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Epidermolysis bullosa acquisita (EBA) represents a big challenge as a rare skin disorder, with no established markers for early detection for patients. Moreover, as a rare disease, it is extremely difficult to acquire good number of patient sample to diagnose accurately with high confidence. EBA has many biomarkers very similar to other bullosa diseases and needs specific clinical expertise to detect it using immunofluorescence microscopy. In this study, we introduce a deep learningbased method, EBAnet, that leveraged Convolutional Neural Network (CNN) based model for the detection of EBA based on Direct immunofluorescence (DIF) microscopy image. The proposed EfficientNet-based model achieved 97.3% sensitivity, 96.1% precision, and 96.7% accuracy in distinguishing EBA from other class and outperformed the existing model for the same purpose. GradCAM based class activation map also highlighted the important region of the DIF images that was focused by the proposed model leveraging the explainability of the model. We believe, EBAnet will add value in the early and accurate detection of EBA, addressing a critical need in clinical practice.

An analysis of how national narratives are inevitably forms of epistemic injustice, depriving individuals of epistemic and moral agency. Denying access to knowledge about the past is a tool of all autocratic regimes, commonly used for the purpose of retaining power and exerting dominance over individuals or groups subordinate to the ruling elite. Yet such narratives and the falsifications used to buttress them, are not the exclusive instruments of autocracies but can be found to pervade the national narratives of what we often nominally label as democracies. The denial of crimes against humanity and genocide are the most egregious examples of the harms perpetrated against victims and survivors. Miranda Fricker’s writings on epistemic injustice are employed in the analysis. Turkish and Azerbaijani genocide denial of the Armenian Genocide are used to illustrate how epistemic injustice lies at the heart of denialism. © 2024 Central European Pragmatist Forum. All rights reserved.

Obstructive Sleep Apnea (OSA) is a prevalent health issue affecting 10-25% of adults in the United States (US) and is associated with significant economic consequences. Machine learning methods have shown promise in improving the efficiency and accessibility of OSA diagnoses, thus reducing the need for expensive and challenging tests. A comparative analysis of Logistic Regression (LR), Support Vector Machine (SVM), Gradient Boosting (GB), Gaussian Naive Bayes (GNB), Random Forest (RF), and K-Nearest Neighbors (KNN) algorithms was conducted to predict Obstructive Sleep Apnea (OSA). To improve the predictive accuracy of these models, Random Oversampling was applied to address the imbalance in the dataset, ensuring a more equitable representation of the minority class. Patient demographics, including age, sex, height, weight, BMI, neck circumference, and gender, were employed as predictive features in the models. The RFC provided outstanding training and testing accuracies of 87% and 65%, respectively, and a Receiver Operating Characteristic (ROC) score of 87%. The GBC and SVM classifiers also demonstrated good performance on the test dataset. The results of this study show that machine learning techniques may be effectively used to diagnose OSA, with the Random Forest Classifier demonstrating the best results.

This chapter explores the impact of implementing a partnership agreement to grant incoming university students credit based on their documented language proficiency. In 2018, an agreement was forged between an urban university and public school district, and, by extension, any high school offering the Seal of Biliteracy (SoBL) to offer students who hold the SoBL university credit for their language proficiency in a world language. This chapter examines the evolution of that agreement over a five-year period and its potential impact on students’ decision to pursue a minor/major in Spanish. Through semi-structured interviews, we also examined multiple university stakeholders’ perceptions and/or experiences about the value of the SoBL, the agreement to grant credit by examination (CBE), as well as other challenges, including the university’s reduction of their language requirement, transitions in leadership, and differing ideas about if and how best to award students credit based on examination.

Objective Preterm birth (PTB) is one of the leading causes of infant and neonatal mortality. Prepregnancy body mass index (BMI; kg/m2) has been linked to PTB but the evidence of this association by weight gain during pregnancy, race, and ethnicity is limited. This study aimed to assess the association between maternal prepregnancy BMI and PTB stratified by weight gain during pregnancy, race, and ethnicity. Study Design The U.S. natality data from 2017 to 2021 were used. In this analysis, we included mothers who had a live singleton birth and available data for prepregnancy BMI, gestational age at birth, weight gain during pregnancy, race, and ethnicity. Logistic regression models were used to assess the association between prepregnancy BMI categories and PTB stratified by weight gain during pregnancy, race, and ethnicity. Results A total of 17,311,509 singleton live births were included of which 1,393,889 (8.05 %) were PTBs. After adjusting for confounders, compared with normal prepregnancy BMI mothers (18.5–24.9), those with underweight BMI (<18.5) were at increased odds of PTB regardless of weight gain during pregnancy, race, and ethnicity. However, for mothers with a prepregnancy BMI above the normal weight (≥25), the association between prepregnancy BMI and PTB differs by weight gain during pregnancy, race, and ethnicity. Asian mothers with obesity II (35.0–39.9) had 93% (odds ratio [OR] = 1.93, 95% confidence interval [CI]: 1.62–2.30) increased odds of PTB for weight gain during pregnancy of 31 to 40 pounds. Their White, Hispanic, and Black counterparts experienced lower odds of PTB for similar weight gain during pregnancy (White: OR = 1.56, 95% CI: 1.51–1.60; Hispanic: OR = 1.48, 95% CI: 1.41, 1.54; and Black: OR = 1.22, 95% CI: 1.17–1.27). Conclusion Mothers with underweight BMI were at increased risk of PTB regardless of weight gain during pregnancy, race, and ethnicity. However, the association between high prepregnancy BMI and PTB varied by weight gain during pregnancy, race, and ethnicity. Key Points

Background: In the United States, chronic obstructive pulmonary disease (COPD) is a significant cause of mortality. As far as we know, it is a chronic, inflammatory lung condition that cuts off airflow to the lungs. Many symptoms have been reported for such a disease: breathing problems, coughing, wheezing, and mucus production. Patients with COPD might be at risk, since they are more susceptible to heart disease and lung cancer. Methods: This study reviews COPD diagnosis utilizing various machine learning (ML) classifiers, such as Logistic Regression (LR), Gradient Boosting Classifier (GBC), Support Vector Machine (SVM), Gaussian Naïve Bayes (GNB), Random Forest Classifier (RFC), K-Nearest Neighbors Classifier (KNC), Decision Tree (DT), and Artificial Neural Network (ANN). These models were applied to a dataset comprising 1603 patients after being referred for a pulmonary function test. Results: The RFC has achieved superior accuracy, reaching up to 82.06% in training and 70.47% in testing. Furthermore, it achieved a maximum F score in training and testing with an ROC value of 0.0.82. Conclusions: The results obtained with the utilized ML models align with previous work in the field, with accuracies ranging from 67.81% to 82.06% in training and from 66.73% to 71.46% in testing.

Meta-heuristic optimization algorithms have become widely used due to their outstanding features, such as gradient-free mechanisms, high flexibility, and great potential for avoiding local optimal solutions. This research explored the grey wolf optimizer (GWO) to find the ideal configuration for a six-element Yagi–Uda antenna. The GWO algorithm adjusted the lengths of the antenna wires and the spacings between them. The goal was to maximize the antenna’s ability to transmit signals (throughput gain). Optimal antenna selection relies on various parameters, including gain, bandwidth, impedance matching, frequency, side-lobe levels, etc. The optimization of a six-element Yagi–Uda antenna presents a challenging engineering design problem due to its multimodal and nonlinear nature. Achieving optimal performance hinges on the intricate interplay between the lengths of the constituent elements and the spacing configurations. To this end, a multiobjective function was adopted to design this antenna. The performance of several meta-heuristic algorithms, including genetic algorithms, biogeography-based optimization, simulated annealing, and grey wolf optimizer, was compared. The GWO-based approach has performed better than its competitors. This optimized antenna design based on GWO reported a gain of 14.21 decibel. Therefore, the GWO-based method optimizes antennas that can be further investigated for other antenna design problems.

Structured faculty development programs focused on integrating health equity into medical education curricula remain limited.

Background: HIV risk behavior in women who use drugs is related to myriad psychosocial issues, including incarceration. The experience of incarceration elevates women’s HIV risk by disrupting social networks, housing, employment, and access to health care. During the COVID-19 pandemic, changes in criminal-legal practices resulted in decreased incarceration, especially among women. These changes may have largely altered HIV risk among women who use drugs, depending on their access to care in the community. Objective: This study seeks to build knowledge about the impact of shifts in criminal-legal practices during the COVID-19 pandemic on HIV risk behaviors of justice-involved women who use drugs. Methods: Qualitative methods are used to gather and analyze women’s narratives about their life experiences before and during the COVID-19 pandemic, with a focus on individual and structural determinants of HIV risk behaviors. Thirty formerly incarcerated women with a history of substance use are being recruited through collaboration with community partners. Each participant completes a sociodemographic survey and two interviews. The first interview uses a life history instrument that invites participants to reflect on key turning points in their lives. The second interview uses a calendar approach to gather information about participants’ lives during the first year of the COVID-19 pandemic (March 2020-March 2021). The interviews (1 hour each) are audio-recorded and transcribed for analysis. Rapid Qualitative Inquiry and thematic analysis are being used to manage, organize, and interpret the data. The study team will collaborate with a subset of participants to develop digital stories about their COVID-19 experiences, a process that allows for member-checking and triangulation. Findings will be disseminated to program and policy makers in academic venues, community settings, and social service agencies. Results: To date, 10 women’s data have been collected. In total, two themes have been identified in this preliminary data: (1)the chaos and instability of participants’ lives increased during the COVID-19 pandemic: participants reported a wide range of psychosocial and health problems and limited engagement with social service systems. Interaction with criminal-legal systems was rife with uncertainty; participants described living in a state of limbo, which was extremely stressful. (2) When asked to describe a “turning point” in their lives, many participants attributed their substance use to the traumatic loss of a child due to death, incarceration, or termination of parental rights. During the COVID-19 pandemic, participants’ struggles to cope with these unresolved experiences of grief and loss were intensified by the widespread death and dying of the pandemic. Conclusions: Preliminary findings suggest that HIV risk factors increased for participants during the COVID-19 pandemic and invite further investment in community-based harm reduction programs, especially housing, that support women who use drugs. Interventions that address experiences of maternal grief and loss may reduce women’s substance use. Trial Registration:

Despite the persistence of breastfeeding racial and ethnic disparities in the United States, little is known about Black fathers' perceptions of breastfeeding and breastfeeding support services (e.g., maternity hospital-based care and lactation management care). This qualitative, community-based participatory research study reports Black fathers' perceptions of barriers and facilitators to breastfeeding, including the provision of breastfeeding support services in Connecticut. A focus group guide was co-developed with community partners and adapted from the Barrier Analysis Tool to identify breastfeeding facilitators, barriers, and service improvement areas. Four focus groups were conducted with 30 Black fathers who were Connecticut residents with a child under 3 years old. Qualitative data were analyzed using rapid template analysis involving deductive and inductive coding. We identified factors influencing breastfeeding and fathers' ability to support breastfeeding across all levels of the Socio-Ecological Model. Facilitators included high paternal breastfeeding knowledge, paternal breastfeeding involvement, parents' shared decision-making, extensive maternity hospital discharge support, ongoing breastfeeding support into the postnatal period, availability of community breastfeeding resources, and designated spaces for public breastfeeding. Barriers included low paternal breastfeeding knowledge, familial discouragement, insufficient prenatal breastfeeding education, exclusion of the father from breastfeeding support services, and stigma against breastfeeding in public. Understanding breastfeeding perceptions among members of a mother's support network, including their partners, is key for developing effective person- and family-centered breastfeeding education and counseling services that are well coordinated from the prenatal to postnatal periods with strong direct engagement from fathers.

This research examines the impact of generative artificial intelligence (AI) on the perception of educational content quality, specifically by comparing AI-generated and human-generated course syllabi in marketing education. Results from four studies indicate a general preference for AI-generated syllabi, attributed to their greater perceived objectivity. This preference is more pronounced in conventional courses but diminishes in unconventional ones, suggesting that the unique aspects of these courses may reduce the advantages of generative AI. In addition, disclosing the AI authorship of syllabi significantly affects their perceived quality negatively, underscoring the impact of transparency on the acceptance of AI-generated educational materials. These findings highlight the potential of generative AI in educational content creation and its limitations in certain contexts. They offer valuable insights for enhancing educational practices and shaping policy decisions to enrich student experiences in the era of AI integration.

The purpose of this study is to learn more about virtual reality (VR) and augmented reality (AR) practices at the United States’ top one hundred university libraries, as well as how they are engaging with the metaverse. We conducted qualitative and descriptive analysis on the websites of the top one hundred university libraries in the United States to determine the application fields and application proportions of VR and AR technologies and found good practice examples of using VR and AR technologies in this field. The findings show that 86 percent of the top one hundred US university libraries have implemented VR and AR technologies, with practice areas focused on: VR/AR studio and VR/AR makerspace; immersive learning services and virtual exhibitions/conference services; visual geographic information system and VR navigation services; virtual reading services and visual retrieval services; and VR reference services. The study provides university library administrators and professionals with the most up-to-date information and best practices of VR and AR engagement areas and the proportion of use, which can aid in the development of strategies to leverage VR and AR technologies to improve patron service and embrace the metaverse for the communities they serve.

Insomnia is more frequently reported in stroke survivors but its independent role in mortality in stroke survivors is unknown. The purpose of this study was to investigate the association of insomnia symptoms with all-cause mortality among stroke survivors.

We report the systematic measurement of protons and light nuclei production in Au +Au collisions at √𝑠𝑁⁢𝑁=3GeV by the STAR experiment at the Relativistic Heavy Ion Collider (RHIC). The transverse momentum (𝑝𝑇) spectra of protons (𝑝), deuterons (𝑑), tritons (𝑡), 3He, and 4He have been measured from midrapidity to target rapidity for different collision centralities. We present the rapidity and centrality dependence of particle yields (𝑑⁢𝑁/𝑑⁢𝑦), average transverse momentum (⟨𝑝𝑇⟩), yield ratios (𝑑/𝑝, 𝑡/𝑝,3He/𝑝, 4He/𝑝), as well as the coalescence parameters (𝐵2, 𝐵3). The 4⁢𝜋 yields for various particles are determined by utilizing the measured rapidity distributions, 𝑑⁢𝑁/𝑑⁢𝑦. Furthermore, we present the energy, centrality, and rapidity dependence of the compound yield ratios (𝑁𝑝×𝑁𝑡/𝑁2𝑑) and compare them with various model calculations. The physics implications of these results on the production mechanism of light nuclei and the QCD phase structure are discussed.

Flow coefficients (𝑣2 and 𝑣3) are measured in high-multiplicity 𝑝+Au, 𝑑+Au, and 3He+Au collisions at a center-of-mass energy of √𝑠𝑁⁢𝑁=200 GeV using the STAR detector. The measurements utilize two-particle correlations with a pseudorapidity requirement of |𝜂|< 0.9 and a pair gap of |Δ⁢𝜂|>1.0. The primary focus is on analysis methods, particularly the subtraction of nonflow contributions. Four established nonflow subtraction methods are applied to determine 𝑣𝑛, validated using the HIJING event generator. 𝑣𝑛 values are compared across the three collision systems at similar multiplicities; this comparison cancels the final-state effects and isolates the impact of initial geometry. While 𝑣2 values show differences among these collision systems, 𝑣3 values are largely similar, consistent with expectations of subnucleon fluctuations in the initial geometry. The ordering of 𝑣𝑛 differs quantitatively from previous measurements using two-particle correlations with a larger rapidity gap, which, according to model calculations, can be partially attributed to the effects of longitudinal flow decorrelations. The prospects for future measurements to improve our understanding of flow decorrelation and subnucleonic fluctuations are also discussed.

This assignment is designed to enhance resilience among students in leadership courses. It leverages the US Army’s Master Resilience Training (MRT) framework and positive psychology to develop resiliency skills.A three-part experiential workshop integrates academic readings (providing a foundation of resilience concepts), explores the influence of personal identities on leadership and connects leadership skills with resilience concepts.Participants reflect on self-awareness tools and positive psychology and create personalized action plans. Participants' resilience skills are enhanced with their personalized resiliency plan.The program provides a structured approach to resilience training, which can be integrated into university curriculums. Students gain self-awareness and psychological tools to manage challenges, which are valuable for personal growth and professional development. There is a persistent gender gap in leadership, and for women to attain greater parity in leadership positions, resilience skills are imperative. By focusing on identity-related factors, the program prepares future leaders for challenges in attaining leadership positions.This program is uniquely tailored for students aspiring to leadership positions, with an emphasis on the role of identity, such as gender, in leader emergence and overcoming related challenges.

This chapter aimed at exploring how technology-mediated project-based language learning (PBLL) could be leveraged to foster 24 language learners’ awareness of social justice issues. Specifically, the study examined students’ perceptions regarding the potential benefits and challenges of technology-mediated PBLL for promoting social justice in a Spanish course. We used a grounded, iterative design and Teacher Action Research to analyze the data that comprised student-generated products (i.e., memes and written descriptions) and students’ perceptions of the benefits and challenges of using social justice projects in a Spanish course. In addition to revealing an array of perceived benefits and challenges of social justice projects for learning Spanish, the findings suggested the importance of using short, authentic multimodal texts and providing micro-scaffolding.

This article addresses advances in the definition, diagnosis, and clinical features of ASD particularly as they affect adolescents. While advances in early diagnosis and treatment of autism have led to substantial improvements in outcomes (e.g., significantly higher levels of personal self-sufficiency and independence), challenges for adolescents with autism spectrum disorders (ASD) remain. Persistent social-communication abilities and intellectual disabilities can hamper prospects of future employment and increase risk for psychopathology including depression and mood disorder and issues surrounding gender identity. Biological issues emerge, as well, for many adolescents with ASD such as increased risk for seizure disorder. In comparison to the literature on preschool and school age children, the research base is much more limited for adolescents, and studies are needed to identify effective, evidence-based, intervention strategies for this population. © 2024 Elsevier Inc. All rights are reserved.

With the STAR experiment at the BNL Relativistic Heavy Ion Collider, we characterize sNN=200GeV p+Au collisions by event activity (EA) measured within the pseudorapidity range ηϵ[-5,-3.4] in the Au-going direction and report correlations between this EA and hard- and soft-scale particle production at midrapidity (ηϵ[-1,1]). At the soft scale, charged particle production in low-EA p+Au collisions is comparable to that in p+p collisions and increases monotonically with increasing EA. At the hard scale, we report measurements of high transverse momentum (pT) jets in events of different EAs. In contrast with the soft particle production, high-pT particle production and EA are found to be inversely related. To investigate whether this is a signal of jet quenching in high-EA events, we also report ratios of pT imbalance and azimuthal separation of dijets in high- and low-EA events. Within our measurement precision, no significant differences are observed, disfavoring the presence of jet quenching in the highest 30% EA p+Au collisions at sNN=200GeV. © 2024 American Physical Society.