Bradley University

Background Sexual minority (SM) female adolescents involved in the legal system experience marginalization and health inequities. This study examined the differences in psychosocial functioning and risk behaviors among legally involved SM and heterosexual female adolescents to better understand their behavioral health needs. We hypothesized that SM females, as individuals at the intersection of two marginalized groups, would demonstrate greater psychiatric symptom severity and engagement in risk behaviors than their heterosexual counterparts. Methods Adolescents involved in the legal system (N = 423) enrolled in a prospective cohort study and completed baseline surveys assessing their demographics, SM status, psychiatric symptoms, substance use, and engagement in self-injurious, delinquent, and sexual risk behaviors. The responses of SM and heterosexual female adolescents (n = 193) were compared using bivariate and regression analyses. Results Participants were 12 to 18 years old (M = 14.49, SD = 1.55), ethnoracially diverse, and 38.3% identified as a SM. SM females, as compared to heterosexual females, reported more PTSD and emotional symptoms, difficulties with anger control and personal adjustment, and engagement in substance use, self-injurious, and sexual risk behaviors. Conclusion Legally involved SM female adolescents in this study had greater psychiatric, substance use, and sexual health treatment needs compared to their heterosexual peers. These findings highlight the need for enhanced understanding of how to effectively support SM female adolescents, including utilization of culturally sensitive and clinically informative screening practices that do not contribute to further discrimination within the legal system. Future work should aim to develop identity-responsive interventions tailored to this population.

Background Early mobility (EM) is beneficial for critically ill patients, but adoption in intermediate care units remains limited. Local Problem At the project site, fewer than 10% of patients admitted to the respiratory care unit (RCU) engaged in EM due to clinical severity, lack of staff confidence, and limited collaboration with physical therapy. Methods A pre- postimplementation quality improvement design was used to assess mobility outcomes. Interventions A nurse-driven EM program was implemented in a 10-bed RCU at a tertiary center. A multidisciplinary team delivered staff education, introduced an evidence-based protocol, and addressed barriers. Results Forty-eight patients were included in the project (22 preimplementation and 26 postimplementation). The number of physical therapy consultations increased from 36% to 73% ( P = .01), with 69% of patients achieving higher discharge mobility postimplementation versus 59% preimplementation. Length of stay and mortality were unchanged. Conclusions EM practices improve mobility in intermediate care through education and collaboration.

Our study examined individuals’ reactions to and perceptions of the Barbie (2023) movie as subversive (e.g., targeting the United States patriarchal structure) versus disparaging (e.g., perpetuating negative perceptions of men/women). More specifically, we examined how various attitudes (e.g., ambivalent sexism, adherence to traditional gender roles, feminist attitudes) related to individuals’ decision to see Barbie , their expectations for and enjoyment of the movie, and their perceptions of its use of subversive humor for social commentary. Participants ( N = 446, 71 % women, 79 % heterosexual, 78 % White, 65 % first-year students, M age = 19.40, SD = 3.25) were recruited from undergraduate courses from several Midwestern universities and completed a survey via Qualtrics. In line with our Selective Exposure Hypothesis , individuals with more egalitarian gender beliefs (e.g., higher levels of feminism, lower levels of sexism) were more likely to have seen Barbie . Similarly, in line with our Motivated Cognition Hypothesis , individuals with more egalitarian gender beliefs had more positive perceptions of Barbie , its messages, and intentions. Our findings further suggest that individuals who disagreed with Barbie ’s message may have avoided viewing the movie, and if they did not avoid it, they may have resisted its message. This research furthers our understanding of subversive humor in relation to gender issues by examining how it was perceived in Barbie, one of the most significant pop culture events of the 2020s thus far.

Prostate cancer (PC) remains a significant health challenge, with androgen receptor (AR) signaling playing a pivotal role in its progression. This study investigates the expression and functional implications of the transient receptor potential melastatin 8 (TRPM8) channel in PC, focusing on its interaction with AR and its impact on oncogenic pathways. We analyzed mRNA expression levels of TRPM8 and AR in PC tissues, revealing that TRPM8 is upregulated in benign and early-stage tumors but significantly downregulated in metastatic samples. This decline correlates with increased AR expression, suggesting a compensatory mechanism that enhances AR-driven tumorigenesis. RNA sequencing and pathway enrichment analyses demonstrated that TRPM8 knockout (KO) prostates exhibited significant alterations in gene expression, particularly in pathways related to extracellular matrix (ECM) remodeling, cell proliferation, and survival signaling. Notably, genes associated with metastasis, such as MMP2 and FAP, were upregulated in TRPM8 KO samples, indicating a potential role for TRPM8 in inhibiting tumor invasion. Furthermore, Gene Set Enrichment Analysis (GSEA) revealed positive enrichment of androgen response, angiogenesis, and epithelial–mesenchymal transition (EMT) pathways in TRPM8 KO prostates, reinforcing the notion that TRPM8 loss creates a pro-tumorigenic environment. Our findings suggest that TRPM8 functions as a molecular brake on PC progression, and its loss may contribute to the development of aggressive disease phenotypes. This study underscores the importance of TRPM8 as a potential therapeutic target and biomarker in PC, warranting further investigation into its role in cancer biology and treatment response.

This paper explores the enhancement of project‐based manufacturing education through the integration of advanced engineering software tools and hands‐on fabrication practices. The curriculum strategically combines computer‐aided design, moldflow simulation, and Mastercam with practical experiences such as computer numerical control milling and injection molding to provide students with a comprehensive understanding of the product development process. These tools bridge the gap between theoretical concepts and real‐world applications, enabling students to design, simulate, manufacture, and optimize products effectively. A semester‐long project serves as the cornerstone of the course, fostering critical thinking, problem‐solving, and decision‐making skills. To assess the impact of the course, a mixed‐methods research design was employed, incorporating student performance data, feedback surveys, and statistical analysis. The results indicate that integrating engineering software tools with hands‐on projects not only equips students with industry‐relevant skills but also enhances their ability to meet professional engineering standards, as outlined by ABET accreditation criteria. This study provides valuable insights into the effectiveness of project‐based learning and contributes to the broader discourse on engineering education methodologies. Additionally, a detailed literature review situates this work within the existing research landscape, highlighting its unique contributions and addressing gaps in educational practice.

For an early career ELA teacher; an experienced ELA teacher and mentor; and a teacher educator, navigating vulnerability and trust in mentoring relationships affords joy and learning in the beautiful chaos of ELA classrooms.

This research paper aims to examine the patterns of Arctic sea ice extent (ASIE) between 1979 and 2020 by using monthly data acquired from NSIDC. Our study employs seasonal time series models such as SARIMA, SARIMAX with global temperature anomalies as an exogenous variable, as well as the neural network (NNAR) models to forecast the future of ASIE in the next 100 years. To evaluate the accuracy of these models, we conduct fittings and analyze various prediction error metrics, MSE, MAE, and MAPE. Our findings reveal that the SARIMAX model outperforms others in out-of-sample forecasting (testing set), effectively integrating data components and accurately capturing both trends and seasonal variations, thus enhancing predictive accuracy for the ASIE relative to other models. According to the SARIMAX model, the overall coverage of sea ice is diminishing, and it is projected that the Arctic will experience its first ice-free month no later than September of 2076, with September of 2074 being a potential milestone. Additionally, the NNAR model suggests that ASIE will not disappear within the next 100 years.

The nine-banded armadillo (Dasypus novemcinctus: hereafter armadillo) was first recorded in the United States (U.S.) in the state of Texas in 1849 and has been expanding its range northward and eastward since then. With the widespread adoption of participatory science as well as the proliferation of nationwide wildlife game camera studies, occurrence data of armadillos can be compiled more rapidly and thoroughly than at any time in the past. Here, we use disparate data sources to update the current geographic distribution of the armadillo in the United States and use occurrence data from the leading edge of its range expansion to create a species distribution model to understand their relationship with landscape and bioclimatic factors. Since the last report on the geographic distribution of the armadillo in 2014, we show that armadillos have expanded to cover the entirety of Missouri and established in southern Iowa, expanded modestly within Kansas and Illinois, expanded northward and eastward in Indiana, expanded eastward in both Kentucky and Tennessee, established throughout the entirety of South Carolina and Georgia and established in the western third of North Carolina. Our species distribution model indicates that there is substantial opportunity for the species to continue to expand its geographic range, particularly in the Eastern United States. These results provide information to managers who are now or might soon be co-existing with the armadillo to proactively manage the species or inform the public regarding potential conflicts.

This study introduces a multimodal sentiment analysis system to assess and recognize human pain sentiments within an Internet of Things (IoT)-enabled healthcare framework. This system integrates facial expressions and speech-audio recordings to evaluate human pain intensity levels. This integration aims to enhance the recognition system’s performance and enable a more accurate assessment of pain intensity. Such a multimodal approach supports improved decision making in real-time patient care, addressing limitations inherent in unimodal systems for measuring pain sentiment. So, the primary contribution of this work lies in developing a multimodal pain sentiment analysis system that integrates the outcomes of image-based and audio-based pain sentiment analysis models. The system implementation contains five key phases. The first phase focuses on detecting the facial region from a video sequence, a crucial step for extracting facial patterns indicative of pain. In the second phase, the system extracts discriminant and divergent features from the facial region using deep learning techniques, utilizing some convolutional neural network (CNN) architectures, which are further refined through transfer learning and fine-tuning of parameters, alongside fusion techniques aimed at optimizing the model’s performance. The third phase performs the speech-audio recording preprocessing; the extraction of significant features is then performed through conventional methods followed by using the deep learning model to generate divergent features to recognize audio-based pain sentiments in the fourth phase. The final phase combines the outcomes from both image-based and audio-based pain sentiment analysis systems, improving the overall performance of the multimodal system. This fusion enables the system to accurately predict pain levels, including ‘high pain’, ‘mild pain’, and ‘no pain’. The performance of the proposed system is tested with the three image-based databases such as a 2D Face Set Database with Pain Expression, the UNBC-McMaster database (based on shoulder pain), and the BioVid database (based on heat pain), along with the VIVAE database for the audio-based dataset. Extensive experiments were performed using these datasets. Finally, the proposed system achieved accuracies of 76.23%, 84.27%, and 38.04% for two, three, and five pain classes, respectively, on the 2D Face Set Database with Pain Expression, UNBC, and BioVid datasets. The VIVAE audio-based system recorded a peak performance of 97.56% and 98.32% accuracy for varying training–testing protocols. These performances were compared with some state-of-the-art methods that show the superiority of the proposed system. By combining the outputs of both deep learning frameworks on image and audio datasets, the proposed multimodal pain sentiment analysis system achieves accuracies of 99.31% for the two-class, 99.54% for the three-class, and 87.41% for the five-class pain problems.

This study explores the methods in which studio instructors present artistic research practices to students in their courses. Despite extensive literature examining librarians’ support for art students’ needs, there is little written on connections between library services and the unique modes of research that take place in the studio classroom. Through qualitative analysis of semi-structured interviews with studio instructors, this study aims to fill this gap in understanding how research is conceptualized and taught within art studio contexts. The findings uncover themes of relationality, reflection and research as concurrent with and inseparable from making. The authors advocate for increased interdisciplinary collaboration and expanded librarian involvement in supporting and enriching artistic research endeavours.

As software development becomes increasingly complex, defect prediction plays a crucial role in ensuring software quality. Existing software defect prediction methods face limitations in parameter selection for classification models, particularly in selecting the penalty factor and kernel function parameters in SVM. Traditional optimization methods often struggle with insufficient convergence precision and a tendency to fall into local optima. To address these issues, this paper proposes the reverse differential chimp optimization algorithm (RDChOA). RDChOA improves upon the traditional chimp optimization algorithm by incorporating the Hammersley sequence initialization, lens-imaging reverse learning strategy, and differential evolution strategy, thereby enhancing global search capability and reducing the likelihood of converging to local optima. RDChOA starts by using the Hammersley sequence to initialize the chimp population, increasing initial population diversity. In the later stages of the algorithm, the lens-imaging reverse learning strategy is employed to update the positions of attackers, further expanding the search space and avoiding local optima. Finally, the differential evolution strategy is applied to adjust the positions of regular chimp individuals, boosting global optimization performance. Through these innovations, RDChOA effectively optimizes the parameters of SVM, addressing the challenges of parameter selection and generalization capability faced by traditional defect prediction algorithms. Experimental results demonstrate that RDChOA performs excellently on eight benchmark test functions, outperforming other swarm intelligence optimization algorithms. Moreover, when applied to multiple public software defect prediction datasets, RDChOA-SVM also shows significant advantages in prediction accuracy.

Motivation: SNAPSHOT USA is an annual, multicontributor camera trap survey of mammals across the United States. The growing SNAPSHOT USA dataset is intended for tracking the spatial and temporal responses of mammal populations to changes in land use, land cover and climate. These data will be useful for exploring the drivers of spatial and temporal changes in relative abundance and distribution, as well as the impacts of species interactions on daily activity patterns. Main Types of Variables Contained: SNAPSHOT USA 2019–2023 contains 987,979 records of camera trap image sequence data and 9694 records of camera trap deployment metadata. Spatial Location and Grain: Data were collected across the United States of America in all 50 states, 12 ecoregions and many ecosystems. Time Period and Grain: Data were collected between 1st August and 29th December each year from 2019 to 2023. Major Taxa and Level of Measurement: The dataset includes a wide range of taxa but is primarily focused on medium to large mammals. Software Format: SNAPSHOT USA 2019–2023 comprises two .csv files. The original data can be found within the SNAPSHOT USA Initiative in the Wildlife Insights platform.

This naturalistic study evaluated the treatment effectiveness of a New England adolescent partial hospital program (PHP) for individuals referred from higher versus lower levels of care (LOC). Participants were adolescents ages 12 to 18 admitted to the in-person ( n = 161) and virtual ( n = 78) day programming. Chart review determined the referral source, and the Youth Outcomes Questionnaire (YOQ) assessed interpersonal relations (IR), intrapersonal distress (ID), suicidal ideation (SI), and self-injurious behavior (SIB) at the time of admission and discharge. Independent samples t-tests demonstrated the similarity of symptom severity at the time of admission for both higher and lower LOC, except for higher reports of SIB for higher LOC referrals to the virtual program. Repeated measures analyses of variance (ANOVAs) revealed improvement in all outcome measures and no significant difference in treatment effects or length of stay between higher versus lower LOC referrals. Study findings demonstrate the promise of PHPs to function effectively as both a step-up and step-down in the mental health continuum of care, reflect appropriate utilization of referrals to this LOC, and support the promise of telehealth as an alternative to in-person therapies.

In this communication we report the construction of a printed circuit board which mounts directly to the vacuum chamber of a mass spectrometer and produces the RF waveforms needed by many nonmass-selective devices such as ion guides and ion funnels. Our device is designed to replace a standard KF40 flange, can maintain vacuum chamber pressures of less than 10–6 Torr, and contains the circuitry of the open-source Wisconsin Oscillator RF power supply to generate RF waveforms of 1–4 MHz and up to 200 Vp-p. In this iteration of the Wisconsin Oscillator, we also introduce a variable resistor to control the output RF amplitude and show that its ion transmission capabilities are identical to those provided by commercial RF power supplies. With this new implementation we have greatly reduced the space and monetary requirements for driving nonmass-selective ion manipulation devices, which we expect to be advantageous to those developing low-cost and/or portable mass spectrometry systems.

Relying on his training in and experience with communication, specifically autoethnography and performance, the author contemplates limitations of Artificial Intelligence (AI) technologies. In so doing, he emphasizes the unique importance and practicality of autoethnographic and performance work. Keywords: Technology, personal narrative, qualitative research, methodology

This paper presents the Pulmonary Acoustic Sensor Telemetry Array (PASTA), a novel hardware prototype developed for remote pulmonary monitoring, diagnosis, and prognostication of pulmonary sounds in clinical settings. PASTA employs an array of acoustic sensors strategically positioned at standard auscultation sites on the patient’s chest and back, enabling real-time acquisition and analysis of pulmonary sounds with an edge computing device. This task is impossible using digital stethoscopes available in the market. The proposed system addresses critical challenges in pulmonary care by providing continuous, high-fidelity, multi-site acoustic data collection, a capability not offered by conventional stethoscopes. This functionality is pivotal for enhancing diagnostic accuracy and enabling early detection of respiratory abnormalities, potentially reducing treatment delays in acute respiratory conditions. PASTA integrates a compact design, multi-channel monitoring, and real-time telemetry, distinguishing itself from existing commercial medical devices. Performance evaluations against established digital stethoscopes, conducted using a high-fidelity medical simulation training manikin, demonstrate sensitivity and signal quality. The potential applications of PASTA extend beyond inpatient care, making it suitable for remote monitoring in outpatient programs, such as hospital-at-home and outpatient hospice care. By bridging a significant gap in current medical device offerings, PASTA sets a new standard in pulmonary auscultation and serves as a foundational platform for future advancements in Internet-of-Things (IoT)-enabled healthcare solutions.

Background/Objectives: Transient Receptor Potential Melastatin 8 (TRPM8) is a non-selective, Ca²⁺-permeable cation channel involved in thermoregulation and other physiological processes, such as basal tear secretion, cell differentiation, and insulin homeostasis. The activation and deactivation of TRPM8 occur through genetic modifications, channel interactions, and signaling cascades. Recent evidence suggests a significant role of TRPM8 in the hypothalamus and amygdala related to pain sensation and sexual behavior. Notably, TRPM8 has been implicated in neuropathic pain, migraines, and neurodegenerative diseases such as Parkinson’s disease. Our laboratory has identified testosterone as a high-affinity ligand of TRPM8. TRPM8 deficiency appears to influence behavioral traits in mice, like increased aggression and deficits in sexual satiety. Here, we aim to explore the pathways altered in brain tissues of TRPM8-deficient mice using the expression and methylation profiles of messenger RNA (mRNA) and long non-coding RNA (lncRNA). Specifically, we focused on brain regions integral to behavioral and hormonal control, including the olfactory bulb, hypothalamus, amygdala, and insula. Methods: RNA was isolated and purified for microarray analysis collected from male wild-type and TRPM8 knockout mice. Results: We identified various differentially expressed genes tied to multiple signaling pathways. Among them, the androgen–estrogen receptor (AR-ER) pathway, steroidogenesis pathway, sexual reward pathway, and cocaine reward pathway are particularly worth noting. Conclusions: These results should bridge the existing gaps in the knowledge regarding TRPM8 and inform potential targets for future studies to elucidate its role in the behavior changes and pathology of the diseases associated with TRPM8 activity.