31825 research outputs found
Sort by
Do tell: a rhetorical and literary analysis of post-9/11 refugee storytelling
No full textDavis, Emily S.Do Tell: A Rhetorical and Literary Analysis of Post-9/11 Refugee Storytelling introduces the concept of “post-9/11 refugee counternarratives,” a framework for understanding stories that resist dominant portrayals of refugees as either passive victims or dangerous threats. These counternarratives challenge reductive frameworks shaped by cultural suspicion, economic anxiety, and securitization, offering temporally layered representations that center agency, fractured identity, and disrupted belonging. ☐ Through a comparative rhetorical and literary analysis of multiple narrative forms, this dissertation explores how medium shapes meaning in refugee storytelling. The project begins with an auto-ethnographic study based on fieldwork with Syrian refugees in Jordan, curating new, firsthand “post-9/11 refugee counternarratives.” It then turns to Olivier Kugler’s graphic novel, Escaping Wars and Waves (2018), analyzing how visual fragmentation and text-image interplay evoke the nonlinearity of displacement. Mohsin Hamid’s novel Exit West (2017) is examined for its speculative use of magical realism to reframe forced migration, while Ben Sharrock’s film Limbo (2020) is explored for its use of stillness, silence, and suspended time to represent the lived realities of asylum-seeking. ☐ By centering form, including structure, medium, and aesthetic strategy, this dissertation argues that these “counternarratives” do not simply depict refugee experience but actively reconfigure how displacement is conceptualized and conveyed. In doing so, the project offers alternative ways of witnessing that move beyond extractive or sentimental modes of representation. It positions refugee storytelling not as a tool for eliciting empathy alone but as a form of community building, resistance, reclamation, and knowledge production. ☐ Ultimately, this study contributes to refugee and narrative theory by highlighting how “post-9/11 refugee counternarratives” open new ethical and interpretive pathways for engaging with stories of forced displacement across literary, visual, and personal terrains.University of Delaware, Department of EnglishPh.D
Deep learning algorithms for biomedical image segmentation in low-data scenarios
No full textBarner, Kenneth E.Automatic segmentation via deep learning plays a major role in biomedical imaging, enhancing diagnostics by dividing images into regions of interest. This procedure helps medical experts understand disease characteristics, lesion sizes, and other crucial details. Despite its potential, deep learning-based automatic segmentation often relies on large annotated data to accurately predict lesions and other critical regions. Among imaging modalities, ultrasound, widely used for its accessibility, real-time capabilities, and effectiveness in detecting lesions, remains inadequately investigated due to the inherent challenges in medical imaging, such as data availability and privacy concerns. This work identifies key research gaps in ultrasound imaging segmentation to address these challenges and contributes to advancements in this critical area. ☐ This dissertation focuses on three key areas for advancing ultrasound image segmentation and improving biomedical image analysis. First, it aims to improve supervised learning-based architectures for tumor segmentation, particularly U-Net models, which, despite their success in biomedical segmentation, often lack reliability for clinical use, especially when tested on out-of-dataset samples. Second, it addresses the challenges posed by limited annotated ultrasound data, which restricts the performance of supervised models. Finally, it addresses the scarcity of ultrasound datasets paired with corresponding masks, a significant issue caused by data privacy concerns, the lack of datasets from various countries, and the high costs of expert-level annotations. ☐ This dissertation introduces an improved supervised model based on a refined U-Net architecture incorporating ReSidual U-blocks (RSU) and Attention Gates to address segmentation challenges in scenarios with limited data. These enhancements improve the model’s ability to capture critical features and long-range dependencies, improving lesion segmentation performance in ultrasound images. Building on this, we integrate a Denoising Diffusion Probabilistic Model (DDPM) with the RSU architecture to create a deeper network capable of handling the high variability and noise in ultrasound datasets. This combination enhances segmentation mask accuracy and addresses challenges posed by samples with diverse characteristics, such as size and shape variations of regions of interest. ☐ Next, we improve data augmentation by enhancing the Mixup technique to address limited data scenarios in image segmentation. Using K-means clustering, ultrasound images are grouped into clusters of similar samples, and Mixup applies within clusters. This approach has the potential to reduce randomness, avoid mixing unrelated regions like tumors and dark backgrounds, and ensure more effective augmentation. It also diversifies the dataset by generating new samples and masks, mitigating data scarcity. Building on this contribution, we extend the application of Cluster Mixup to unsupervised segmentation. The goal is to leverage unlabeled ultrasound images by augmenting healthy samples with Cluster Mixup, followed by unsupervised learning to detect suspected tumors. This approach could show the potential to qualitatively and quantitatively improve the segmentation of regions of interest and enhance diagnostic capabilities. ☐ Additionally, we build upon Cluster Mixup by proposing a variant of D-DDPM, a diffusion-based model, to learn the distributions of combined images and masks, enabling the simultaneous and joint generation of synthetic images and annotations. This technique expands the dataset with a large number of image-mask pairs. We involve medical experts in evaluating the synthetic dataset, ensuring the selection of relevant samples, and improving dataset quality. Statistical analysis obtained from medical experts shows the reliability of our approach and its potential application to real-world problems. ☐University of Delaware, Department of Electrical and Computer EngineeringPh.D
Transcriptome meta-analysis of mouse lenses deficient for key genes to uncover regulatory pathways in lens development and pathology
No full textLachke, Salil A.The vertebrate lens is a powerful model to study tissue-specific gene regulation, development, and disease. Recent advances in high-throughput transcriptomics have yielded a growing number of high-throughput RNA-sequencing (RNA-seq) datasets from conditional knockout (cKO) mouse models carrying deletion/mutations of specific genes essential for lens biology and perturbations of which are linked to cataract in humans or animal models. However, these studies are often analyzed in isolation, limiting the discovery of shared regulatory programs and broader biological insights. A systematic meta-analysis of these RNA-seq datasets has not been performed, representing a key knowledge-gap. Therefore, I performed a comprehensive meta-analysis of publicly available lens KO RNA-seq datasets – which spanned embryonic, early postnatal, and adult stages – to identify core transcriptional programs and misregulated pathways relevant to lens biology. Raw FASTQ files were downloaded from the NCBI Gene Expression Omnibus (GEO) using SRR accession numbers and processed through a elaborate pipeline. Batch normalization was applied to account for variation due to laboratory origin and differential expression analysis was conducted using DESeq2 and edgeR, with meta-analysis performed separately for embryonic, early postnatal, and adult datasets. Gene Ontology (GO) enrichment was assessed using enrichGO (clusterProfiler) and Database for Annotation, Visualization and Integrated Discovery (DAVID), and lens-specific expression and enrichment were evaluated using the web-based eye bioinformatics tool, iSyTE (integrated Systems Tool for Eye gene discovery). This analysis led, for the first time, to the identification of a cohort of genes that are commonly mis-expressed in the lens, regardless of the specific gene perturbation. Thus, this analysis identified a “core” set of genes whose mis-regulation correlates with lens pathology. In embryonic datasets, differentially expressed genes (DEGs) were highly enriched for processes central to lens morphogenesis, such as lens fiber cell differentiation, and visual system development. Many downregulated genes, including Crygf, Crygb, Dnase2b, and Bfsp1 – identified in the present analysis – are known for their critical role in facilitating lens transparency, serving as an independent validation of the meta-analysis having worked well for the identification of key genes in the lens. Besides the established, novel genes whose function is not yet characterized in the lens were also identified (Tmprss11e, Ermap, Uox). In early postnatal datasets, a smaller set of DEGs was identified, including ectopically upregulated retinal genes (Prph2, Pdc, Pde6b). In adult lenses, a novel gene Ces5a was identified as significantly downregulated in the knockout samples. In wild-type (WT) lenses, Ces5a shows moderate expression during development and early postnatal stages, but its expression increases sharply—by over 17-fold—by postnatal day 56 (P56), where it exhibits a 26-fold enrichment compared to whole-body expression levels. This work provides the first stage-stratified meta-analysis of lens cKO RNA-seq data, uncovering shared and unique transcriptional consequences of gene disruption in lens development. Importantly, it identifies a “core” set of genes whose misexpression correlates with lens pathology, regardless of the genetic changes that caused it, thus potentially identifying new targets that may be commonly targeted in therapeutic approaches.University of Delaware, Center for Bioinformatics and Computational BiologyM.S
How Do Anxiety and Depression Trajectories Vary Among Black, Latinx, and Afro-Latinx Sexual Minority Young Men? Uncovering Variation in Development With Intersectional Subgroups
No full text© American Psychological Association, 2025. This paper is not the copy of record and may not exactly replicate the authoritative document published in the APA journal. The final article is available in Developmental Psychology, upon publication, at: https://doi.org/10.1037/dev0001968.The present study investigated whether the patterns of intersectional stigma experiences were associated with differences in the developmental, parallel trajectories of anxious and depressive symptoms across the transition to adulthood among Black, Latinx, and Afro-Latinx cisgender sexual minority young men. Data were from the Healthy Young Men’s Cohort Study collected semiannually from 2016 to 2020 in Los Angeles and included 426 cisgender Black, Latinx, and Afro-Latinx sexual minority young men between the ages of 18 and 25 at baseline. Multidomain latent growth modeling with a complex grouping variable was used to estimate the parallel trajectories of anxious and depressive symptoms and whether these trajectories varied based on the patterns of intersectional stigma at baseline. Models were adjusted for individually varying age of observations to approximate the growth processes from ages 18 to 29. Results demonstrated a general decline in anxious symptoms and depressive symptoms over time. Relative to all other patterns of stigma experiences, the subgroup characterized by a pattern of compounding racism and heterosexism exhibited the highest levels of anxious and depressive symptoms and an earlier peak in anxious symptoms. This compound stigma group also exhibited an earlier and the highest peak in anxious symptoms compared to all other groups. Results highlight the impact of intersecting stigma on mental health across early adult development, the need for mental health intervention early or before the transition to adulthood, and continued effort to challenge and combat racist and heterosexist biases.
Public Significance Statement
Subgroups of Black, Latinx, and Afro-Latinx sexual minority men who experience different patterns of intersectional stigma early in the transition to adulthood also experience higher levels of anxiety and depression. Young men experiencing compounded racism and heterosexism are likely to need earlier intervention to prevent the higher levels and earlier peak in mental health symptoms.This work was supported by the National Institute on Drug Abuse (grant number U01 DA036926); the National Institute on Mental Health (grant number T32 MH020031). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health
BODY DISSATISFACTION IN FEMALE COLLEGE ATHLETES: THE ROLE OF BODY MASS INDEX, SPORT TYPE, AND RACE
No full textenterDisordered eating disproportionately affects female athletes, compared to male
athletes and female non-athletes. A key predictor of disordered eating is body
dissatisfaction. A biopsychosocial approach was used to frame the current study
analyzing body dissatisfaction and three variables: (1) Body Mass Index, (2) sport
type, and (3) race in female college athletes. The study includes a sample of Division 1
University of Delaware female athletes (n=218) who participated in a 28-item survey
assessing demographic information and eating disorder risk in Spring and Summer of
2023. Body dissatisfaction was measured using items from the Eating Disorder
Examination Questionnaire – Short and Brief Eating Disorder in Athletes
Questionnaire. There was a difference in mean BMI found for athletes with high and
low body dissatisfaction, as revealed by an independent samples t-test. Those who
were “Always”, “Usually”, or “Often” satisfied with the shape of their body had a
mean BMI of 22.26 /2 ± 2.38, while those who were “Sometimes”, “Rarely”, or
“Never” satisfied with the shape of their had a mean BMI of 23.41 /2 ± 2.71
(p=.001). A chi-square test of independence showed no significant association
between sport type and body dissatisfaction, indicating that Non-Lean and Lean
athletes had no significant difference in body dissatisfaction levels. There was no
significant difference in body dissatisfaction between White and POC athletes
revealed by a chi-square test of independence. More research is needed to understand
the interplay of biological, social, and psychological correlates of body dissatisfaction,
such as body composition, sport type, and race.ente
Hardware-based multi-platform architecture for super-lattice light emitting diode infrared scene projectors
No full textKiamilev, Fouad E.Infrared scene projectors(IRSP) are critical laboratory tools used for the setup, calibration, and testing of Infrared(IR) imaging systems within a hardware-in-the-loop environment. These IRSPs are used to display dynamic user-defined video scenery that is then detected by an IR sensor in real-time simulations. IRSP technology until now has been dominated by resistor arrays that emit a heat signature to produce a desired image. A great deal of research has been put into a new IRSP system that can replace these arrays. In 2014, the CVORG research group, led by Dr. Kiamilev, and the University of Iowa research group, led by Dr. Thomas Boggess, produced the first IR Light Emitting Diodes(IRLED) scene projectors through SLEDs(Super-Lattice Light Emitting Diode’s) technology. Since then, the projectors have gone through different iterations, with each version improving greatly upon the prior array technology. ☐ SLEDs IRSP systems are designed to be modular and scalable to accommodate the demand for higher resolutions and faster frame rates for future-generation arrays. As existing IRSP technology advances, IRSP hardware capabilities need to expand in tandem. The current state-of-the-art projectors use commercial off-the-shelf Dewar products that cannot be easily modified and run at frame rates around 120 Hz, while also only being able to support up to 1K x 1K sized arrays. These limitations, in addition to physical limitations such as the fixed window size of the Dewar enclosure being limited to 1.6” and 100 built-in transmission line Input/Output(I/O) signals, put forth a central open question for IRSP technology: how do we improve performance such that kilohertz frame rates can be attained at larger resolutions? One of the major avenues would be to expand the hardware architecture to be able to accommodate this, giving room for the software structures to develop without the limitation of hardware resources. This research will focus on the design of a multi-platform approach to expand the hardware architecture, making the system more flexible and adaptable, by not only bypassing the limitations of the current cryogenic Dewar package but also providing a more direct connection to the array pads themselves. Using the multi-platform approach on the Close Support Electronics(CSE), the goal of the multiple CSE setup is to replace a single CSE with various multi-CSE configurations to increase the frame rate.University of Delaware, Department of Electrical and Computer EngineeringPh.D
High-quality polycrystalline vanadium dioxide thin films deposited via pulsed laser deposition with high uniformity and consistency
No full textThis article was originally published in Journal of Materials Science: Materials in Electronics. The version of record is available at: https://doi.org/10.1007/s10854-025-15921-6
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.Vanadium dioxide (VO2) is a phase transition material that experiences significant shifts in electrical, optical, and mechanical properties near its transition temperature. Among various methods for depositing VO2 thin films, pulsed laser deposition (PLD) provides precise stoichiometric control, good versatility, and high consistency. In this work, we introduce an optimized PLD-based method for depositing high-quality polycrystalline VO2 thin films. Experimental results demonstrate a resistance change of over four orders of magnitude during the phase transition, accompanied by high uniformity, with a thickness variation of less than 2% across a 100 mm wafer, and reliable reproducibility over time.This research was primarily supported by National Science Foundation through the University of Delaware Materials Research Science and Engineering Center DMR-2011824. Additional support was received from National Aeronautics and Space Administration Grant Number 80NSSC23M0076
NEURAL CORRELATES OF OBJECT-BASED WARPING
No full textenterObject-based warping is a visual illusion in which space within an object
appears expanded when points are located within the object and compressed when
points are located on or just beyond the boundaries of an object. Other studies have
used functional imaging to investigate the neural mechanisms of visual illusions, but
this has yet to be done for object-based warping. In the current study, we tested
feasibility of measuring positions of small stimuli (N = 5), a prerequisite for studying
object-based warping using functional imaging; then, we delineated visual regions in
subjects (N = 3) using probabilistic atlases and conducted multivoxel pattern analysis
(MVPA) to determine which of these visual regions may hold representations of
object-based warping based on classifier errors in position estimation. We trained
classifiers on activity when a dot stimulus is isolated, when the dot is on a perceived
figure region, and when the dot is on a perceived ground region. The classifier
estimated locations above chance for nearly all visual regions in all subjects and tests,
with greater accuracy for early than late visual regions. However, accuracy drops
dramatically on runs with figure-ground displays, and early visual regions did not
consistently exhibit systematic errors in estimates that would demonstrate expansion
and compression effects involved in object-based warping. Our results do not support
our hypothesis that early visual regions hold representations of position that are
subject to object-based warping. We have demonstrated that this approach to studying
object-based warping is ineffective, perhaps due to a limited amount of data and the
use of figure-ground stimuli that are too high contrast and complex. Future work is
needed to more precisely investigate the neural mechanisms of object-based warping.ente
Beyond adoption: The persistence of conservation and climate-smart agricultural practices in the United States
No full textThis article was originally published in Proceedings of the National Academy of Sciences (PNAS). The version of record is available at: https://doi.org/10.1073/pnas.2518373122
Copyright © 2025 the Author(s). Published by PNAS. This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY) https://creativecommons.org/licenses/by/4.0/Achieving sustainability goals requires that humans change their behavior not just once but persistently. Yet despite decades of research on the adoption of conservation and climate-smart agricultural practices, little is known about the extent to which these practices persist over time. One key reason is the lack of longitudinal, field-level data. Using ground-verified, longitudinal data on cover cropping across thousands of farm parcels in Indiana (USA), we find that persistence is low and contrasts sharply with the predictions made by Indiana conservation experts. We also find low persistence in a new national dataset of self-reported cover cropping by farm operators. The potential for low behavioral persistence in sustainable agricultural practices raises essential questions about the design of conservation programs and the modeling and valuation of ecosystem services.NSF Human-Environment and Geographical Sciences Program 2117722 and 2239859; USDA National Institute of Food and Agriculture 2023-67023-39033 and 2019-67023-29854,and Economic Research Service 58-6000-1-0072. This research was supported by the US
Department of Agriculture, Economic Research Service. The findings and conclusions in this publication are those of the authors and should not be construed to represent any official USDA or US Government determination or policy. We thank Kristin Rowles for assistance in creating Fig. 2