Syracuse University
Syracuse University Research Facility and Collaborative EnvironmentNot a member yet
20376 research outputs found
Sort by
Hooves, Paws, and Patriarchy: Reimagining a Multispecies Society in Late Chosŏn Korea
Get PDFIn late Chosŏn Korea, domesticated animals were more than passive fixtures of human society and culture, but rather active participants in the negotiation of social power dynamics. This paper proposes a species-inclusive reading of Chosŏn society, focusing on the eighteenth century, to expand our understanding from a patriarchal system to a kyriarchal framework. This thesis examines how horses, dogs, and sacrificial animals were enmeshed in intersecting hierarchies of gender and social class in urban and peripheral spaces. I argue that while interactions and conceptions of animals were shaped by the prevailing Confucian worldview, these relationships also worked reciprocally to reinforce, subvert, or complicate the ideological order. Existing scholarship on animals in Korean historiography has largely concentrated on animals within symbolic or economic framings, with few studies examining domesticated animals as active agents within the socio-ideological structures of the Chosŏn dynasty. By engaging with regional animal histories, the present research addresses a critical gap in the field. Through an interdisciplinary approach that combines historical analysis, feminist theory, and art history, I integrate visual and textual sources and situate them within broader political and intellectual contexts to examine how equine and canine existences were experienced, conceptualised, and represented. This research reveals how the species examined could be considered active participants in the construction of social, cultural, and religious interspecies dynamics. By investigating human-animal relationships through a Neo-Confucian lens, this study offers a better understanding of animal roles in early modern East Asian contexts. As such, it also contributes to the wider Animal History field through acknowledging the ways in which East Asian human-animal relations were shaped by logics distinct from those typically explored in Western histories
FLEXIBLE RATE: A MOTOR-LEARNING BASED TREATMENT FOR CHILDHOOD STUTTERING
Get PDFAbstract Purpose: This study examined the efficacy of a novel teletherapy speech restructuring treatment, Flexible Rate, grounded in motor learning principles and delivered within the Challenge Point Framework. The following variables were evaluated: stuttering frequency (percentage of stuttered words), perceived stuttering severity (rated by participants, parents, and the researcher), perceived speaking control, and communicative attitudes. Methods: A series of single-case studies utilizing an ABA design was used to examine the effects of Flexible Rate treatment in two children who stutter, ages 10 and 13. During treatment (B), participants completed two 30-minute virtual sessions per week for eight weeks. Participants were trained to elongate the initial sound of the first word in a sentence and in words beginning with individualized target sounds. Treatment outcomes were compared to baseline (A1) and follow-up (A2) phases. Measures included stuttering frequency (percentage of stuttered words), perceived stuttering severity (rated by the participant, parent, and researcher), perceived speaking control, and the OASES, CAT, and TOCS Parent Observational Rating Scale. Visual analysis and Cohen’s d were used to evaluate treatment effects. Results: Both participants demonstrated reductions in stuttering frequency and perceived stuttering severity, with large effect sizes observed from baseline to follow-up. Perceived speaking control increased post-treatment for both participants. Both participants showed gains on select OASES subscales, though only one demonstrated improvement on the CAT. Parental ratings on the TOCS indicated clinically significant post-treatment changes for both participants. Conclusion: Results suggest that Flexible Rate may reduce stuttering frequency and perceived stuttering severity, while improving perceived speaking control. However, limited changes in communicative attitudes highlight the need for further study
2024-2025 Annual Report: Guided by Faith, Modeling Change.
Get PDFThis annual report provides a concise overview of the Muslim Chaplaincy and its initiatives and programs for Muslim students during the 2024-2025 academic year. It showcases only a select few of the programs and accomplishments of the Muslim community at Syracuse University and beyond
Our Common Cause: Advocating for Human Labor for Outreach, Technical Expertise, and the future of Institutional Repositories
Get PDFSince 2021, SURFACE, Syracuse University’s institutional repository, has seen substantial growth in both content and commitment to long-term digital preservation. This progress is largely due to the Syracuse University Libraries’ investment in a dedicated three-person team focused on sustainability and development. As we face emerging technologies and AI, we question whether AI can replace human labor to reduce costs. Despite the financial appeal of cost-cutting, our presentation underscores the essential role of human labor. Through community-focused outreach and technical advancements like mass minting DOIs and restructuring our platform, we have demonstrated the irreplaceable value of human involvement. This presentation will explore the human labor behind the growth and maintenance of an Institutional Repository, arguing that AI cannot replace the nuanced and strategic efforts of human workers. We will discuss how our distributed labor approach has fostered a vibrant community and ensured long-term sustainability and preservation. Additionally, we will address the importance of outreach in transforming repositories into dynamic spaces of shared resources produced and maintained by collective effort. Through real-world examples, we will illustrate the limitations of AI and the critical need for human expertise in managing and preserving digital resources
Exploring Higher-Order Networks
Get PDFNetworks are natural representations of interactions in the real world (social networks, bio-networks, road networks, and the like) and are utilized across various disciplines. By default, network interactions are pairwise; in recent years, the demand for model ing higher-order interactions has kept increasing. For example, in collaboration networks, we aim to distinguish between one publication coauthored by three or three publications coauthored by two in a triangle. In this work, we perform higher-order network analysis in the following two directions. First, we explore the influence of higher-order structures on dyadic (pairwise) graphs; second, we model higher-order interactions by ordered hy pergraphs and analyze their cross-order properties. Followed by applications such as link prediction and representation learning, we verify the effectiveness of higher-order network modelings. In dyadic graphs, specific subgraph patterns with high frequencies are called network mo tifs. Inspired by the study of motifs, we first introduce a neighborhood motif-based link prediction method. Through extensive observation of real-world graphs, we found that the network structure of the neighborhood has a strong influence on getting two nodes connected. Specifically, a more well-connected and compact structure leads to a higher probability of wiring rather than a sparser structure. Based on this discovery, we propose a novel scoring method based on neighborhood motifs. By learning a γ-decaying model, we measure the pairwise similarities between nodes more accurately, even when only using the information of the common neighbors, which is often used in current techniques. We further model higher-order interactions directly using ordered hypergraphs. We ex plore and quantify similarities among various orders of the network. Our goal is to build relationships between different network orders and to solve higher-order problems using lower-order information. Similarities between different orders are not directly compara ble. Hence, we introduce a set of general cross-order similarities and a measure: subedge rate. Our experiments on multiple real-world datasets demonstrate that most higher-order networks have considerable consistency as we move from higher orders to lower orders. Using this discovery, we develop a new cross-order framework for a higher-order link pre diction method. These methods can predict higher-order links from lower-order edges, which cannot be attained by current higher-order methods that rely on data from a single order. Using the same topology, we explore the relationship between higher-order structure and spectral properties. These spectral properties are closely related to the structural properties of dyadic graphs. We generalize such connections and characterize higher-order networks by their spectral information. Specifically, we prove that the second moment provides an upper bound on the degree distributions while the third moment provides an upper bound on the number of on triangles. With the theoretical support, we use all such spec tral moments across different orders as the higher-order graph representation. Extensive experiments show the utility of this new representation in various settings. For instance, graph classification on higher-order graphs shows that this representation significantly out performs other techniques
Chemical Reversal of Beta-lactam Resistance and Induction of Hyper-susceptibility in Pseudomonas aeruginosa by Limiting Horizontal Gene Transfer under Beta-lactam Stress
Get PDFThe emergence of antibiotic resistance poses a significant threat to global health, with certain bacterial strains evolving to resist multiple drugs. Urgent action is necessary to develop new antibacterial agents that effectively target these resistant strains; however, no new classes of antibiotics have been approved in recent decades. This research employs innovative biological tools to identify and highlight factors influencing resistance dynamics. Additionally, this study refines the novel approach by utilizing adjuvants to chemically repurpose existing antibiotics and improve their effectiveness against antibiotic-resistant strains. Pseudomonas aeruginosa, recognized for its adaptability, persistent infections—especially in cystic fibrosis patients—and widespread antibiotic resistance, serves as our model organism. Horizontal gene transfer (HGT) is the exchange of genes between related and distinct species and represents the primary mechanism by which resistance spreads in bacteria. While the scientific community accepts that HGT contributes to resistance development, the magnitude and extent of this contribution remain open questions. In this work, we design novel serial passage assays on agar to highlight and quantify the effects of HGT during resistance development. We demonstrate that strains stressed in the presence of HGT with a single antibiotic (a beta-lactam) achieve high-level resistance (extremely high MICs), display increased fitness, and develop resistance to other antibiotics (multi-drug resistance (MDR)). Additionally, our work shows that strains stressed with one antibiotic without HGT can become resistant to that antibiotic, but exhibit increased susceptibility to other antibiotics compared to wild types, a phenomenon termed hyper-susceptibility. This study provides experimental evidence that horizontal gene transfer contributes to multi-drug resistance and the emergence of superbugs. Therefore, HGT should be considered and addressed when developing strategies against the rise of MDR strains. The Luk Lab\u27s recent work has introduced a new class of small molecule ligands that bind to two essential proteins—LecA and Pili—thus inhibiting tolerance, a precursor to resistance in Pseudomonas aeruginosa. We are investigating how these ligands can control horizontal gene transfer (HGT) and resistance. This research employs a small ligand molecule, SFβC, to inhibit and reverse resistance in wild-type PAO1. SFβC inhibited the development of Aztreonam resistance in PAO1 over 20 days by lowering the minimum inhibitory concentration (MIC). Furthermore, this study demonstrates that SFβC can reverse acquired resistance once it has developed, sometimes reducing the MIC to below initial susceptible levels in systems where HGT is blocked. In growth rate curves, this research indicates that SFβC enhances and allows sub-MIC concentrations of Aztreonam to kill Aztreonam-resistant strains. Bacteria have developed numerous strategies to evade treatment beyond resistance, including biofilm formation, tolerance, and persistence. Biofilms protect bacteria by limiting antibiotic penetration and as a hub for tolerant and persistent strains. Most conventional antibiotics promote biofilm formation and tolerance at sub-lethal concentrations. However, the Luk lab has introduced a new antibiotic, Farnesol Triazole Cellobioside (FTC), effectively kills bacteria without promoting tolerance and persistence. This research demonstrates that sub-MIC concentrations of FTC inhibit biofilm formation while conventional antibiotics promote it. Furthermore, this study reports progress in synthesizing a potential new antibiotic, 3,3-diMeTC, featuring 3,3-dimethyl branching based on structural modifications of FTC to achieve lower MICs against gram-negative bacteria. Collectively, this research presents promising strategies in the fight against antibiotic resistance by proposing methods to understand and inhibit resistance and designing new antibacterial agents to target resistant strains effectively