms of teaching them the research process and how to get published. I focus on the stages of the mentoring process—from first-year students to those taking comprehensive examinations—and developing a dissertation topic. Further, I emphasize how learning the research process not only helps graduate students progress in their theses and dissertations, but also helps them get published. I outline how I mentor students in getting published, first by observing how I write a published paper, then coauthoring, and then encouraging them to publish on their own.

Introduction/Objective Pancreatic neuroendocrine tumors (P-NET) are diverse tumors with slow growth and varying levels of potential malignant behavior. The overall 5-year survival rates for P-NET are approximately 95%, 72%, and 23% if localized, and in cases of regional and distant metastasis respectively. These tumors exhibit a low rate of mutations but show varied genomic abnormalities, contributing to their unpredictable nature. In this study, we aimed to find the genetic alterations observed in metastatic P-NET for their potential therapeutic and prognostic applications and compared these tumors to metastatic neuroendocrine tumors originating from the small intestine (SI-NET) and rectum (R-NET). Methods/Case Report We initially extracted RNA-seq datasets from the GEO database. Next, we analyzed the datasets in six defined groups and differentially expressed genes were obtained by comparison to controls. Either of the groups of P-NET, SI-NET, or R-NET cases were evaluated individually and altogether (primary vs metastatic; 4 groups) and in 2 groups comparing primary/metastatic P-NET cases to primary/metastatic cases of SI-NET plus R- NET. Using a Venn diagram, we isolated the commonality between gene expression profiles. Afterward, we obtained the ontology of genes and signaling pathways from KEGG and Enrichr. Finally, we drew the protein network and evaluated the proteins in GEPIA and TCGA clinical databases. Results (if a Case Study enter NA) A total of 212 cases including 113 P-NET (83 primary and 30 metastatic), 81 SI- NET (44 primary and 37 metastatic), and 18 R-NET (3 primary and 15 metastatic) were selected. We found 8 genes (ORM1, HP, APOA2, HRG, KNG1, ALB, SERPINC1, and AHSG) are mutually markedly upregulated in cases of metastatic P-NET and SI-NET but were not identified in metastatic R-NET. The products of these genes were predominantly involved in complement cascades. CABYR, MYCN, and CALU genes were among the most upregulated genes specifically isolated in the metastatic P-NET that played roles in activating intracellular signaling pathways, transcription, and protein folding. Conclusion The shared upregulated genes in metastatic P-NET and SI-NET may support the relatively better prognosis of these tumors in comparison to the metastatic R-NET due to the activation of the complement cascade (immune system). Furthermore, the identified genetic alterations in the metastatic P-NET cases may have potential therapeutic and prognostic applications for future use.

Practice guidelines for early childhood education (ECE) and clinical autism interventions (Naturalistic Developmental Behavioral Intervention, NDBI) have emerged separately in history, represent different disciplines, and operate within different service systems. This manuscript identifies priorities, principles, and practices that are shared across the NDBI and ECE frameworks, unique to each framework but compatible with the other, or in conflict. Both frameworks support converging inclusive ECE models focused on autism in that they are both grounded in responsive relationships, natural learning environments, and strategies to promote children’s motivation and active engagement. While compatible in general, each framework extends the other in important ways. For example, NDBI goes beyond the ECE frameworks by focusing on a more fine-grained examination of learning strategies and targets. Opportunities for bridging gaps are identified, including the use of implementation science frameworks to integrate perspectives from different stakeholder groups, supporting the scale-up of inclusion preschools in community settings.